{"title":"Econometrics and economic statistics Books","description":"","products":[{"product_id":"statistical-9781472130259","title":"Statistical","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003e''Refreshingly clear and engaging'' Tim Harford\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e\u003cbr\u003e''Delightful . . . full of unique insights'' Prof Sir David Spiegelhalter\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eThere''s no getting away from statistics. We encounter them every day. We are all users of statistics whether we like it or not.\u003cbr\u003e\u003cbr\u003eDo missed appointments really cost the NHS 1bn per year?\u003cbr\u003e\u003cbr\u003eWhat''s the difference between the mean gender pay gap and the median gender pay gap?\u003cbr\u003e\u003cbr\u003eHow can we work out if a claim that we use 42 billion single-use plastic straws per year in the UK is accurate?\u003cbr\u003e\u003cbr\u003eWhat did the Vote Leave campaign''s 350m bus really mean?\u003cbr\u003e\u003cbr\u003eHow can we tell if the headline ''Public pensions cost you 4,000 a year'' is correct?\u003cbr\u003e\u003cbr\u003eDoes snow really cost the UK economy 1bn per day?\u003cbr\u003e\u003cbr\u003eBut how do we distinguish statistical fact from fiction? What can we do to decide whether a number, claim or news story is accurate? Without an understanding of data, we cannot truly understand what is going on in the wo\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eFascinating . . . timely . . . a lovely humorous undercurrent to it all -- Marcus Berkmann * Daily Mail *\u003cbr\u003eA refreshingly clear and engaging guide to the statistical claims all around us * Tim Harford, author of Fifty Things That Made The Modern Economy \u0026amp; Presenter of BBC More or Less *\u003cbr\u003eHaving spent his journalistic career working in a newsroom, being inundated with press releases full with dodgy statistics, Reuben has learned all the ways in which numbers can tell a misleading story. In this delightful book, full of unique insights from personal experience, he warns us of the phrases to look out for, and all the questions to ask about shabby surveys and dubious economic forecasts - there's also a great chapter on how to interpret big numbers. And he advises that we all ask the big question - is this number reasonably likely to be true? * Prof Sir David Spiegelhalter *\u003cbr\u003eStatistics can clarify or confuse. That's why you need to read this book * John Humphrys *\u003c\/p\u003e","brand":"Little, Brown Book Group","offers":[{"title":"Default Title","offer_id":47851686035799,"sku":"9781472130259","price":8.09,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781472130259.jpg?v=1710642103"},{"product_id":"statistical-ten-easy-ways-to-avoid-being-misled-by-numbers-9781472130266","title":"Statistical Ten Easy Ways to Avoid Being Misled","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAn accessible guide to interrogating the many statistics we are bombarded by every day.","brand":"Constable \u0026 Robinson","offers":[{"title":"Default Title","offer_id":48087972053335,"sku":"9781472130266","price":11.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781472130266.jpg?v=1713544044"},{"product_id":"maths-for-economics-9780198839507","title":"Maths for Economics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMaths for Economics provides a comprehensive and solid foundation in core mathematical principles and methods used in economics, beginning with revisiting basic skills in arithmetic, algebra, equation solving, and slowly building to more advanced topics. Suitable for those with a range of prior school-level expereince or more generally for those who feel they need to go back to the very basics, students can learn with confidence. Drawing on his extensive experience of teaching in the area, the author appreciates that maths can be a daunting topic for many. As such the text is fully supports the reader by using a combination of engaging learning features including summary sections, examples to show how theory is used in practice and progress exercises, which encourage independent study. Each chapter ends with a conclusion check list to allow students to reflect on topics as they master them.Digital formats and resourcesThe fifth edition is available for students and institutions to purchase in a variety of formats, and is supported by online resources. The e-book offers a mobile experience and convenient access along with functionality tools, navigation features, and links that offer extra learning support: www.oxfordtextbooks.co.uk\/ebooks Online resources supporting the book include,For Students: - Ask the author forum - Excel tutorial - Maple tutorial - Further exercises - Answers to further questions - Expanded solutions to progress exercises For Lecturers: - Test exercises - Graphs from the book - Answers to test exercises\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart One: Foundations 1: Arithmetic 2: Algebra 3: Linear equations 4: Quadratic equations 5: Some further equations and techniques Part Two: Optimization With One Independent Variable 6: Derivatives and differentiation 7: Derivatives in action 8: Economic applications of functions and derivatives 9: Elasticity Part Three: Mathematics Of Finance And Growth 10: Compound growth and present discounted value 11: The exponential function and logarithms 12: Continuous growth and the natural exponential function 13: Derivatives of exponential and logarithmic functions and their applications Part Four: Optimization With Two Or More Independent Variables 14: Functions of two or more independent variables 15: Maximum and minimum values, the total differential, and applications 16: Constrained maximum and minimum values 17: Returns to scale and homogenous functions; partial elasticities; growth accounting; logarithmic scales Part Five: Some Further Topics 18: Integration 19: Matrix algebra 20: Difference and differential equations 21: W21:Extensions and future directions","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732806447447,"sku":"9780198839507","price":55.09,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780198839507.jpg?v=1719998478"},{"product_id":"econometric-methods-with-applications-in-business-and-economics-9780199268016","title":"Econometric Methods with Applications in Business","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eProviding an understanding and experience of econometrics, this book covers basic econometric methods and addresses the creative process of model building. Using examples and exercises, it focuses on regression and covers choice data and time series data. It is aimed at undergraduate students, new graduate students, and applied researchers.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'. . . students will find the contents of this book to be a very helpful guide . . . Because of its wide coverage and careful presentation the book should be useful for a diverse group of students in many countries and interested in a variety of areas of applications.' * C. W. J. Granger, Nobel Laureate *\u003cbr\u003e'Most econometric texts can be described as either primarily theoretical or primarily applied. This is the first text I've seen that does a really nice job of bridging the gap between the two in a single unified whole. . . . I can strongly recommend this book to anyone desiring a firm understanding of both where econometric methods come from and how they are used in practice.' * James D. Hamilton, University of California, San Diego *\u003cbr\u003e'. . . superbly presented, the coverage is thorough, the technical rigour is sensibly balanced, and the empirical examples demonstrate the techniques effectively. The exercises are stimulating, the answers are insightful, and the exposition in the background material is excellent. It will appeal very strongly to researchers, instructors and students' * Michael McAleer, University of Western Australia *\u003cbr\u003e'. . . a thorough introduction to the basic principles of econometrics . . . The strong link between theory and applications provides great motivation for studying econometrics.' * Helmut Lütkepohl, European University Institute, Florence *\u003cbr\u003e'. . . meticulously crafted to give an almost seamless transition between learning and doing econometrics . . . There is something here for all students of econometrics.' * Michael P. Clements, Warwick University *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1 REVIEW OF STATISTICS; 2 SIMPLE REGRESSION; 3 MULTIPLE REGRESSION; 4 NON-LINEAR METHODS; 5 DIAGNOSTIC TESTS AND MODEL ADJUSTMENTS; 6 QUALITATIVE AND LIMITED DEPENDENT VARIABLES; 7 TIME SERIES AND DYNAMIC MODELS; APPENDIX A: MATRIX METHODS; APPENDIX B: DATA SETS; INDEX","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732839641431,"sku":"9780199268016","price":99.75,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780199268016.jpg?v=1719998619"},{"product_id":"introduction-to-econometrics-9780199676828","title":"Introduction to Econometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eIntroduction to Econometrics provides students with clear and simple mathematics notation and step-by-step explanations of mathematical proofs, to give them a thorough understanding of the subject. Extensive exercises throughout build confidence by encouraging students to apply econometric techniques. Retaining its student-friendly approach, Introduction to Econometrics has a comprehensive revision guide to all the essential statistical concepts needed to study econometrics, additional Monte Carlo simulations, new summaries, and non-technical introductions to more advanced topics at the end of chapters.This book is supported by online resources, which include:For lecturers: Instructor''s manual for the text and data sets, detailing the exercises and their solutions. Customizable PowerPoint slides.For students: Data sets referred to in the book. A comprehensive study guide offers students the opportunity to gain experience with econometrics through practice with exercises. Software manual. PowerPoint slides with explanations.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eReview from previous edition What sets this book apart is abundance of available online material... * Sunčica Vujić, University of Antwerp *\u003cbr\u003eThis is an excellent text for introductory econometrics courses and this edition is even better, especially with the increase in figures and charts. * Dr Bruce Morley, University of Bath *\u003cbr\u003eStudents of finance need to be comfortable with the econometric tools necessary to both grasp empirical work and undertake it. This text provides an excellent point of reference and constant companion in developing precisely that understanding. * Paul Stewart, University of Ulster *\u003cbr\u003eExcellent textbook, which I have adopted as required reading for my class. The explanations are very clear, and yet it is very concise and does not overwhelm students. * Thomas Chadefaux, Trinity College Dublin *\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eINTRODUCTION; REVIEW: RANDOM VARIABLES, SAMPLING, ESTIMATION AND INFERENCE","brand":"Oxford University Press","offers":[{"title":"Default Title","offer_id":48732881617239,"sku":"9780199676828","price":68.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780199676828.jpg?v=1719998798"},{"product_id":"the-art-of-statistics-9780241258767","title":"The Art of Statistics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003e''A statistical national treasure'' Jeremy Vine, BBC Radio 2\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003e''Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force\u003c\/b\u003e'' \u003ci\u003ePopular Science\u003c\/i\u003e\u003cbr\u003e\u003cbr\u003eDo busier hospitals have higher survival rates? How many trees are there on the planet? Why do old men have big ears? \u003cb\u003eDavid Spiegelhalter \u003c\/b\u003ereveals the answers to these and many other questions - questions that can only be addressed using statistical science.\u003cbr\u003e\u003cbr\u003eStatistics has played a leading role in our scientific understanding of the world for centuries, yet \u003cb\u003ewe are all familiar with the way statistical claims can be sensationalised\u003c\/b\u003e, particularly in the media. In the age of big data, as data science becomes established as a discipline, a basic grasp of \u003cb\u003estatistical literacy is more important than ever\u003c\/b\u003e. \u003cbr\u003e\u003cbr\u003eIn \u003ci\u003eThe Art of Statistics\u003c\/i\u003e, \u003cb\u003eDavid Spiegelhalter guides the reader through the essential principles we need in order to derive knowledge from data.\u003c\/b\u003e Drawing on real world problems to introduce conceptual issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether serial killer Harold Shipman could have been caught earlier, and if screening for ovarian cancer is beneficial. \u003cbr\u003e\u003cbr\u003e\u003cb\u003e''Shines a light on how we can use the ever-growing deluge of data to improve our understanding of the world'' \u003c\/b\u003e\u003ci\u003e\u003cb\u003eNature\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eDavid Spiegelhalter is probably the greatest living statistical communicator; more than that, he's one of the great communicators in any field. \u003cb\u003eThis marvellous book will transform your relationship with the numbers that swirl all around us. Read it and learn.\u003c\/b\u003e -- Tim Harford\u003cbr\u003e\u003cb\u003eThere is something in here for everyone\u003c\/b\u003e ... A call to arms for greater societal data literacy ... Spiegelhalter's work serves as a reminder that there are passionate, self-aware statisticians who can \u003cb\u003eargue eloquently that\u003c\/b\u003e \u003cb\u003etheir discipline is needed now more than ever\u003c\/b\u003e. * Financial Times *\u003cbr\u003e\u003cb\u003eShines a light on how we can use the ever-growing deluge of data to improve our understanding of the world\u003c\/b\u003e . . . \u003ci\u003eThe Art of Statistics\u003c\/i\u003e will serve students well. And it will be a boon for journalists eager to use statistics responsibly - along with anyone who wants to approach research and its reportage with healthy scepticism. * Nature *\u003cbr\u003eWhat David Spiegelhalter does here is provide a \u003cb\u003every thorough introductory grounding\u003c\/b\u003e in statistics without making use of mathematical formulae. And it's \u003cb\u003eremarkable\u003c\/b\u003e. Spiegelhalter is \u003cb\u003ewarm \u003c\/b\u003eand \u003cb\u003eencouraging\u003c\/b\u003e - it's a genuinely enjoyable read ... This book \u003cb\u003eshould be required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics\u003c\/b\u003e. A\u003cb\u003e tour de force\u003c\/b\u003e. * Popular Science *\u003cbr\u003e\u003ci\u003eThe Art of Statistics\u003c\/i\u003e is in the \u003cb\u003egreat educational tradition\u003c\/b\u003e of its publishing imprint, Pelican Books: an attempt to get everyone up to speed with the practical uses of statistics, without pages of terrifying equations or Greek letters. In a series of \u003cb\u003espry, airy chapters, he succeeds fabulously\u003c\/b\u003e ...  Lucid and readable. In an age of scientific clickbait, 'big data' and personalised medicine,\u003cb\u003e this is a book that nearly everyone would benefit from reading\u003c\/b\u003e. * Spectator *\u003cbr\u003eImportant and comprehensive -- Hannah Fry * New Yorker *\u003cbr\u003eThis is an \u003cb\u003eexcellent \u003c\/b\u003ebook. Spiegelhalter is \u003cb\u003egreat at explaining\u003c\/b\u003e difficult ideas . . . Yes, statistics can be difficult. But much less difficult if you read this book. * Evening Standard *\u003cbr\u003eLike the fictional investigator Sherlock Holmes, Spiegelhalter\u003cb\u003e takes readers on a trail to challenge methodology and stats thrown at us by the media and others\u003c\/b\u003e. But where other authors have attempted this and failed, he is \u003cb\u003einventive and clever\u003c\/b\u003e in picking the right examples that spark the reader's interest to become active on their own. * Engineering and Technology *\u003cbr\u003eDo you trust headlines telling you . . . that bacon, ham and sausages carry the same cancer risk as cigarettes? No, nor do I. That is why \u003cb\u003ewe need a book like this\u003c\/b\u003e that explains how such implausible nonsense arises in the first place. Written by a\u003cb\u003e master of the subject \u003c\/b\u003e. . . this book \u003cb\u003etells us to examine our assumptions. Bravo.\u003c\/b\u003e * Standpoint *","brand":"Penguin Books Ltd","offers":[{"title":"Default Title","offer_id":48732979855703,"sku":"9780241258767","price":10.44,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780241258767.jpg?v=1719999145"},{"product_id":"natural-resources-as-capital-mit-press-the-mit-press-9780262534055","title":"Natural Resources as Capital MIT Press The MIT","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eAn introduction to the concepts and tools of natural resource economics, including dynamic models, market failures, and institutional remedies.\u003c\/b\u003e\u003cp\u003eThis introduction to natural resource economics treats resources as a type of capital; their management is an investment problem requiring forward-looking behavior within a dynamic setting. Market failures are widespread, often associated with incomplete or nonexistent property rights, complicated by policy failures. The book covers standard resource economics topics, including both the Hotelling model for nonrenewable resources and models for renewable resources. The book also includes some topics in environmental economics that overlap with natural resource economics, including climate change.\u003c\/p\u003e\u003cp\u003eThe text emphasizes skills and intuition needed to think about dynamic models and institutional remedies in the presence of both market and policy failures. It presents the nuts and bolts of resource economics as applied to nonrenewable r\u003c\/p\u003e","brand":"MIT Press Ltd","offers":[{"title":"Default Title","offer_id":48733466820951,"sku":"9780262534055","price":50.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780262534055.jpg?v=1720000186"},{"product_id":"quantitative-risk-and-portfolio-management-9781009209045","title":"Quantitative Risk and Portfolio Management","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA modern introduction to risk and portfolio management for advanced undergraduate and beginning graduate students who will become practitioners in the field of quantitative finance, including extensive live data and Python code as online supplements which allow the application of theory to real-world situations.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This is the book I wish I had had when I started my career in quantitative finance twenty years ago. It is written with the rigor of an academic, the insight of an experienced practitioner, and the didactic style of an empathetic and engaging teacher. Winston connects with his readers through insightful and entertaining discussions of historical background and of how actual financial markets behave or misbehave. At the same time, he provides rigorous but crystal clear and unhurried explanations of technical concepts. His choice of topics reflects current practice. A practitioner will find much to learn and enjoy in this book. A student who masters this material will be well prepared for a career in quantitative finance.' Colm O'Cinneide, Franklin Templeton Investments\u003cbr\u003e'Ken Winston has created a concise, valuable reference for the quantitatively minded that, in addition to describing our standard approaches for asset pricing and risk management, shows how these tools can and must be extended to reflect the more complicated risks we actually face.' David Germany, Pitzer College\u003cbr\u003e'This book is a remarkable combination of finance theory, mathematics, and practice. The development of finance theory is deep enough to challenge the most advanced students, yet it is full of applications. The author's long history of developing risk models is evident in every chapter. The book belongs in the curricula of the best graduate programs in finance and economics.' Charles Trzcinka, Indiana University\u003cbr\u003e'Few people are as qualified as Ken Winston to provide an academically disciplined practitioner view of how to manage and profit from investment risk-taking. Trained as a mathematician, Ken was the chief risk officer for some of the world's largest investment managers. Successful risk managers must have excellent quantitative and people skills, and Ken has both. The value of quantitative skill is evident in a game of numbers. People skills are necessary to communicate and successfully enforce limits on managers who too often dream of unachievable profits. Ken drew on both sets of skills to produce this innovative book, already well tested in his classrooms at Cal Tech and NYU. It is an essential read for all aspiring investment managers.' Larry Harris, University of Southern California\u003cbr\u003e'This is the book that I wish I had been able to have when I switched from applied math\/ engineering to applied finance more than thirty years ago. In essence, the book fills a very important void: how to approach financial engineering problems from the practitioner's viewpoint. A must-have for risk managers and investment professionals.' Arturo Cifuentes, Chile Sovereign Fund\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface; 1. What is risk?; 2. Risk metrics; 3. Fixed income modeling; 4. Equity modeling; 5. Convex optimization; 6. Factor models; 7. Distributions; 8. Simulation, scenarios and stress testing; 9. Time-varying volatility; 10. Modeling relationships; 11. Credit modeling; 12. Hedging; References; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738018820439,"sku":"9781009209045","price":51.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781009209045.jpg?v=1723811682"},{"product_id":"advanced-issues-in-partial-least-squares-structural-equation-modeling-9781071862506","title":"Advanced Issues in Partial Least Squares","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe \u003cstrong\u003eSecond Edition\u003c\/strong\u003e of \u003cem\u003e\u003cstrong\u003eAdvanced Issues in Partial Least Squares Structural Equation Modeling\u003c\/strong\u003e\u003c\/em\u003e offers a straightforward and practical guide to PLS-SEM for users ready to go further than the basics of \u003cem\u003e\u003cstrong\u003eA Primer on Partial Least Squares Structural Equation Modeling\u003c\/strong\u003e\u003c\/em\u003e\u003cstrong\u003e\u003cem\u003e,Third Edition\u003c\/em\u003e\u003c\/strong\u003e. Even in this advanced guide, the authors have limited the emphasis on equations, formulas, and Greek symbols, and instead rely on detailed explanations of the fundamentals of PLS-SEM and provide general guidelines for understanding and evaluating the results of applying the method. A single study on corporate reputation features as an example throughout the book, along with a single software package (SmartPLS 4.0) to provide a seamless learning experience. The approach of this book is based on the authors' many years of conducting research and teaching methodology courses, including developing the SmartPLS software. The pr\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Excellent guide on how to use smart pls. Good starter product for understanding the underlying concepts.\" -- Saurabh Gupta\u003cbr\u003e\"Must have if you want to do PLS\" -- Jason Xiong\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eChapter 1: An Overview of Recent and Emerging Developments in PLS-SEM Chapter 2: Higher-order Constructs Chapter 3: Advanced Modeling and Model Assessment Chapter 4: Advanced Results Illustration Chapter 5: Modeling Observed Heterogeneity Chapter 6: Modeling Unobserved Heterogeneity","brand":"SAGE Publications Inc","offers":[{"title":"Default Title","offer_id":48738220376407,"sku":"9781071862506","price":55.1,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781071862506.jpg?v=1723811830"},{"product_id":"fat-chance-9781108728188","title":"Fat Chance","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eDesigned for the intellectually curious, this book provides a solid foundation in basic probability theory in a charming style, without technical jargon. This text will immerse the reader in a mathematical view of the world, and teach them techniques to solve real-world problems both inside and outside the casino.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Probability is a subject of fundamental importance that's often taught as a dry slog through ball-filled urns. Fat Chance, a snappy and example-rich text, is the perfect antidote and a great choice for a general-audience math course.' Jordan Ellenberg, University of Wisconsin-Madison\u003cbr\u003e'Mathematics is a minority sport. Only very few understand, say, algebraic geometry or ergodic theory, and that's perfectly OK. Yet, probability theory is different. We are surrounded by chance and uncertainty. You cannot be a fully-fledged, functioning twenty-first-century human, whether at work, as a consumer or as an active citizen, without understanding the basic rules that govern chance. This is a book explaining chance and probability to non-mathematicians: accessible, not expecting any prior knowledge - but, there being no free lunch, assuming basic intelligence and an open mind. A small price for great enlightenment.' Arieh Iserles, University of Cambridge\u003cbr\u003e'Fat Chance is a fun and friendly introduction to the big ideas of risk, probability, and uncertainty in our everyday lives and in the world around us. It's written by three of the greatest mathematicians - and teachers - anywhere on the planet. I loved it! And according to my calculations, there's a high probability you'll love it too.' Steven Strogatz, Cornell University, and author of Infinite Powers\u003cbr\u003e'… Fat Chance is an enjoyable small introductory book that, without the pretension of serving as a reference textbook, will certainly help undergraduate students to approach the fascinating world of probability but will also be appreciated by whoever desires to learn the basics through self-education.' Massimo Nespolo, Journal of Applied Crystallography\u003cbr\u003e'The book is an excellent text for anyone looking for a very enjoyable introduction to probability. It can be read on its own or used as a textbook for a math course for non-math majors.' Thomas Hagedorn, MAA Reviews\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I. Counting: 1. Simple counting; 2. The multiplication principle; 3. The subtraction principle; 4. Collections; 5. Games of chance; Interlude; 6. The binomial theorem; 7. Advanced counting; Part II. Probability: 8. Expected value; 9. Conditional probability; 10. Unfair coins and loaded dice; 11. Geometric probability; Part III. Probability in the Large: 12. Games and their payoffs; 13. The normal distribution; 14. Don't try this at home.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738326708567,"sku":"9781108728188","price":27.48,"currency_code":"GBP","in_stock":true}]},{"product_id":"the-econometricians-gauss-galton-pearson-fisher-hotelling-cowles-frisch-and-haavelmo-great-minds-in-finance-9781137341365","title":"The Econometricians Gauss Galton Pearson Fisher","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis is the seventh book in a series of discussions about the great minds in the history and theory of finance.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1) Preface to the Great Minds in Finance Series.- 2) Preamble.- Section One: Mathematicians and Astronomers.- 3) The Early Life of Carl Friedrich Gauss.- The Times of Carl Friedrich Gauss.- Carl Friedrich Gauss’ Great Idea.- 6)The Later Years and Legacy of Carl Friedrich Gauss.- Section Two: From Least Squares to Eugenics.- 7) The Early Life of Francis Galton.- 8) The Times of Francis Galton.- 9) The Later Life and Legacy of Sir Francis Galton.- 10) The Early Life of Karl Pearson.- 11) Karl Pearson’s Great Idea.- 12) The Later Life and Legacy of Karl Pearson.- Section Three: The Formation of Modern Statistics.- 13) The Early Life of Ronald Aylmer Fisher.- 14) The Times of Ronald Aylmer Fisher.- 15) Ronald Fisher’s Great Idea.- 16) Later Life and Legacy of Ronald Fisher.- 17) The Early Life of Harold Hotelling.- 18) The Times of Harold Hotelling.- 19) Harold Hotelling’s Great Idea.- 20) The Later Life and Legacy of Harold Hotelling.- Section Four: The Birth of a Commission and Econometrics.- 21) The Early Life of Alfred Cowles III.- 22) The Times of Alfred Cowles III.- 23)The Great Idea of Alfred Cowles III.- 24) Legacy and Later Life of Alfred Cowles III.- 25) The Early Life of Ragnar Frisch.- 26) The Times of Ragnar Frisch.- 27) Ragnar Frisch’s Great Idea.- 28) The Legacy and Later Life of Ragnar Frisch.- 29) The Early Years of Trygve Haavelmo.- 30) The Times of Trygve Haavelmo.- 31) Haavelmo’s Great Idea.- Section Five: What We Have Learned.- 33) Conclusions.- 34) Glossary.","brand":"Palgrave Macmillan","offers":[{"title":"Default Title","offer_id":48738379858263,"sku":"9781137341365","price":96.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781137341365.jpg?v=1723811999"},{"product_id":"in-fact-9780717190386","title":"In Fact","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIf you follow the headlines, you could be forgiven for thinking that things in Ireland are worse than ever. In fact, we live longer than ever before, we have never been healthier or better educated, we earn five times more than our grandparents did, our personal freedoms exceed those of any previous generation, and the lives of women and children have been transformed for the better.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eAt a time when some good news is welcome, this uplifting book tells our national story through facts and stats, placing Ireland under the microscope to chart 100 undeniable achievements of the past 100 years.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eWhen the State was formed, Ireland was one of the most poverty-stricken nations in Europe. Now it has the second-highest quality of life in the world. While there is still more to be done, \u003cem\u003eIn Fact\u003c\/em\u003e illustrates that Ireland, for all its imperfections, is in a much better state than you might think.\u003c\/p\u003e","brand":"Gill","offers":[{"title":"Default Title","offer_id":48738419999063,"sku":"9780717190386","price":20.69,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780717190386.jpg?v=1720048145"},{"product_id":"control-systems-and-reinforcement-learning-9781316511961","title":"Control Systems and Reinforcement Learning","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of ''deep'' or ''Q'', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'Control Systems and Reinforcement Learning is a densely packed book with a vivid, conversational style. It speaks both to computer scientists interested in learning about the tools and techniques of control engineers and to control engineers who want to learn about the unique challenges posed by reinforcement learning and how to address these challenges. The author, a world-class researcher in control and probability theory, is not afraid of strong and perhaps controversial opinions, making the book entertaining and attractive for open-minded readers. Everyone interested in the \"why\" and \"how\" of RL will use this gem of a book for many years to come.' Csaba Szepesvári, Canada CIFAR AI Chair, University of Alberta, and Head of the Foundations Team at DeepMind\u003cbr\u003e'This book is a wild ride, from the elements of control through to bleeding-edge topics in reinforcement learning. Aimed at graduate students and very good undergraduates who are willing to invest some effort, the book is a lively read and an important contribution.' Shane G. Henderson, Charles W. Lake, Jr. Chair in Productivity, Cornell University\u003cbr\u003e'Reinforcement learning, now the de facto workhorse powering most AI-based algorithms, has deep connections with optimal control and dynamic programing. Meyn explores these connections in a marvelous manner and uses them to develop fast, reliable iterative algorithms for solving RL problems. This excellent, timely book from a leading expert on stochastic optimal control and approximation theory is a must-read for all practitioners in this active research area.' Panagiotis Tsiotras, David and Andrew Lewis Chair and Professor, Guggenheim School of Aerospace Engineering, Georgia Institute of Technology\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction; Part I. Fundamentals Without Noise: 2. Control crash course; 3. Optimal control; 4. ODE methods for algorithm design; 5. Value function approximations; Part II. Reinforcement Learning and Stochastic Control: 6. Markov chains; 7. Stochastic control; 8. Stochastic approximation; 9. Temporal difference methods; 10. Setting the stage, return of the actors; A. Mathematical background; B. Markov decision processes; C. Partial observations and belief states; References; Glossary of Symbols and Acronyms; Index.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48738559852887,"sku":"9781316511961","price":47.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781316511961.jpg?v=1720049468"},{"product_id":"applied-econometrics-9781352012026","title":"Applied Econometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis trusted textbook returns in its 4th edition with even more exercises to help consolidate understanding - and a companion website featuring additional materials, including a solutions manual for instructors. Offering a unique blend of theory and practical application, it provides ideal preparation for doing applied econometric work as it takes students from a basic level up to an advanced understanding in an intuitive, step-by-step fashion. Clear presentation of economic tests and methods of estimation is paired with practical guidance on using several types of software packages. Using real world data throughout, the authors place emphasis upon the interpretation of results, and the conclusions to be drawn from them in econometric work. This book will be essential reading for economics undergraduate and master's students taking a course in applied econometrics. Its practical nature makes it ideal for modules requiring a research project. New to this Edition:- Additional practical e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePART I: STATISTICAL BACKGROUND AND BASIC DATA HANDLING 1. Fundamental Concepts 2. The Structure Of Economic Data and Basic Data Handling PART II: THE CLASSICAL LINEAR REGRESSION MODEL 3. Simple Regression 4. Multiple Regression PART III: VIOLATING THE ASSUMPTIONS OF THE CLRM 5. Multicollinearity 6. Heteroskedasticity 7. Autocorrelation 8. Misspecification: Wrong Regressors, Measurement Errors And Wrong Functional Forms PART IV: TOPICS IN ECONOMETRICS 9. Dummy Variables 10. Dynamic Econometric Models 11. Simultaneous Equation Models 12. Limited Dependent Variable Regression Models PART V: TIME SERIES ECONOMETRICS 13. ARIMA Models And The Box–Jenkins Methodology 14. Modelling The Variance: ARCH–GARCH Models 15. Vector Autoregressive(VAR) Models And Causality Tests 16. Non-Stationarity and Unit Root Tests 17. Cointegration and Error-Correction Models 18. Identification In Standard and Cointegrated Systems 19. Solving Models 20. Time Varying Coefficient Models: A New Way of Estimating Bias Free Parameters PART VI: PANEL DATA ECONOMETRICS 21. Traditional Panel Data Models 22. Dynamic Heterogeneous Panels 23. Non-Stationary Panels PART VII: USING ECONOMETRIC SOFTWARE 24. Practicalities in Using Eviews and Stata.","brand":"Bloomsbury Publishing PLC","offers":[{"title":"Default Title","offer_id":48738628305239,"sku":"9781352012026","price":60.79,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781352012026.jpg?v=1720049706"},{"product_id":"handbook-of-research-on-emerging-theories-models-and-applications-of-financial-econometrics-9783030541071","title":"Handbook of Research on Emerging Theories,","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis handbook presents emerging research exploring the theoretical and practical aspects of econometric techniques for the financial sector and their applications in economics. By doing so, it offers invaluable tools for predicting and weighing the risks of multiple investments by incorporating data analysis. Throughout the book the authors address a broad range of topics such as predictive analysis, monetary policy, economic growth, systemic risk and investment behavior.\u003c\/p\u003e  This book is a must-read for researchers, scholars and practitioners in the field of economics who are interested in a better understanding of current research on the application of econometric methods to financial sector data.\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction.- Exploratory Classification of Time-Series.- Predicting the tail behaviour of financial time series exchange\/Johannesburg stock exchange closing banking indices - Extreme value theory approach.- Financial Econometrics and Systemic Risk.- Monetary Policy Shocks, Financial Heterogeneity and Corporate Dynamic Investment Activity: Financial Heterogeneity and Corporate Dynamic Investment Activity.- Oil Price Scenarios: Its Economic and Fiscal Impacts on the Kuwait Economy.- Exchange Rate Sensitivity of Firm Value: Evidence from Non-Financial Firms Listed on Borsa Istanbul.- Limited Dependent Variables (Logit and Probit Models) and An Application on BIST-100: Logit and Probit Models.- Vector Autoregressive Model: Model and Analysis.-Construction of the Monetary Conditions Index with TVP-VAR Model: Empirical Evidences for Turkish Economy.- Monetary Policy Regimes, Fiscal Implications, and Policy Interactions among Developing Economies.- The impacts of transportation sector and unemployment on economic growth: Evidence from asymmetric causality.- ARCH Models and An Application on Exchange Rate Volatility: ARCH\u0026amp;GARCH MODELS.- Using CoGARCH Filtered Volatility in Modelling within ARDL Framework.- Performance of MS-GARCH Models: Bayesian MCMC based estimation.- Volatility Spillovers Between Oil Prices and BIST (Borsa Istanbul) Dividend Indexes: Oil Prices and Dividend Indexes.- Volatility Spillovers Between Oil Prices and BIST (Borsa Istanbul) Dividend Indexes: Oil Prices and Dividend Indexes.- Panel Data Analysis.- An Amalgamation of big data analytics with tweet feeds for Stock Market Trend Anticipating Systems- A Review: Big data analytics with tweet feeds for Stock Market Trend Anticipating Systems.- Capital Structure Adjustment Speed: Evidence from Borsa Istanbul Sub-Sectors.\u003cp\u003e \u003c\/p\u003e","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743039861079,"sku":"9783030541071","price":142.49,"currency_code":"GBP","in_stock":true}]},{"product_id":"econometrics-9783030801489","title":"Econometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis textbook teaches some of the basic econometric methods and the underlying assumptions behind them. It also includes a simple and concise treatment of more advanced topics in spatial correlation, panel data, limited dependent variables, regression diagnostics, specification testing and time series analysis. Each chapter has a set of theoretical exercises as well as empirical illustrations using real economic applications. These empirical exercises usually replicate a published article using Stata, Eviews as well as SAS.\u003c\/p\u003e\u003cp\u003eThis new sixth edition has been fully revised and updated, and includes new material on limited dependent variables and panel data as well as revision of basic topics like heteroskedasticity, endogeneity, over-identification and specification testing. The author also provides more exercises and empirical examples based on published economic applications.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart 1: What Is Econometrics?.- Basic Statistical Concepts.- Simple Linear Regression.- Multiple Regression Analysis.- Violations of the Classical Assumptions.- Distributed Lags and Dynamic Models.- Part 2: The General Linear Model: The Basics.- Regression Diagnostics and Specification Tests.- Generalized Least Squares.- Seemingly Unrelated Regressions.- Simultaneous Equations Model.- Pooling Time-Series of Cross-Section Data.- Limited Dependent Variables.- Time-Series Analysis.","brand":"Springer Nature Switzerland AG","offers":[{"title":"Default Title","offer_id":48743050936663,"sku":"9783030801489","price":52.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783030801489.jpg?v=1720063892"},{"product_id":"introduction-to-mathematics-for-economics-with-r-9783031052019","title":"Introduction to Mathematics for Economics with R","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book provides a practical introduction to mathematics for economics using R software. Using R as a basis, this book guides the reader through foundational topics in linear algebra, calculus, and optimization. The book is organized in order of increasing difficulty, beginning with a rudimentary introduction to R and progressing through exercises that require the reader to code their own functions in R. All chapters include applications for topics in economics and econometrics. As fully reproducible book, this volume gives readers the opportunity to learn by doing and develop research skills as they go. As such, it is appropriate for students in economics and econometrics.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction to R.- 2. Linear Algebra.- 3. Functions of one variable.- 4. Dierential Calculus.- 5. Integral Calculus.- 6. Multivariable Calculus.- 7. Constrained Optimization.- 8. Trigonometry.- 9. Complex numbers.- 10. Difference equations.- 11. Differential equations.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743066337623,"sku":"9783031052019","price":42.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031052019.jpg?v=1720063961"},{"product_id":"introduction-to-mathematics-for-economics-with-r-9783031052040","title":"Introduction to Mathematics for Economics with R","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book provides a practical introduction to mathematics for economics using R software. Using R as a basis, this book guides the reader through foundational topics in linear algebra, calculus, and optimization. The book is organized in order of increasing difficulty, beginning with a rudimentary introduction to R and progressing through exercises that require the reader to code their own functions in R. All chapters include applications for topics in economics and econometrics. As fully reproducible book, this volume gives readers the opportunity to learn by doing and develop research skills as they go. As such, it is appropriate for students in economics and econometrics.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction to R.- 2. Linear Algebra.- 3. Functions of one variable.- 4. Dierential Calculus.- 5. Integral Calculus.- 6. Multivariable Calculus.- 7. Constrained Optimization.- 8. Trigonometry.- 9. Complex numbers.- 10. Difference equations.- 11. Differential equations.","brand":"Springer International Publishing AG","offers":[{"title":"Default Title","offer_id":48743066435927,"sku":"9783031052040","price":42.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783031052040.jpg?v=1720063961"},{"product_id":"statistics-for-business-and-economics-compendium-of-essential-formulas-9783662658482","title":"Statistics for Business and Economics: Compendium","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis 2nd edition compendium contains and explains essential statistical formulas within an economic context. Expanded by more than 100 pages compared to the 1st edition, the compendium has been supplemented with numerous additional practical examples, which will help readers to better understand the formulas and their practical applications. This statistical formulary is presented in a practice-oriented, clear, and understandable manner, as it is needed for meaningful and relevant application in global business, as well as in the academic setting and economic practice.\u003cbr\u003eThe topics presented include, but are not limited to: statistical signs and symbols, descriptive statistics, empirical distributions, ratios and index figures, correlation analysis, regression analysis, inferential statistics, probability calculation, probability distributions, theoretical distributions, statistical estimation methods, confidence intervals, statistical testing methods, the Peren-Clement index, and the usual statistical tables.\u003cbr\u003eGiven its scope, the book offers an indispensable reference guide and is a must-read for undergraduate and graduate students, as well as managers, scholars, and lecturers in business, politics, and economics.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eStatistical Signs and Symbols.- Descriptive Statistics.- Inferential Statistics.- Probability Calculation.- Statistical Tables.- Bibliography.- Index.","brand":"Springer-Verlag Berlin and Heidelberg GmbH \u0026 Co. KG","offers":[{"title":"Default Title","offer_id":48743143211351,"sku":"9783662658482","price":40.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783662658482.jpg?v=1720064299"},{"product_id":"smart-analysis-of-tourism-policy-efficiency-in-bulgaria-for-the-period-1980-2017-9788395771392","title":"Smart Analysis of Tourism Policy Efficiency in","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e The purpose of this study is to determine the role of tourism in the economy of Bulgaria. In this paper, we present the history of the Bulgarian tourism industry trends from the beginning to its contemporary policy patterns. We apply an econometric methodology consisting of unit root test, cointegration analysis, linear regression, correlation analysis, Granger causality test and 3-D visualizations by IBM Watson Studio based on the statistics for the period 1980-2017. Exploring the link between tourism and the economic development of Bulgaria, the tourism – led - growth hypothesis about Bulgaria is validated for the post-communism period. Our findings show that a relationship between tourism and Bulgaria’s economic development exists. We can conclude that tourism is in part an endogenous growth process. \u003c\/p\u003e\u003cbr\u003e \u003cp\u003e ABSTRACTING \u0026amp; INDEXING \u003c\/p\u003e \u003cp\u003e \u003cem\u003eSmart Analysis of Tourism Policy Efficiency in Bulgaria for the Period 1980-2017\u003c\/em\u003e is covered by the following services: \u003c\/p\u003e \u003cp\u003e \u003cbr\u003eBaidu Scholar\u003cbr\u003eBarnes \u0026amp; Noble\u003cbr\u003eBayerische Staatsbibliothek\u003cbr\u003eBDS\u003cbr\u003eBoD\u003cbr\u003eBowker Book Data\u003cbr\u003eCiando\u003cbr\u003eCNKI Scholar (China National Knowledge Infrastructure)\u003cbr\u003eDimensions\u003cbr\u003eEBSCO\u003cbr\u003eExLibris\u003cbr\u003eGoogle Books\u003cbr\u003eGoogle Scholar\u003cbr\u003eNaviga\u003cbr\u003eReadCube\u003cbr\u003eSemantic Scholar\u003cbr\u003eTDOne (TDNet)\u003cbr\u003eWorldCat (OCLC)\u003cbr\u003eX-MOL\u003cbr\u003e\u003cbr\u003eAdditionally, the proceedings volume is registered and indexed in the Crossref database and accessible on Amazon.\u003cbr\u003e \u003c\/p\u003e","brand":"De Gruyter","offers":[{"title":"Default Title","offer_id":48743203504471,"sku":"9788395771392","price":13.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9788395771392.jpg?v=1720064567"},{"product_id":"hands-on-intermediate-econometrics-using-r-templates-for-learning-quantitative-methods-and-r-software-9789811256738","title":"Hands-on Intermediate Econometrics Using R:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eHow to learn both applied statistics (econometrics) and free, open-source software R? This book allows students to have a sense of accomplishment by copying and pasting many hands-on templates provided here.The textbook is essential for anyone wishing to have a practical understanding of an extensive range of topics in Econometrics. No other text provides software snippets to learn so many new statistical tools with hands-on examples. The explicit knowledge of inputs and outputs of each new method allows the student to know which algorithm is worth studying. The book offers sufficient theoretical and algorithmic details about a vast range of statistical techniques.The second edition's preface lists the following topics generally absent in other textbooks. (i) Iteratively reweighted least squares, (ii) Pillar charts to represent 3D data. (iii) Stochastic frontier analysis (SFA) (iv) model selection with Mallows' Cp criterion. (v) Hodrick-Prescott (HP) filter. (vi) Automatic ARIMA models. (vi)  Nonlinear Granger-causality using kernel regressions and bootstrap confidence intervals. (vii) new Keynesian Phillips curve (NKPC). (viii) Market-neutral pairs trading using two cointegrated stocks. (ix) Artificial neural network (ANN) for product-specific forecasting. (x) Vector AR and VARMA models. (xi) New tools for diagnosing the endogeneity problem. (xii) The elegant set-up of k-class estimators and identification. (xiii)  Probit-logit models and Heckman selection bias correction. (xiv) Receiver operating characteristic (ROC) curves and areas under them. (xv) Confusion matrix. (xvi) Quantile regression (xvii) Elastic net estimator. (xviii) generalized Correlations (xix) maximum entropy bootstrap for time series. (xx) Convergence concepts quantified. (xxi) Generalized partial correlation coefficients (xxii) Panel data and duration (survival) models.","brand":"World Scientific Publishing Co Pte Ltd","offers":[{"title":"Default Title","offer_id":48743285391703,"sku":"9789811256738","price":63.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811256738.jpg?v=1720064925"},{"product_id":"hands-on-intermediate-econometrics-using-r-templates-for-learning-quantitative-methods-and-r-software-9789811256172","title":"Hands-on Intermediate Econometrics Using R:","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eHow to learn both applied statistics (econometrics) and free, open-source software R? This book allows students to have a sense of accomplishment by copying and pasting many hands-on templates provided here.The textbook is essential for anyone wishing to have a practical understanding of an extensive range of topics in Econometrics. No other text provides software snippets to learn so many new statistical tools with hands-on examples. The explicit knowledge of inputs and outputs of each new method allows the student to know which algorithm is worth studying. The book offers sufficient theoretical and algorithmic details about a vast range of statistical techniques.The second edition's preface lists the following topics generally absent in other textbooks. (i) Iteratively reweighted least squares, (ii) Pillar charts to represent 3D data. (iii) Stochastic frontier analysis (SFA) (iv) model selection with Mallows' Cp criterion. (v) Hodrick-Prescott (HP) filter. (vi) Automatic ARIMA models. (vi)  Nonlinear Granger-causality using kernel regressions and bootstrap confidence intervals. (vii) new Keynesian Phillips curve (NKPC). (viii) Market-neutral pairs trading using two cointegrated stocks. (ix) Artificial neural network (ANN) for product-specific forecasting. (x) Vector AR and VARMA models. (xi) New tools for diagnosing the endogeneity problem. (xii) The elegant set-up of k-class estimators and identification. (xiii)  Probit-logit models and Heckman selection bias correction. (xiv) Receiver operating characteristic (ROC) curves and areas under them. (xv) Confusion matrix. (xvi) Quantile regression (xvii) Elastic net estimator. (xviii) generalized Correlations (xix) maximum entropy bootstrap for time series. (xx) Convergence concepts quantified. (xxi) Generalized partial correlation coefficients (xxii) Panel data and duration (survival) models.","brand":"World Scientific Publishing Co Pte Ltd","offers":[{"title":"Default Title","offer_id":48743285621079,"sku":"9789811256172","price":121.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789811256172.jpg?v=1720064925"},{"product_id":"econometric-models-for-industrial-organization-9789813209008","title":"Econometric Models For Industrial Organization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eEconomic Models for Industrial Organization focuses on the specification and estimation of econometric models for research in industrial organization. In recent decades, empirical work in industrial organization has moved towards dynamic and equilibrium models, involving econometric methods which have features distinct from those used in other areas of applied economics. These lecture notes, aimed for a first or second-year PhD course, motivate and explain these econometric methods, starting from simple models and building to models with the complexity observed in typical research papers. The covered topics include discrete-choice demand analysis, models of dynamic behavior and dynamic games, multiple equilibria in entry games and partial identification, and auction models.","brand":"World Scientific Publishing Co Pte Ltd","offers":[{"title":"Default Title","offer_id":48743294992727,"sku":"9789813209008","price":31.35,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9789813209008.jpg?v=1723812658"},{"product_id":"asymptotic-theory-for-econometricians-9780127466521","title":"Asymptotic Theory for Econometricians","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAn econometric estimator is a solution to an optimization problem. This book provides the tools and concepts necessary to study the behavior of econometric estimators and test statistics in large samples.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eThe Linear Model and Instrumental Variables Estimators. Consistency. Laws of Large Numbers. Asymptotic Normality. Central Limit Theory. Estimating Asymptotic Covariance Matrices. Functional Central Limit Theory and Applications. Directions for Further Study. Solution Set. References. Index.","brand":"Emerald Publishing Limited","offers":[{"title":"Default Title","offer_id":48864159957335,"sku":"9780127466521","price":96.06,"currency_code":"GBP","in_stock":true}]},{"product_id":"even-you-can-learn-statistics-and-analytics-9780137654765","title":"Even You Can Learn Statistics and Analytics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eDavid M. Levine\u003c\/b\u003e and \u003cb\u003eDavid F. Stephan\u003c\/b\u003e are part of a writing team known for their series of business statistics textbooks that include \u003ci\u003eBasic Business Statistics, Business Statistics: A First Course, and Statistics for Managers Using Microsoft Excel\u003c\/i\u003e. In long teaching careers at Baruch College, both were known for their classroom innovations, with Levine being honored with a Presidential Excellence Award for Distinguished Teaching Award and Stephan granted the privilege to design and develop the College's first computer-based classroom. Both are active members of the Data, Analytics and Statistics Instruction SIG of the Decision Sciences Institute.\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eLevine\u003c\/b\u003e is Professor Emeritus of Information Systems at Baruch College. He is nationally recognized innovator in business statistics education and is also the coauthor of \u003ci\u003eApplied Statistics for Engineers and Scientists Using Microsoft Excel and Minitab\u003c\/i\u003e. Levine is also the author\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eIntroduction The Even You Can Learn Statistics and Analytics Owner's Manual. xiii    \u003cbr\u003e    Chapter 1 Fundamentals of Statistics. 1    \u003cbr\u003e        1.1 The First Three Words of Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2    \u003cbr\u003e        1.2 The Fourth and Fifth Words. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4    \u003cbr\u003e        1.3 The Branches of Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4    \u003cbr\u003e        1.4 Sources of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5    \u003cbr\u003e        1.5 Sampling Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7    \u003cbr\u003e        1.6 Sample Selection Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8    \u003cbr\u003e    Chapter 2 Presenting Data in Tables and Charts . 15    \u003cbr\u003e        2.1 Presenting Categorical Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15    \u003cbr\u003e        2.2 Presenting Numerical Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23    \u003cbr\u003e        2.3 “Bad” Charts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29    \u003cbr\u003e    Chapter 3 Descriptive Statistics. 45    \u003cbr\u003e        3.1 Measures of Central Tendency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45    \u003cbr\u003e        3.2 Measures of Position. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49    \u003cbr\u003e        3.3 Measures of Variation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54    \u003cbr\u003e        3.4 Shape of Distributions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59    \u003cbr\u003e    Chapter 4 Probability. 75    \u003cbr\u003e        4.1 Events. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75    \u003cbr\u003e        4.2 More Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76    \u003cbr\u003e        4.3 Some Rules of Probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78    \u003cbr\u003e        4.4 Assigning Probabilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81    \u003cbr\u003e    Chapter 5 Probability Distributions. 87    \u003cbr\u003e        5.1 Probability Distributions for Discrete Variables. . . . . . . . . . . . . . . . . . . . . . . . 87    \u003cbr\u003e        5.2 The Binomial and Poisson Probability Distributions. . . . . . . . . . . . . . . . . . . . 93    \u003cbr\u003e        5.3 Continuous Probability Distributions and the Normal Distribution . . . . . . . 100    \u003cbr\u003e        5.4 The Normal Probability Plot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108    \u003cbr\u003e    Chapter 6 Sampling Distributions and Confidence Intervals. 121    \u003cbr\u003e        6.1 Foundational Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122    \u003cbr\u003e        6.2 Sampling Error and Confidence Intervals. . . . . . . . . . . . . . . . . . . . . . . . . . . 125    \u003cbr\u003e        6.3 Confidence Interval Estimate for the Mean Using the t Distribution (? Unknown). . . 128    \u003cbr\u003e        6.4 Confidence Interval Estimation for Categorical Variables . . . . . . . . . . . . . . . 131    \u003cbr\u003e        6.5 Confidence Interval Estimation When Normality Cannot Be Assumed. . . . . 134    \u003cbr\u003e    Chapter 7 Fundamentals of Hypothesis Testing. 145    \u003cbr\u003e        7.1 The Null and Alternative Hypotheses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145    \u003cbr\u003e        7.2 Hypothesis Testing Issues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147    \u003cbr\u003e        7.3 Decision-Making Risks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149    \u003cbr\u003e        7.4 Performing Hypothesis Testing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150    \u003cbr\u003e        7.5 Types of Hypothesis Tests. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152    \u003cbr\u003e    Chapter 8 Hypothesis Testing: Z and t Tests. 157    \u003cbr\u003e        8.1 Test for the Difference Between Two Proportions . . . . . . . . . . . . . . . . . . . . . 157    \u003cbr\u003e        8.2 Test for the Difference Between the Means of Two Independent Groups . . . . 163    \u003cbr\u003e        8.3 The Paired t Test. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168    \u003cbr\u003e    Chapter 9 Hypothesis Testing: Chi-Square Tests and the One-Way Analysis of Variance (ANOVA). 183    \u003cbr\u003e        9.1 Chi-Square Test for Two-Way Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183    \u003cbr\u003e        9.2 One-Way Analysis of Variance (ANOVA): Testing for the    \u003cbr\u003e        Differences Among the Means of More Than Two Groups. . . . . . . . . . . . . . . 191    \u003cbr\u003e    Chapter 10 Simple Linear Regression. 211    \u003cbr\u003e        10.1 Basics of Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211    \u003cbr\u003e        10.2 Developing a Simple Linear Regression Model. . . . . . . . . . . . . . . . . . . . . . 214    \u003cbr\u003e        10.3 Measures of Variation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221    \u003cbr\u003e        10.4 Inferences About the Slope. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226    \u003cbr\u003e        10.5 Common Mistakes When Using Regression Analysis . . . . . . . . . . . . . . . . . 229    \u003cbr\u003e    Chapter 11 Multiple Regression. 243    \u003cbr\u003e        11.1 The Multiple Regression Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243    \u003cbr\u003e        11.2 Coefficient of Multiple Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246    \u003cbr\u003e        11.3 The Overall F Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246    \u003cbr\u003e        11.4 Residual Analysis for the Multiple Regression Model . . . . . . . . . . . . . . . . . 247    \u003cbr\u003e        11.5 Inferences Concerning the Population Regression Coefficients. . . . . . . . . . 248    \u003cbr\u003e    Chapter 12 Introduction to Analytics. 259    \u003cbr\u003e        12.1 Basic Concepts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259    \u003cbr\u003e        12.2 Descriptive Analytics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265    \u003cbr\u003e        12.3 Typical Descriptive Analytics Visualizations. . . . . . . . . . . . . . . . . . . . . . . . 269    \u003cbr\u003e    Chapter 13 Predictive Analytics. 279    \u003cbr\u003e        13.1 Predictive Analytics Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279    \u003cbr\u003e        13.2 More About Predictive Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281    \u003cbr\u003e        13.3 Tree Induction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284    \u003cbr\u003e        13.4 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287    \u003cbr\u003e        13.5 Association Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290    \u003cbr\u003e    Appendix A Microsoft Excel Operation and Configuration . 299    \u003cbr\u003e    Appendix B Review of Arithmetic and Algebra. 301    \u003cbr\u003e    Appendix C Statistical Tables. 311    \u003cbr\u003e    Appendix D Spreadsheet Tips . 339    \u003cbr\u003e    Appendix E Advanced Techniques. 343    \u003cbr\u003e    Appendix F Documentation for Downloadable Files. 353    \u003cbr\u003e    \u003cbr\u003e    \u003cbr\u003e    9780137654765, TOC, 4\/25\/2022    \u003cbr\u003e    \u003cbr\u003e\u003c\/p\u003e","brand":"Pearson Education (US)","offers":[{"title":"Default Title","offer_id":48864175784279,"sku":"9780137654765","price":23.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780137654765.jpg?v=1722270748"},{"product_id":"econometric-analysis-of-cross-section-and-panel-data-second-edition-the-mit-press-9780262232586","title":"Econometric Analysis of Cross Section and Panel","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eThe second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated.\u003c\/b\u003e\u003cp\u003eThe second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and\/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis.\u003c\/p\u003e\u003cp\u003e\u003ci\u003eEconometric Analysis of Cross Section and Panel Dat\u003c\/i\u003e\u003c\/p\u003e","brand":"MIT Press Ltd","offers":[{"title":"Default Title","offer_id":48864303350103,"sku":"9780262232586","price":94.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780262232586.jpg?v=1722271309"},{"product_id":"business-analytics-9780357902202","title":"Business Analytics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eDevelop the analytical skills that are in high demand in businesses today with Camm\/Cochran\/Fry\/Ohlmann's best-selling BUSINESS ANALYTICS, 5E. You master the full range of analytics as you strengthen descriptive, predictive and prescriptive analytic skills. Real examples and memorable visuals clearly illustrate data and results. Step-by-step instructions guide you through using Excel, Tableau, R or the Python-based Orange data mining software to perform advanced analytics. Practical, relevant problems at all levels of difficulty let you apply what you've learned. Updates throughout this edition address topics beyond traditional quantitative concepts, such as data wrangling, data visualization and data mining, which are increasingly important in today's business environment. MindTap and WebAssign online learning platforms are also available with an interactive eBook, algorithmic practice problems and Exploring Analytics visualizations to strengthen your understanding of key concepts.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction. 2. Descriptive Statistics. 3. Data Visualization. 4. Data Wrangling. 5. Probability: An Introduction to Modeling Uncertainty. 6. Descriptive Data Mining. 7. Statistical Inference. 8. Linear Regression. 9. Time Series Analysis and Forecasting. 10. Predictive Data Mining: Regression. 11. Predictive Data Mining: Classification. 12. Spreadsheet Modeling. 13. Monte Carlo Simulation. 14. Linear Optimization Models. 15. Integer Linear Optimization Models. 16. Nonlinear Optimization Models. 17. Decision Analysis. Appendix A: Basics of Excel. Appendix B: Database Basics with Microsoft Access. Appendix C: Solutions to Even-Numbered Questions (online).","brand":"Cengage Learning, Inc","offers":[{"title":"Default Title","offer_id":48864478560599,"sku":"9780357902202","price":239.11,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780357902202.jpg?v=1722272131"},{"product_id":"statistical-rethinking-9780367139919","title":"Statistical Rethinking","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003e\u003cstrong\u003eWinner of the 2024 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)\u003c\/strong\u003e\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eStatistical Rethinking: A Bayesian Course with Examples in R and Stan\u003c\/b\u003e builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.\u003c\/p\u003e\u003cp\u003eThe text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.\u003c\/p\u003e\u003cp\u003eThe second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFeatures\u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e \u003cul\u003e \u003cli\u003eIntegrates working code into the main text.\u003c\/li\u003e \u003c\/ul\u003e \u003c\/ul\u003e\u003cul\u003e \u003cul\u003e \u003cli\u003eIllustrates concepts through worked data analysis examples.\u003c\/li\u003e \u003c\/ul\u003e \u003c\/ul\u003e\u003cul\u003e \u003cul\u003e \u003cli\u003eEmphasizes understanding assumptions and how assumptions are reflected in code.\u003c\/li\u003e \u003c\/ul\u003e \u003c\/ul\u003e\u003cul\u003e \u003cul\u003e \u003cli\u003eOffers more detailed explanations of the mathematics in optional sections.\u003c\/li\u003e \u003c\/ul\u003e \u003c\/ul\u003e\u003cul\u003e \u003cul\u003e \u003cli\u003ePresents examples of using the dagitty R package to analyze causal graphs.\u003cbr\u003e\u003cbr\u003e \u003c\/li\u003e \u003cli\u003eProvides the rethinking R package on the author's website and on GitHub.\u003c\/li\u003e \u003c\/ul\u003e \u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\"The first edition (and this second edition) of *Statistical Rethinking* beautifully outlines the key steps in the statistical analysis cycle, starting from formulating the research question. I find that many statistics textbooks omit the issue of problem formulation and either jump into data acquisition or further into analysis after the fact. McElreath has created a fantastic text for students of applied statistics to not only learn about the Bayesian paradigm, but also to gain a deep appreciation for the statistical thought process. I also found that many students appreciated McElreath’s engaging writing style and humor, and personally found the infusion of humor quite refreshing.\"\u003cbr\u003e\u003ci\u003e- Adam Loy, Carleton College\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e\"(The chapter) ‘Generalized Linear Madness’ represents another great chapter of an even better edition of an already awesome textbook.\"\u003cbr\u003e\u003ci\u003e- Benjamin K. Goodrich, Columbia University\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e\"(Chapter 16) is a worthy concluding chapter to a masterful book. Eminently readable and enjoyable. Brimful of small thought-provoking bits which may inspire deeper studies, but first and foremost a window on the trial and error process involved in building a statistical model or rather, indeed, any scientific theory.\"\u003cbr\u003e\u003ci\u003e- Josep Fortiana Gregori, University of Barcelona\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e\"I do regard the manuscript as technically correct, clearly written, and at an appropriate level of difficulty. The technical approaches and the R codes of the book are perfect for our students. They can learn concepts of Bayesian models, data analysis, and model validation methods through using the R codes. The codes help students to have better understanding of the models and data analysis process.\" \u003cbr\u003e\u003ci\u003e- Nguyet Nguyen, Youngstown State University\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e \"As a textbook it successfully brings the statistician’s toolbox to a wider audience with an accessible style and good humour. It should be recommended to statistics students, both old and new.\"\u003c\/p\u003e\u003cp\u003e- Nathan Green, \u003ci\u003eJournal of the Royal Statistical Society\u003c\/i\u003e, 2021, https:\/\/doi.org\/10.1111\/rssa.12755\u003c\/p\u003e\u003cp\u003e\"In conclusion, \u003ci\u003eStatistical Rethinking\u003c\/i\u003e frames usual methods and tools taught in graduate statistical courses into a different way to encourage the reader to understand the details and appreciate the underlying assumptions. The accompanying R package offers example codes for some interesting problems that are not available in standard library or other popular packages. This book can be used as a supplement to a graduate course or it can be used by practitioners wanting to brush up their knowledge with better understanding of statistical techniques.\"\u003c\/p\u003e\u003cp\u003e- Abhirup Mallik in \u003ci\u003eTechnometrics\u003c\/i\u003e, August 2021\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\"The first edition (and this second edition) of *Statistical Rethinking* beautifully outlines the key steps in the statistical analysis cycle, starting from formulating the research question. I find that many statistics textbooks omit the issue of problem formulation and either jump into data acquisition or further into analysis after the fact. McElreath has created a fantastic text for students of applied statistics to not only learn about the Bayesian paradigm, but also to gain a deep appreciation for the statistical thought process. I also found that many students appreciated McElreath’s engaging writing style and humor, and personally found the infusion of humor quite refreshing.\"\u003cbr\u003e\u003ci\u003e~Adam Loy, Carleton College\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e\"(The chapter) ‘Generalized Linear Madness’ represents another great chapter of an even better edition of an already awesome textbook.\"\u003cbr\u003e\u003ci\u003e~Benjamin K. Goodrich, Columbia University\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e\"(Chapter 16) is a worthy concluding chapter to a masterful book. Eminently readable and enjoyable. Brimful of small thought-provoking bits which may inspire deeper studies, but first and foremost a window on the trial and error process involved in building a statistical model or rather, indeed, any scientific theory.\"\u003cbr\u003e\u003ci\u003e~Josep Fortiana Gregori, University of Barcelona\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e\"I do regard the manuscript as technically correct, clearly written, and at an appropriate level of difficulty. The technical approaches and the R codes of the book are perfect for our students. They can learn concepts of Bayesian models, data analysis, and model validation methods through using the R codes. The codes help students to have better understanding of the models and data analysis process.\" \u003cbr\u003e\u003ci\u003e~Nguyet Nguyen, Youngstown State University\u003c\/i\u003e\u003c\/p\u003e\u003cp\u003e\"In conclusion, \u003ci\u003eStatistical Rethinking\u003c\/i\u003e frames usual methods and tools taught in graduate statistical courses into a different way to encourage the reader to understand the details and appreciate the underlying assumptions. The accompanying R package offers example codes for some interesting problems that are not available in standard library or other popular packages. This book can be used as a supplement to a graduate course or it can be used by practitioners wanting to brush up their knowledge with better understanding of statistical techniques.\"\u003cbr\u003e~Abhirup Mallik in \u003ci\u003eTechnometrics\u003c\/i\u003e, August 2021\u003c\/p\u003e\u003cp\u003e\"As a textbook it successfully brings the statistician’s toolbox to a wider audience with an accessible style and good humour. It should be recommended to statistics students, both old and new.\"\u003cbr\u003e~ Nathan Green, \u003ci\u003eJournal of the Royal Statistical Society\u003c\/i\u003e, 2021\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e1. The Golem of Prague. 2. Small Worlds and Large Worlds. Chapter 3. Sampling the Imaginary. 4. Geocentric Models. 5. The Many Variables \u0026amp; The Spurious Waffles. 6. The Haunted DAG \u0026amp; The Causal Terror. 7. Ulysses’ Compass. 8. Conditional Manatees. 8. Conditional Manatees. 9. Markov Chain Monte Carlo. 10. Big Entropy and the Generalized Linear Model. 11. God Spiked the Integers. 12. Monsters and Mixtures. 13. Models With Memory. 14. Adventures in Covariance. 15. Missing Data and Other Opportunities. 16. Generalized Linear Madness. 17. Horoscopes.\u003c\/p\u003e","brand":"Taylor \u0026 Francis Ltd","offers":[{"title":"Default Title","offer_id":48864487768407,"sku":"9780367139919","price":73.14,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780367139919.jpg?v=1722272171"},{"product_id":"naked-statistics-stripping-the-dread-from-the-data-9780393347777","title":"Naked Statistics  Stripping the Dread from the","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA \u003cem\u003eNew York Times\u003c\/em\u003e bestseller\u003cbr\u003e \u003cbr\u003e \"Brilliant, funny…the best math teacher you never had.\" —\u003cem\u003eSan Francisco Chronicle\u003c\/em\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Sparkling and intensely readable…A riff on basic statistics that is neither textbook nor essay but a happy amalgam of the two.\" -- New York Times\u003cbr\u003e\"\u003cem\u003eNaked Statistics\u003c\/em\u003e is an apt title. Charles Wheelan strips away the superfluous outer garments and exposes the underlying beauty of the subject in a way that everyone can appreciate.\" -- Hal Varian, chief economist at Google\u003cbr\u003e\"[Wheelan] does something unique here: he makes statistics interesting and fun. His book strips the subject of its complexity to expose the sexy stuff underneath.\" -- The Economist\u003cbr\u003e\"Almost anyone interested in sports, politics, business, and the myriad of other areas in which statistics rule the roost today will benefit from this highly readable, on target, and important book.\" -- Frank Newport, Gallup editor-in-chief\u003cbr\u003e\"A fun, engaging book that shows why statistics is a vital tool for anyone who wants to understand the modern world.\" -- Jacob J. Goldstein, NPR’s Planet Money\u003cbr\u003e\"Two phrases you don’t often see together: ‘statistics primer’ and ‘rollicking good time.’ Until Charlie Wheelan got to it, that is. This book explains the way statistical ideas can help you understand much of everyday life.\" -- Austan Goolsbee, professor of economics at the University of Chicago and former chairman of the Council of Economic Advisers\u003cbr\u003e\"A well written, surprisingly funny, and enthusiastic primer on statistics…It is hard to imagine a more accessible introduction to a field with an undeserved reputation for inaccessibility.\" -- New Republic\u003cbr\u003e\"With humor and an engaging conversational style, [Wheelan] walks the reader through the basics of statistical concepts and their applications, using real-world examples to illustrate how statistics work and why they matter. All in all, it’s an excellent book.\" -- Science News\u003cbr\u003e\"\u003cem\u003eNaked Statistics\u003c\/em\u003e is the book that I wish I had in 1991, the year that I took stats during my first semester at grad school…Wheelan is a master of explaining the core concepts and methods of statistics in a way that is both accessible and relevant. He is clearly a master teacher, and his gifts are in abundant display in \u003cem\u003eNaked Statistics\u003c\/em\u003e.\" -- Inside Higher Ed","brand":"WW Norton \u0026 Co","offers":[{"title":"Default Title","offer_id":48864546357591,"sku":"9780393347777","price":13.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780393347777.jpg?v=1722272412"},{"product_id":"an-introduction-to-analysis-of-financial-data-with-r-9780470890813","title":"An Introduction to Analysis of Financial Data","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eA complete set of statistical tools for beginning financial analysts from a leading authority\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWritten by one of the leading experts on the topic, \u003ci\u003eAn Introduction to Analysis of Financial Data with R\u003c\/i\u003e explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research.\u003c\/p\u003e \u003cp\u003eThe author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eLinear\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e“I found this book highly informative and interesting to read. The proper mix of theory and hands-on programming examples makes it recommended reading for both R programmers interested in finance and financial analysts with a basic programming background. Well written and following a clear and defined logical layout, the author has written a current reference text on using a powerful open-source programming language for typical financial analysis.”  (\u003ci\u003eComputing Reviews\u003c\/i\u003e, 25 March 2014)\u003c\/p\u003e \u003cp\u003e“All in all, this book is a good and useful introduction to financial time series with many real-world examples. It is suitable for use both as a textbook and for self-study, with exercises provided at the end of each chapter.”  (\u003ci\u003eInternational Statistical Review\u003c\/i\u003e, 14 June 2013)\u003c\/p\u003e\n\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 FINANCIAL DATA AND THEIR PROPERTIES 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Asset Returns 2\u003c\/p\u003e \u003cp\u003e1.2 Bond Yields and Prices 7\u003c\/p\u003e \u003cp\u003e1.3 Implied Volatility 10\u003c\/p\u003e \u003cp\u003e1.4 R Packages and Demonstrations 12\u003c\/p\u003e \u003cp\u003e1.4.1 Installation of R Packages 12\u003c\/p\u003e \u003cp\u003e1.4.2 The Quantmod Package 12\u003c\/p\u003e \u003cp\u003e1.4.3 Some Basic R Commands 16\u003c\/p\u003e \u003cp\u003e1.5 Examples of Financial Data 17\u003c\/p\u003e \u003cp\u003e1.6 Distributional Properties of Returns 20\u003c\/p\u003e \u003cp\u003e1.6.1 Review of Statistical Distributions and Their Moments 20\u003c\/p\u003e \u003cp\u003e1.7 Visualization of Financial Data 27\u003c\/p\u003e \u003cp\u003e1.8 Some Statistical Distributions 32\u003c\/p\u003e \u003cp\u003e1.8.1 Normal Distribution 32\u003c\/p\u003e \u003cp\u003e1.8.2 Lognormal Distribution 32\u003c\/p\u003e \u003cp\u003e1.8.3 Stable Distribution 33\u003c\/p\u003e \u003cp\u003e1.8.4 Scale Mixture of Normal Distributions 33\u003c\/p\u003e \u003cp\u003e1.8.5 Multivariate Returns 34\u003c\/p\u003e \u003cp\u003eExercises 36\u003c\/p\u003e \u003cp\u003eReferences 37\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 LINEAR MODELS FOR FINANCIAL TIME SERIES 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Stationarity 40\u003c\/p\u003e \u003cp\u003e2.2 Correlation and Autocorrelation Function 43\u003c\/p\u003e \u003cp\u003e2.3 White Noise and Linear Time Series 50\u003c\/p\u003e \u003cp\u003e2.4 Simple Autoregressive Models 51\u003c\/p\u003e \u003cp\u003e2.4.1 Properties of AR Models 52\u003c\/p\u003e \u003cp\u003e2.4.2 Identifying AR Models in Practice 60\u003c\/p\u003e \u003cp\u003e2.4.3 Goodness of Fit 67\u003c\/p\u003e \u003cp\u003e2.4.4 Forecasting 67\u003c\/p\u003e \u003cp\u003e2.5 Simple Moving Average Models 69\u003c\/p\u003e \u003cp\u003e2.5.1 Properties of MA Models 72\u003c\/p\u003e \u003cp\u003e2.5.2 Identifying MA Order 73\u003c\/p\u003e \u003cp\u003e2.5.3 Estimation 74\u003c\/p\u003e \u003cp\u003e2.5.4 Forecasting Using MA Models 75\u003c\/p\u003e \u003cp\u003e2.6 Simple ARMA Models 78\u003c\/p\u003e \u003cp\u003e2.6.1 Properties of ARMA(1,1) Models 79\u003c\/p\u003e \u003cp\u003e2.6.2 General ARMA Models 80\u003c\/p\u003e \u003cp\u003e2.6.3 Identifying ARMA Models 81\u003c\/p\u003e \u003cp\u003e2.6.4 Forecasting Using an ARMA Model 84\u003c\/p\u003e \u003cp\u003e2.6.5 Three Model Representations for an ARMA Model 84\u003c\/p\u003e \u003cp\u003e2.7 Unit-Root Nonstationarity 86\u003c\/p\u003e \u003cp\u003e2.7.1 Random Walk 86\u003c\/p\u003e \u003cp\u003e2.7.2 Random Walk with Drift 88\u003c\/p\u003e \u003cp\u003e2.7.3 Trend-Stationary Time Series 90\u003c\/p\u003e \u003cp\u003e2.7.4 General Unit-Root Nonstationary Models 91\u003c\/p\u003e \u003cp\u003e2.7.5 Unit-Root Test 91\u003c\/p\u003e \u003cp\u003e2.8 Exponential Smoothing 96\u003c\/p\u003e \u003cp\u003e2.9 Seasonal Models 98\u003c\/p\u003e \u003cp\u003e2.9.1 Seasonal Differencing 99\u003c\/p\u003e \u003cp\u003e2.9.2 Multiplicative Seasonal Models 101\u003c\/p\u003e \u003cp\u003e2.9.3 Seasonal Dummy Variable 107\u003c\/p\u003e \u003cp\u003e2.10 Regression Models with Time Series Errors 110\u003c\/p\u003e \u003cp\u003e2.11 Long-Memory Models 117\u003c\/p\u003e \u003cp\u003e2.12 Model Comparison and Averaging 120\u003c\/p\u003e \u003cp\u003e2.12.1 In-sample Comparison 120\u003c\/p\u003e \u003cp\u003e2.12.2 Out-of-sample Comparison 121\u003c\/p\u003e \u003cp\u003e2.12.3 Model Averaging 125\u003c\/p\u003e \u003cp\u003eExercises 125\u003c\/p\u003e \u003cp\u003eReferences 127\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 CASE STUDIES OF LINEAR TIME SERIES 128\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Weekly Regular Gasoline Price 129\u003c\/p\u003e \u003cp\u003e3.1.1 Pure Time Series Model 130\u003c\/p\u003e \u003cp\u003e3.1.2 Use of Crude Oil Prices 133\u003c\/p\u003e \u003cp\u003e3.1.3 Use of Lagged Crude Oil Prices 134\u003c\/p\u003e \u003cp\u003e3.1.4 Out-of-Sample Predictions 135\u003c\/p\u003e \u003cp\u003e3.2 Global Temperature Anomalies 140\u003c\/p\u003e \u003cp\u003e3.2.1 Unit-Root Stationarity 141\u003c\/p\u003e \u003cp\u003e3.2.2 Trend-Nonstationarity 145\u003c\/p\u003e \u003cp\u003e3.2.3 Model Comparison 148\u003c\/p\u003e \u003cp\u003e3.2.4 Long-Term Prediction 150\u003c\/p\u003e \u003cp\u003e3.2.5 Discussion 153\u003c\/p\u003e \u003cp\u003e3.3 US Monthly Unemployment Rates 157\u003c\/p\u003e \u003cp\u003e3.3.1 Univariate Time Series Models 157\u003c\/p\u003e \u003cp\u003e3.3.2 An Alternative Model 161\u003c\/p\u003e \u003cp\u003e3.3.3 Model Comparison 165\u003c\/p\u003e \u003cp\u003e3.3.4 Use of Initial Jobless Claims 165\u003c\/p\u003e \u003cp\u003e3.3.5 Comparison 173\u003c\/p\u003e \u003cp\u003eExercises 174\u003c\/p\u003e \u003cp\u003eReferences 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 ASSET VOLATILITY AND VOLATILITY MODELS 176\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Characteristics of Volatility 177\u003c\/p\u003e \u003cp\u003e4.2 Structure of a Model 178\u003c\/p\u003e \u003cp\u003e4.3 Model Building 181\u003c\/p\u003e \u003cp\u003e4.4 Testing for ARCH Effect 182\u003c\/p\u003e \u003cp\u003e4.5 The ARCH Model 185\u003c\/p\u003e \u003cp\u003e4.5.1 Properties of ARCH Models 186\u003c\/p\u003e \u003cp\u003e4.5.2 Advantages and Weaknesses of ARCH Models 187\u003c\/p\u003e \u003cp\u003e4.5.3 Building an ARCH Model 188\u003c\/p\u003e \u003cp\u003e4.5.4 Some Examples 193\u003c\/p\u003e \u003cp\u003e4.6 The GARCH Model 199\u003c\/p\u003e \u003cp\u003e4.6.1 An Illustrative Example 201\u003c\/p\u003e \u003cp\u003e4.6.2 Forecasting Evaluation 210\u003c\/p\u003e \u003cp\u003e4.6.3 A Two-Pass Estimation Method 210\u003c\/p\u003e \u003cp\u003e4.7 The Integrated GARCH Model 211\u003c\/p\u003e \u003cp\u003e4.8 The GARCH-M Model 213\u003c\/p\u003e \u003cp\u003e4.9 The Exponential Garch Model 215\u003c\/p\u003e \u003cp\u003e4.9.1 An Illustrative Example 217\u003c\/p\u003e \u003cp\u003e4.9.2 An Alternative Model Form 218\u003c\/p\u003e \u003cp\u003e4.9.3 Second Example 218\u003c\/p\u003e \u003cp\u003e4.9.4 Forecasting Using an EGARCH Model 220\u003c\/p\u003e \u003cp\u003e4.10 The Threshold Garch Model 222\u003c\/p\u003e \u003cp\u003e4.11 Asymmetric Power ARCH Models 224\u003c\/p\u003e \u003cp\u003e4.12 Nonsymmetric GARCH Model 226\u003c\/p\u003e \u003cp\u003e4.13 The Stochastic Volatility Model 228\u003c\/p\u003e \u003cp\u003e4.14 Long-Memory Stochastic Volatility Models 230\u003c\/p\u003e \u003cp\u003e4.15 Alternative Approaches 232\u003c\/p\u003e \u003cp\u003e4.15.1 Use of High Frequency Data 232\u003c\/p\u003e \u003cp\u003e4.15.2 Use of Daily Open, High, Low, and Close Prices 235\u003c\/p\u003e \u003cp\u003eExercises 239\u003c\/p\u003e \u003cp\u003eReferences 241\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 APPLICATIONS OF VOLATILITY MODELS 243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Garch Volatility Term Structure 244\u003c\/p\u003e \u003cp\u003e5.1.1 Term Structure 246\u003c\/p\u003e \u003cp\u003e5.2 Option Pricing and Hedging 248\u003c\/p\u003e \u003cp\u003e5.3 Time-Varying Correlations and Betas 251\u003c\/p\u003e \u003cp\u003e5.3.1 Time-Varying Betas 256\u003c\/p\u003e \u003cp\u003e5.4 Minimum Variance Portfolios 259\u003c\/p\u003e \u003cp\u003e5.5 Prediction 263\u003c\/p\u003e \u003cp\u003eExercises 271\u003c\/p\u003e \u003cp\u003eReferences 272\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 HIGH FREQUENCY FINANCIAL DATA 274\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Nonsynchronous Trading 275\u003c\/p\u003e \u003cp\u003e6.2 Bid–Ask Spread of Trading Prices 279\u003c\/p\u003e \u003cp\u003e6.3 Empirical Characteristics of Trading Data 282\u003c\/p\u003e \u003cp\u003e6.4 Models for Price Changes 285\u003c\/p\u003e \u003cp\u003e6.4.1 Ordered Probit Model 288\u003c\/p\u003e \u003cp\u003e6.4.2 A Decomposition Model 293\u003c\/p\u003e \u003cp\u003e6.5 Duration Models 298\u003c\/p\u003e \u003cp\u003e6.5.1 Diurnal Component 299\u003c\/p\u003e \u003cp\u003e6.5.2 The ACD Model 301\u003c\/p\u003e \u003cp\u003e6.5.3 Estimation 303\u003c\/p\u003e \u003cp\u003e6.6 Realized Volatility 308\u003c\/p\u003e \u003cp\u003e6.6.1 Handling Microstructure Noises 313\u003c\/p\u003e \u003cp\u003e6.6.2 Discussion 317\u003c\/p\u003e \u003cp\u003eAppendix A: Some Probability Distributions 320\u003c\/p\u003e \u003cp\u003eAppendix B: Hazard Function 323\u003c\/p\u003e \u003cp\u003eExercises 324\u003c\/p\u003e \u003cp\u003eReferences 325\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 VALUE AT RISK 327\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Risk Measure and Coherence 328\u003c\/p\u003e \u003cp\u003e7.1.1 Value at Risk (VaR) 329\u003c\/p\u003e \u003cp\u003e7.1.2 Expected Shortfall 334\u003c\/p\u003e \u003cp\u003e7.2 Remarks on Calculating Risk Measures 336\u003c\/p\u003e \u003cp\u003e7.3 Riskmetrics 337\u003c\/p\u003e \u003cp\u003e7.3.1 Discussion 342\u003c\/p\u003e \u003cp\u003e7.3.2 Multiple Positions 343\u003c\/p\u003e \u003cp\u003e7.4 An Econometric Approach 345\u003c\/p\u003e \u003cp\u003e7.4.1 Multiple Periods 348\u003c\/p\u003e \u003cp\u003e7.5 Quantile Estimation 352\u003c\/p\u003e \u003cp\u003e7.5.1 Quantile and Order Statistics 353\u003c\/p\u003e \u003cp\u003e7.5.2 Quantile Regression 354\u003c\/p\u003e \u003cp\u003e7.6 Extreme Value Theory 358\u003c\/p\u003e \u003cp\u003e7.6.1 Review of Extreme Value Theory 358\u003c\/p\u003e \u003cp\u003e7.6.2 Empirical Estimation 361\u003c\/p\u003e \u003cp\u003e7.6.3 Application to Stock Returns 363\u003c\/p\u003e \u003cp\u003e7.7 An Extreme Value Approach to Var 368\u003c\/p\u003e \u003cp\u003e7.7.1 Discussion 370\u003c\/p\u003e \u003cp\u003e7.7.2 Multiperiod VaR 371\u003c\/p\u003e \u003cp\u003e7.7.3 Return Level 371\u003c\/p\u003e \u003cp\u003e7.8 Peaks Over Thresholds 372\u003c\/p\u003e \u003cp\u003e7.8.1 Statistical Theory 373\u003c\/p\u003e \u003cp\u003e7.8.2 Mean Excess Function 374\u003c\/p\u003e \u003cp\u003e7.8.3 Estimation 376\u003c\/p\u003e \u003cp\u003e7.8.4 An Alternative Parameterization 378\u003c\/p\u003e \u003cp\u003e7.9 The Stationary Loss Processes 381\u003c\/p\u003e \u003cp\u003eExercises 383\u003c\/p\u003e \u003cp\u003eReferences 384\u003c\/p\u003e \u003cp\u003eIndex 387\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48864641712471,"sku":"9780470890813","price":106.16,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470890813.jpg?v=1722272857"},{"product_id":"microeconometrics-9780521848053","title":"Microeconometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis book deals with methods and models of microeconometrics, the statistical modeling of behavioral relationships based on data from sample surveys or actual or quasi-social experiments. The book is oriented to the graduate student and researcher using such data. The level of the book is post-first year PhD economics.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e'This book presents an elegant and accessible treatment of the broad range of rapidly expanding topics currently being studied by microeconometricians. Thoughtful, intuitive, and careful in laying out central concepts of sophisticated econometric methodologies, it is not only an excellent textbook for students, but also an invaluable reference text for practitioners and researchers.' Cheng Hsiao, University of Southern California\u003cbr\u003e'I wish Microeconometrics was available when I was a student!  Here, in one place - and in clear and readable prose - you can find all of the tools that are necessary to do cutting-edge applied economic analysis, and with many helpful examples.' Alan Krueger, Princeton University\u003cbr\u003e'Cameron and Trivedi have written a remarkably thorough and up-to-date treatment of microeconometric methods. This is not a superficial cookbook; the early chapters carefully lay the theoretical foundations on which the authors build their discussion of methods for discrete and limited dependent variables and for analysis of longitudinal data. A distinctive feature of the book is its attention to cutting-edge topics like semiparametric regression, bootstrap methods, simulation-based estimation, and empirical likelihood estimation. A highly valuable book.'  Gary Solon, University of Michigan\u003cbr\u003e'The empirical analysis of micro data is more widespread than ever before. The book by Cameron and Trivedi contains a superb treatment of all the methods that economists like to apply to such data. What is more, it fully integrates a number of exciting new methods that have become applicable due to recent advances in computer technology. The text is in perfect balance between econometric theory and empirical intuition, and it contains many insightful examples.'   Gerard J. van den Berg, Free University, Amsterdam, The Netherlands\u003cbr\u003e'… it is well organised and well written … the authors are to be congratulated on this sure-footed addition to the econometrics literature.' The Times Higher Education Supplement\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. Introduction; 2. Causal and non-causal models; 3. Microeconomic data structures; 4. Linear models; 5. ML and NLS estimation; 6. GMM and systems estimation; 7. Hypothesis tests; 8. Specification tests and model selection; 9. Semiparametric methods; 10. Numerical optimization; 11. Bootstrap methods; 12. Simulation-based methods; 13. Bayesian methods; 14. Binary outcome models; 15. Multinomial models; 16. Tobit and selection models; 17. Transition data: survival analysis; 18. Mixture models and unobserved heterogeneity; 19. Models of multiple hazards; 20. Models of count data; 21. Linear panel models: basics; 22. Linear panel models: extensions; 23. Nonlinear panel models; 24. Stratified and clustered samples; 25. Treatment evaluation; 26. Measurement error models; 27. Missing data and imputation; A. Asymptotic theory; B. Making pseudo-random draw.","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":48864976109911,"sku":"9780521848053","price":64.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780521848053.jpg?v=1722273385"},{"product_id":"weapons-of-math-destruction-9780553418835","title":"Weapons of Math Destruction","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e\u003ci\u003eLonglisted for the National Book Award\u003cbr\u003e\u003c\/i\u003eNew York Times \u003ci\u003eBestseller\u003c\/i\u003e\u003c\/b\u003e\u003cp\u003e\u003c\/p\u003eA former...","brand":"Random House USA Inc","offers":[{"title":"Default Title","offer_id":48865066189143,"sku":"9780553418835","price":11.7,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780553418835.jpg?v=1722273489"},{"product_id":"the-data-detective-9780593084663","title":"The Data Detective","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Penguin Putnam Inc","offers":[{"title":"Default Title","offer_id":48865145061719,"sku":"9780593084663","price":16.65,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780593084663.jpg?v=1722273748"},{"product_id":"econometrics-9780691010182","title":"Econometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eIntroducing first year PhD students to standard graduate econometrics material, this work covers the standard material necessary for understanding the principal techniques of econometrics from ordinary least squares through cointegration. It is useful for those who intend to write a thesis on applied topics and also for the theoretically inclined.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Students of econometrics and their teachers will find this book to be the best introduction to the subject at the graduate and advanced undergraduate level. Starting with least squares regression, Hayashi provides an elegant exposition of all the standard topics of econometrics, including a detailed discussion of stationary and non-stationary time series. The particular strength of the book is the excellent balance between econometric theory and its applications, using GMM as an organizing principle throughout. Each chapter includes a detailed empirical example taken from classic and current applications of econometrics.\"\u003cb\u003e—Dale Jorgensen, Harvard University\u003c\/b\u003e\u003cbr\u003e\"\u003ci\u003eEconometrics\u003c\/i\u003e will be a very useful book for intermediate and advanced graduate courses. It covers the topics with an easy to understand approach while at the same time offering a rigorous analysis. The computer programming tips and problems should also be useful to students. I highly recommend this book for an up-to-date coverage and thoughtful discussion of topics in the methodology and application of econometrics.\"\u003cb\u003e—Jerry A. Hausman, Massachusetts Institute of Technology\u003c\/b\u003e\u003cbr\u003e\"\u003ci\u003eEconometrics\u003c\/i\u003e covers both modern and classic topics without shifting gears. The coverage is quite advanced yet the presentation is simple. Hayashi brings students to the frontier of applied econometric practice through a careful and efficient discussion of modern economic theory. The empirical exercises are very useful. . . . The projects are carefully crafted and have been thoroughly debugged.\"\u003cb\u003e—Mark W. Watson, Princeton University\u003c\/b\u003e\u003cbr\u003e\"\u003ci\u003eEconometrics\u003c\/i\u003e strikes a good balance between technical rigor and clear exposition. . . . The use of empirical examples is well done throughout. I very much like the use of old 'classic' examples. It gives students a sense of history—and shows that great empirical econometrics is a matter of having important ideas and good data, not just fancy new methods. . . . The style is just great, informal and engaging.\"\u003cb\u003e—James H. Stock, John F. Kennedy School of Government, Harvard University\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eList of Figures xvii  Preface xix  1 Finite-Sample Properties of OLS 3  1.1 The Classical Linear Regression Model 3  The Linearity Assumption 4  Matrix Notation 6  The Strict Exogeneity Assumption 7  Implications of Strict Exogeneity 8  Strict Exogeneity in Time-Series Models 9  Other Assumptions of the Model 10  The Classical Regression Model for Random Samples 12  \"Fixed\" Regressors 13  1.2 The Algebra of Least Squares 15  OLS Minimizes the Sum of Squared Residuals 15  Normal Equations 16  Two Expressions for the OLS Estimator 18  More Concepts and Algebra 18  Influential Analysis (optional) 21  A Note on the Computation of OLS Estimates 23  1.3 Finite-Sample Properties of OLS 27  Finite-Sample Distribution of b 27  Finite-Sample Properties of s2 30  Estimate of Var(b | X) 31  1.4 Hypothesis Testing under Normality 33  Normally Distributed Error Terms 33  Testing Hypotheses about Individual Regression Coefficients 35  Decision Rule for the t-Test 37  Confidence Interval 38  p-Value 38  Linear Hypotheses 39  The F-Test 40  A More Convenient Expression for F 42  t versus F 43  An Example of a Test Statistic Whose Distribution Depends on X 45  1.5 Relation to Maximum Likelihood 47  The Maximum Likelihood Principle 47  Conditional versus Unconditional Likelihood 47  The Log Likelihood for the Regression Model 48  ML via Concentrated Likelihood 48  Cramer-Rao Bound for the Classical Regression Model 49  The F-Test as a Likelihood Ratio Test 52  Quasi-Maximum Likelihood 53  1.6 Generalized Least Squares (GLS) 54  Consequence of Relaxing Assumption 1.4 55  Efficient Estimation with Known V 55  A Special Case: Weighted Least Squares (WLS) 58  Limiting Nature of GLS 58  1.7 Application: Returns to Scale in Electricity Supply 60  The Electricity Supply Industry 60  The Data 60  Why Do We Need Econometrics? 61  The Cobb-Douglas Technology 62  How Do We Know Things Are Cobb-Douglas? 63  Are the OLS Assumptions Satisfied? 64  Restricted Least Squares 65  Testing the Homogeneity of the Cost Function 65  Detour: A Cautionary Note on R2 67  Testing Constant Returns to Scale 67  Importance of Plotting Residuals 68  Subsequent Developments 68  Problem Set 71  Answers to Selected Questions 84  2 Large-Sample Theory 88  2.1 Review of Limit Theorems for Sequences of Random Variables 88  Various Modes of Convergence 89  Three Useful Results 92  Viewing Estimators as Sequences of Random Variables 94  Laws of Large Numbers and Central Limit Theorems 95  2.2 Fundamental Concepts in Time-Series Analysis 97  Need for Ergodic Stationarity 97  Various Classes of Stochastic Processes 98  Different Formulation of Lack of Serial Dependence 106  The CLT for Ergodic Stationary Martingale Differences Sequences 106  2.3 Large-Sample Distribution of the OLS Estimator 109  The Model 109  Asymptotic Distribution of the OLS Estimator 113  s2 Is Consistent 115  2.4 Hypothesis Testing 117  Testing Linear Hypotheses 117  The Test Is Consistent 119  Asymptotic Power 120  Testing Nonlinear Hypotheses 121  2.5 Estimating E([not displayable]) Consistently 123  Using Residuals for the Errors 123  Data Matrix Representation of S 125  Finite-Sample Considerations 125  2.6 Implications of Conditional Homoskedasticity 126  Conditional versus Unconditional Homoskedasticity 126  Reduction to Finite-Sample Formulas 127  Large-Sample Distribution of t and F Statistics 128  Variations of Asymptotic Tests under Conditional Homoskedasticity 129  2.7 Testing Conditional Homoskedasticity 131  2.8 Estimation with Parameterized Conditional Heteroskedasticity (optional) 133  The Functional Form 133  WLS with Known [alpha] 134  Regression of e2i on zi Provides a Consistent Estimate of [alpha] 135  WLS with Estimated [alpha] 136  OLS versus WLS 137  2.9 Least Squares Projection 137  Optimally Predicting the Value of the Dependent Variable 138  Best Linear Predictor 139  OLS Consistently Estimates the Projection Coefficients 140  2.10 Testing for Serial Correlation 141  Box-Pierce and Ljung-Box 142  Sample Autocorrelations Calculated from Residuals 144  Testing with Predetermined, but Not Strictly Exogenous, Regressors 146  An Auxiliary Regression-Based Test 147  2.11 Application: Rational Expectations Econometrics 150  The Efficient Market Hypotheses 150  Testable Implications 152  Testing for Serial Correlation 153  Is the Nominal Interest Rate the Optimal Predictor? 156  Rt Is Not Strictly Exogenous 158  Subsequent Developments 159  2.12 Time Regressions 160  The Asymptotic Distribution of the OLS Estimates 161  Hypothesis Testing for Time Regressions 163  2.A Asymptotics with Fixed Regressors 164  2.B Proof of Proposition 2.10 165  Problem Set 168  Answers to Selected Questions 183  3 Single-Equation GMM 186  3.1 Endogeneity Bias: Working's Example 187  A Simultaneous Equations Model of Market Equilibrium 187  Endogeneity Bias 188  Observable Supply Shifters 189  3.2 More Examples 193  A Simple Macroeconometric Model 193  Errors-in-Variables 194  Production Function 196  3.3 The General Formulation 198  Regressors and Instruments 198  Identification 200  Order Condition for Identification 202  The Assumption for Asymptotic Normality 202  3.4 Generalized Method of Moments Defined 204  Method of Moments 205  Generalized Method of Moments 206  Sampling Error 207  3.5 Large-Sample Properties of GMM 208  Asymptotic Distribution of the GMM Estimator 209  Estimation of Error Variance 210  Hypothesis Testing 211  Estimation of S 212  Efficient GMM Estimator 212  Asymptotic Power 214  Small-Sample Properties 215  3.6 Testing Overidentifying Restrictions 217  Testing Subsets of Orthogonality Conditions 218  3.7 Hypothesis Testing by the Likelihood-Ratio Principle 222  The LR Statistic for the Regression Model 223  Variable Addition Test (optional) 224  3.8 Implications of Conditional Homoskedasticity 225  Efficient GMM Becomes 2SLS 226  J Becomes Sargan's Statistic 227  Small-Sample Properties of 2SLS 229  Alternative Derivations of 2SLS 229  When Regressors Are Predetermined 231  Testing a Subset of Orthogonality Conditions 232  Testing Conditional Homoskedasticity 234  Testing for Serial Correlation 234  3.9 Application: Returns from Schooling 236  The NLS-Y Data 236  The Semi-Log Wage Equation 237  Omitted Variable Bias 238  IQ as the Measure of Ability 239  Errors-in-Variables 239  2SLS to Correct for the Bias 242  Subsequent Developments 243  Problem Set 244  Answers to Selected Questions 254  4 Multiple-Equation GMM 258  4.1 The Multiple-Equation Model 259  Linearity 259  Stationarity and Ergodicity 260  Orthogonality Conditions 261  Identification 262  The Assumption for Asymptotic Normality 264  Connection to the \"Complete\" System of Simultaneous Equations 265  4.2 Multiple-Equation GMM Defined 265  4.3 Large-Sample Theory 268  4.4 Single-Equation versus Multiple-Equation Estimation 271  When Are They \"Equivalent\"? 272  Joint Estimation Can Be Hazardous 273  4.5 Special Cases of Multiple-Equation GMM: FIVE, 3SLS, and SUR 274  Conditional Homoskedasticity 274  Full-Information Instrumental Variables Efficient (FIVE) 275  Three-Stage Least Squares (3SLS) 276  Seemingly Unrelated Regressions (SUR) 279  SUR versus OLS 281  4.6 Common Coefficients 286  The Model with Common Coefficients 286  The GMM Estimator 287  Imposing Conditional Homoskedasticity 288  Pooled OLS 290  Beautifying the Formulas 292  The Restriction That Isn't 293  4.7 Application: Interrelated Factor Demands 296  The Translog Cost Function 296  Factor Shares 297  Substitution Elasticities 298  Properties of Cost Functions 299  Stochastic Specifications 300  The Nature of Restrictions 301  Multivariate Regression Subject to Cross-Equation Restrictions 302  Which Equation to Delete? 304  Results 305  Problem Set 308  Answers to Selected Questions 320  5 Panel Data 323  5.1 The Error-Components Model 324  Error Components 324  Group Means 327  A Reparameterization 327  5.2 The Fixed-Effects Estimator 330  The Formula 330  Large-Sample Properties 331  Digression: When [eta]i Is Spherical 333  Random Effects versus Fixed Effects 334  Relaxing Conditional Homoskedasticity 335  5.3 Unbalanced Panels (optional) 337  \"Zeroing Out\" Missing Observations 338  Zeroing Out versus Compression 339  No Selectivity Bias 340  5.4 Application: International Differences in Growth Rates 342  Derivation of the Estimation Equation 342  Appending the Error Term 343  Treatment of [alpha]i 344  Consistent Estimation of Speed of Convergence 345  Appendix 5.A: Distribution of Hausman Statistic 346  Problem Set 349  Answers to Selected Questions 363  6 Serial Correlation 365  6.1 Modeling Serial Correlation: Linear Processes 365  MA(q) 366  MA([infinity]) as a Mean Square Limit 366  Filters 369  Inverting Lag Polynomials 372  6.2 ARMA Processes 375  AR(1) and Its MA([infinity]) Representation 376  Autocovariances of AR(1) 378  AR(p) and Its MA([infinity]) Representation 378  ARMA(p,q) 380  ARMA(p) with Common Roots 382  Invertibility 383  Autocovariance-Generating Function and the Spectrum 383  6.3 Vector Processes 387  6.4 Estimating Autoregressions 392  Estimation of AR(1) 392  Estimation of AR(p) 393  Choice of Lag Length 394  Estimation of VARs 397  Estimation of ARMA(p,q) 398  6.5 Asymptotics for Sample Means of Serially Correlated Processes 400  LLN for Covariance-Stationary Processes 401  Two Central Limit Theorems 402  Multivariate Extension 404  6.6 Incorporating Serial Correlation in GMM 406  The Model and Asymptotic Results 406  Estimating S When Autocovariances Vanish after Finite Lags 407  Using Kernels to Estimate S 408  VARHAC 410  6.7 Estimation under Conditional Homoskedasticity (Optional) 413  Kernel-Based Estimation of S under Conditional Homoskedasticity 413  Data Matrix Representation of Estimated Long-Run Variance 414  Relation to GLS 415  6.8 Application: Forward Exchange Rates as Optimal Predictors 418  The Market Efficiency Hypothesis 419  Testing Whether the Unconditional Mean Is Zero 420  Regression Tests 423  Problem Set 428  Answers to Selected Questions 441  7 Extremum Estimators 445  7.1 Extremum Estimators 446  \"Measurability\" of [theta] 446  Two Classes of Extremum Estimators 447  Maximum Likelihood (ML) 448  Conditional Maximum Likelihood 450  Invariance of ML 452  Nonlinear Least Squares (NLS) 453  Linear and Nonlinear GMM 454  7.2 Consistency 456  Two Consistency Theorems for Extremum Estimators 456  Consistency of M-Estimators 458  Concavity after Reparameterization 461  Identification in NLS and ML 462  Consistency of GMM 467  7.3 Asymptotic Normality 469  Asymptotic Normality of M-Estimators 470  Consistent Asymptotic Variance Estimation 473  Asymptotic Normality of Conditional ML 474  Two Examples 476  Asymptotic Normality of GMM 478  GMM versus ML 481  Expressing the Sampling Error in a Common Format 483  7.4 Hypothesis Testing 487  The Null Hypothesis 487  The Working Assumptions 489  The Wald Statistic 489  The Lagrange Multiplier (LM) Statistic 491  The Likelihood Ratio (LR) Statistic 493  Summary of the Trinity 494  7.5 Numerical Optimization 497  Newton-Raphson 497  Gauss-Newton 498  Writing Newton-Raphson and Gauss-Newton in a Common Format 498  Equations Nonlinear in Parameters Only 499  Problem Set 501  Answers to Selected Questions 505  8 Examples of Maximum Likelihood 507  8.1 Qualitative Response (QR) Models 507  Score and Hessian for Observation t 508  Consistency 509  Asymptotic Normality 510  8.2 Truncated Regression Models 511  The Model 511  Truncated Distributions 512  The Likelihood Function 513  Reparameterizing the Likelihood Function 514  Verifying Consistency and Asymptotic Normality 515  Recovering Original Parameters 517  8.3 Censored Regression (Tobit) Models 518  Tobit Likelihood Function 518  Reparameterization 519  8.4 Multivariate Regressions 521  The Multivariate Regression Model Restated 522  The Likelihood Function 523  Maximizing the Likelihood Function 524  Consistency and Asymptotic Normality 525  8.5 FIML 526  The Multiple-Equation Model with Common Instruments Restated 526  The Complete System of Simultaneous Equations 529  Relationship between ([Gamma]0, [Beta]0) and [delta]0 530  The FIML Likelihood Function 531  The FIML Concentrated Likelihood Function 532  Testing Overidentifying Restrictions 533  Properties of the FIML Estimator 533  ML Estimation of the SUR Model 535  8.6 LIML 538  LIML Defined 538  Computation of LIML 540  LIML versus 2SLS 542  8.7 Serially Correlated Observations 543  Two Questions 543  Unconditional ML for Dependent Observations 545  ML Estimation of AR.1\/ Processes 546  Conditional ML Estimation of AR(1) Processes 547  Conditional ML Estimation of AR(p) and VAR(p) Processes 549  Problem Set 551  9 Unit-Root Econometrics 557  9.1 Modeling Trends 557  Integrated Processes 558  Why Is It Important to Know if the Process Is I(1)? 560  Which Should Be Taken as the Null, I(0) or I(1)? 562  Other Approaches to Modeling Trends 563  9.2 Tools for Unit-Root Econometrics 563  Linear I(0) Processes 563  Approximating I(1) by a Random Walk 564  Relation to ARMA Models 566  The Wiener Process 567  A Useful Lemma 570  9.3 Dickey-Fuller Tests 573  The AR(1) Model 573  Deriving the Limiting Distribution under the I(1) Null 574  Incorporating the Intercept 577  Incorporating Time Trend 581  9.4 Augmented Dickey-Fuller Tests 585  The Augmented Autoregression 585  Limiting Distribution of the OLS Estimator 586  Deriving Test Statistics 590  Testing Hypotheses about [zeta] 591  What to Do When p Is Unknown? 592  A Suggestion for the Choice of pmax(T) 594  Including the Intercept in the Regression 595  Incorporating Time Trend 597  Summary of the DF and ADF Tests and Other Unit-Root Tests 599  9.5 Which Unit-Root Test to Use? 601  Local-to-Unity Asymptotics 602  Small-Sample Properties 602  9.6 Application: Purchasing Power Parity 603  The Embarrassing Resiliency of the Random Walk Model? 604  Problem Set 605  Answers to Selected Questions 619  10 Cointegration 623  10.1 Cointegrated Systems 624  Linear Vector I(0) and I(1) Processes 624  The Beveridge-Nelson Decomposition 627  Cointegration Defined 629  10.2 Alternative Representations of Cointegrated Systems 633  Phillips's Triangular Representation 633  VAR and Cointegration 636  The Vector Error-Correction Model (VECM) 638  Johansen's ML Procedure 640  10.3 Testing the Null of No Cointegration 643  Spurious Regressions 643  The Residual-Based Test for Cointegration 644  Testing the Null of Cointegration 649  10.4 Inference on Cointegrating Vectors 650  The SOLS Estimator 650  The Bivariate Example 652  Continuing with the Bivariate Example 653  Allowing for Serial Correlation 654  General Case 657  Other Estimators and Finite-Sample Properties 658  10.5 Application: the Demand for Money in the United States 659  The Data 660  (m - p, y, R) as a Cointegrated System 660  DOLS 662  Unstable Money Demand? 663  Problem Set 665  Appendix. Partitioned Matrices and Kronecker Products 670  Addition and Multiplication of Partitioned Matrices 671  Inverting Partitioned Matrices 672","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48865513898327,"sku":"9780691010182","price":49.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691010182.jpg?v=1722274334"},{"product_id":"the-econometrics-of-financial-markets-9780691043012","title":"The Econometrics of Financial Markets","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eCovers the spectrum of empirical finance, including the predictability of asset returns, tests of the Random Walk Hypothesis, the microstructure of securities markets, event analysis, the Capital Asset Pricing Model and the Arbitrage Pricing Theory, and the term structure of interest rates, dynamic models of economic equilibrium.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eWinner of the 2014 Eugene Fama Prize for Outstanding Contributions to Doctoral Education, University of Chicago Booth School of Business Winner of the 1997 Award for Best Professional\/Scholarly Book in Economics, Association of American Publishers Winner of the 1997 Paul A. Samuelson Award, TIAA-CREF \"The definitive work explaining this complex but important field of academic endeavor. Oh, and by the way, it's not just academic. The big question that financial econometircs addresses is: What can you learn about the future from the financial data available from the past? This broad issue can be specified in many different ways, and all the important ones are discussed in the book... The vast literature on all the topics examined is assessed, rendered coherent, and then analysed by three men who themselves have made significant advances in the field.\"--Ruben Lee, London Financial Market \"This book is sophisticated, yet accessible; full of details, yet intriguing... Instructors will appreciate the attempt to make each chapter as self contained as possible which leaves them free to choose specified sequences of topics. Professionals will be pleased with the quick and authoritative introductions to important areas of Finance... [A] well written introduction (indeed, something more) to Financial Econometrics. It is alert, explicit and articulate about assumptions... a splendid offering... \"--Maurizio Tiso, Review of Financial Studies \"Written by the \"A\" team of financial empiricism, it is a long awaited book. It covers many topics one could only usually find couched in the technical jargon of research papers, presented in this volume with pedagogical intentions. The language, while remaining technical, is quite accessible. It can be effortlessly read by scientific traders with standard knowledge of statistical methods... This book should be made mandatory reading in research departments.\"--Derivative Strategies\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eList of Figures xiii List of Tables xv Preface xix 1Introduction 3 1.1 Organization of the Book 4 1.2 Useful Background 6 1.2.1 Mathematics Background 6 1.2.2 Probability and Statistics Background 6 1.2.3 Finance Theory Background 7 1.3 Notation 8 1.4 Prices, Returns, and Compounding 9 1.4.1 Definitions and Conventions 9 1.4.2 The Marginal, Conditional, and Joint Distribution of Returns 13 1.5 Market Efficiency 20 1.5.1 Efficient Markets and the Law of Iterated Expectations 22 1.5.2 Is Market Efficiency Testable? 24 2The Predictability of Asset Returns 27 2.1 The Random Walk Hypotheses 28 2.1.1 The Random Walk 1: IID Increments 31 2.1.2 The Random Walk 2: Independent Increments 32 2.1.3 The Random Walk 3: Uncorrelated Increments 33 2.2 Tests of Random Walk 1: IID Increments 33 2.2.1 Traditional Statistical Tests 33 2.2.2 Sequences and Reversals, and Runs 34 2.3 Tests of Random Walk 2: Independent Increments 41 2.3.1 Filter Rules 42 2.3.2 Technical Analysis 43 2.4 Tests of Random Walk 3: Uncorrelated Increments 44 2.4.1 Autocorrelation Coefficients 44 2.4.2 Portmanteau Statistics 47 2.4.3 Variance Ratios 48 2.5 Long-Horizon Returns 55 2.5.1 Problems with Long-Horizon Inferences 57 2.6 Tests For Long-Range Dependence 59 2.6.1 Examples of Long-Range Dependence 59 2.6.2 The Hurst-Mandelbrot Rescaled Range Statistic 62 2.7 Unit Root Tests 64 2.8 Recent Empirical Evidence 65 2.8.1 Autocorrelations 66 2.8.2 Variance Ratios 68 2.8.3 Cross-Autocorrelations and Lead-Lag Relations 74 2.8.4 Tests Using Long-Horizon Returns 78 2.9 Conclusion 80 3Market Microstructure 83 3.1 Nonsynchronous Trading 84 3.1.1 A Model of Nonsynchronous Trading 85 3.1.2 Extensions and Generalizations 98 3.2 The Bid-Ask Spread 99 3.2.1 Bid-Ask Bounce 101 3.2.2 Components of the Bid-Ask Spread 103 3.3 Modeling Transactions Data 107 3.3.1 Motivation 108 3.3.2 Rounding and Barrier Models 114 3.3.3 The Ordered Probit Model 122 3.4 Recent Empirical Findings 128 3.4.1 Nonsynchronous Trading 128 3.4.2 Estimating the Effective Bid-Ask Spread 134 3.4.3 Transactions Data 136 3.5 Conclusion 144 4Event-Study Analysis 149 4.1 Outline of an Event Study 150 4.2 An Example of an Event Study 152 4.3 Models for Measuring Normal Performance 153 4.3.1 Constant-Mean-Return Model 154 4.3.2 Market Model 155 4.3.3 Other Statistical Models 155 4.3.4 Economic Models 156 4.4 Measuring and Analyzing Abnormal Returns 157 4.4.1 Estimation of the Market Model 158 4.4.2 Statistical Properties of Abnormal Returns 159 4.4.3 Aggregation of Abnormal Returns 160 4.4.4 Sensitivity to Normal Return Model 162 4.4.5 CARs for the Earnings-Announcement Example 163 4.4.6 Inferences with Clustering 166 4.5 Modifying the Null Hypothesis 167 4.6 Analysis of Power 168 4.7 Nonparametric Tests 172 4.8 Cross-Sectional Models 173 4.9 Further Issues 175 4.9.1 Role of the Sampling Interval 175 4.9.2 Inferences with Event-Date Uncertainty 176 4.9.3 Possible Biases 177 4.10 Conclusion 178 5The Capital Asset Pricing Model 181 5.1 Review of the CAPM 181 5.2 Results from Efficient-Set Mathematics 184 5.3 Statistical Framework for Estimation and Testing 188 5.3.1 Sharpe-Lintner Version 189 5.3.2 Black Version 196 5.4 Size of Tests 203 5.5 Power of Tests 204 5.6 Nonnormal and Non-IID Returns 208 5.7 Implementation of Tests 211 5.7.1 Summary of Empirical Evidence 211 5.7.2 Illustrative Implementation 212 5.7.3 Unobservability of the Market Portfolio 213 5.8 Cross-Sectional Regressions 215 5.9 Conclusion 217 6Multifactor Pricing Models 219 6.1 Theoretical Background 219 6.2 Estimation and Testing 222 6.2.1 Portfolios as Factors with a Riskfree Asset 223 6.2.2 Portfolios as Factors without a Riskfree Asset 224 6.2.3 Macroeconomic Variables as Factors 226 6.2.4 Factor Portfolios Spanning the Mean-Variance\\protect\\\\ Frontier 228 6.3 Estimation of Risk Premia and Expected Returns 231 6.4 Selection of Factors 233 6.4.1 Statistical Approaches 233 6.4.2 Number of Factors 238 6.4.3 Theoretical Approaches 239 6.5 Empirical Results 240 6.6 Interpreting Deviations from Exact Factor Pricing 242 6.6.1 Exact Factor Pricing Models, Mean-Variance Analysis, and the Optimal Orthogonal Portfolio 243 6.6.2 Squared Sharpe Ratios 245 6.6.3 Implications for Separating Alternative Theories 246 6.7 Conclusion 251 7Present-Value Relations 253 7.1 The Relation between Prices, Dividends, and Returns 254 7.1.1 The Linear Present-Value Relation with Constant Expected Returns 255 7.1.2 Rational Bubbles 258 7.1.3 An Approximate Present-Value Relation with Time-Varying Expected Returns 260 7.1.4 Prices and Returns in a Simple Example 264 7.2 Present-Value Relations and US Stock Price Behavior 267 7.2.1 Long-Horizon Regressions 267 7.2.2 Volatility Tests 275 7.2.3 Vector Autoregressive Methods 279 7.3 Conclusion 286 8Intertemporal Equilibrium Models 291 8.1 The Stochastic Discount Factor 293 8.1.1 Volatility Bounds 296 8.2 Consumption-Based Asset Pricing with Power Utility 304 8.2.1 Power Utility in a Lognormal Model 306 8.2.2 Power Utility and Generalized Method of\\protect\\\\ Moments 314 8.3 Market Frictions 314 8.3.1 Market Frictions and Hansen-Jagannathan\\protect\\\\ Bounds 315 8.3.2 Market Frictions and Aggregate Consumption\\protect\\\\ Data 316 8.4 More General Utility Functions 326 8.4.1 Habit Formation 326 8.4.2 Psychological Models of Preferences 332 8.5 Conclusion 334 9Derivative Pricing Models 339 9.1 Brownian Motion 341 9.1.1 Constructing Brownian Motion 341 9.1.2 Stochastic Differential Equations 346 9.2 A Brief Review of Derivative Pricing Methods 349 9.2.1 The Black-Scholes and Merton Approach 350 9.2.2 The Martingale Approach 354 9.3 Implementing Parametric Option Pricing Models 355 9.3.1 Parameter Estimation of Asset Price Dynamics 356 9.3.2 Estimating $\\sigma $ in the Black-Scholes Model 361 9.3.3 Quantifying the Precision of Option Price Estimators 367 9.3.4 The Effects of Asset Return Predictability 369 9.3.5 Implied Volatility Estimators 377 9.3.6 Stochastic Volatility Models 379 9.4 Pricing Path-Dependent Derivatives Via Monte Carlo Simulation 382 9.4.1 Discrete Versus Continuous Time 383 9.4.2 How Many Simulations to Perform 384 9.4.3 Comparisons with a Closed-Form Solution 384 9.4.4 Computational Efficiency 386 9.4.5 Extensions and Limitations 390 9.5 Conclusion 391 10Fixed-Income Securities 395 10.1 Basic Concepts 396 10.1.1 Discount Bonds 397 10.1.2 Coupon Bonds 401 10.1.3 Estimating the Zero-Coupon Term Structure 409 10.2 Interpreting the Term Structure of Interest Rates 413 10.2.1 The Expectations Hypothesis 413 10.2.2 Yield Spreads and Interest Rate Forecasts 418 10.3 Conclusion 423 11Term-Structure Models 427 11.1 Affine-Yield Models 428 11.1.1 A Homoskedastic Single-Factor Model 429 11.1.2 A Square-Root Single-Factor Model 435 11.1.3 A Two-Factor Model 438 11.1.4 Beyond Affine-Yield Models 441 11.2 Fitting Term-Structure Models to the Data 442 11.2.1 Real Bonds, Nominal Bonds, and Inflation 442 11.2.2 Empirical Evidence on Affine-Yield Models 445 11.3 Pricing Fixed-Income Derivative Securities 455 11.3.1 Fitting the Current Term Structure Exactly 456 11.3.2 Forwards and Futures 458 11.3.3 Option Pricing in a Term-Structure Model 461 11.4 Conclusion 464 12Nonlinearities in Financial Data 467 12.1 Nonlinear Structure in Univariate Time Series 468 12.1.1 Some Parametric Models 470 12.1.2 Univariate Tests for Nonlinear Structure 475 12.2 Models of Changing Volatility 479 12.2.1 Univariate Models 481 12.2.2 Multivariate Models 490 12.2.3 Links between First and Second Moments 494 12.3 Nonparametric Estimation 498 12.3.1 Kernel Regression 500 12.3.2 Optimal Bandwidth Selection 502 12.3.3 Average Derivative Estimators 504 12.3.4 Application: Estimating State-Price Densities 507 12.4 Artificial Neural Networks 512 12.4.1 Multilayer Perceptrons 512 12.4.2 Radial Basis Functions 516 12.4.3 Projection Pursuit Regression 518 12.4.4 Limitations of Learning Networks 518 12.4.5 Application: Learning the Black-Scholes Formula 519 12.5 Overfitting and Data-Snooping 523 12.6 Conclusion 524 Appendix 527 A.1 Linear Instrumental Variables 527 A.2 Generalized Method of Moments 532 A.3 Serially Correlated and Heteroskedastic Errors 534 A.4 GMM and Maximum Likelihood 536 References 541 Author Index 587 Subject Index 597","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48865515995479,"sku":"9780691043012","price":58.5,"currency_code":"GBP","in_stock":true}]},{"product_id":"time-series-analysis-9780691042893","title":"Time Series Analysis","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eA graduate-level text which describes the recent dramatic changes that have taken place in the way that researchers analyze economic and financial time series. It explores such important innovations as vector regression, nonlinear time series models and the generalized methods of moments.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"A carefully prepared and well written book... Without doubt, it can be recommended as a very valuable encyclopedia and textbook for a reader who is looking for a mainly theoretical textbook which combines traditional time series analysis with a review of recent research areas.\"--Journal of Economics\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface1Difference Equations12Lag Operators253Stationary ARMA Processes434Forecasting725Maximum Likelihood Estimation1176Spectral Analysis1527Asymptotic Distribution Theory1808Linear Regression Models2009Linear Systems of Simultaneous Equations23310Covariance-Stationary Vector Processes25711Vector Autoregressions29112Bayesian Analysis35113The Kalman Filter37214Generalized Method of Moments40915Models of Nonstationary Time Series43516Processes with Deterministic Time Trends45417Univariate Processes with Unit Roots47518Unit Roots in Multivariate Time Series54419Cointegration57120Full-Information Maximum Likelihood Analysis of Cointegrated Systems63021Time Series Models of Heteroskedasticity65722Modeling Time Series with Changes in Regime677A Mathematical Review704B Statistical Tables751C Answers to Selected Exercises769D Greek Letters and Mathematical Symbols Used in the Text786Author Index789Subject Index792","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48865516028247,"sku":"9780691042893","price":58.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691042893.jpg?v=1722274342"},{"product_id":"mostly-harmless-econometrics-9780691120355","title":"Mostly Harmless Econometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eShows how the basic tools of applied econometrics allow the data to speak. This book covers regression-discontinuity designs and quantile regression - as well as how to get standard errors right. It is suitable for various areas in contemporary social science.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"A quirky and thought-provoking read for any budding econometrician... Insightful and refreshing.\"--James Davidson, Times Higher Education \"I'd recommend it to the entire range of empirical economists, from those still in training to those who, like me, have only a hazy memory of statistical theory and stick to our tried and tested methods of estimation ... an excellent guide to how to do basic regression\/IV\/panel data estimation really well. In particular, it demonstrates through many examples how to bring about a happy marriage between one's underlying model and the data which might or might not confirm the researcher's hypotheses.\"--Diane Coyle, The Enlightened Economist Blog \"The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social sciences.\"--Pavel Stoynov, Zentralblatt MATH \"[T]he matter covered in the book is surely of interest to most agricultural economists. Even if it is not a complete overview of existing econometric research methods, it certainly contains a good deal of hands on advice driven by years of experience.\"--European Review of Agricultural Economics \"This book is an extremely thought-provoking contribution to the literature. It champions a different paradigm to that characterising most econometrics texts and does so with considerable (idiosyncratic) style and grace. Highly recommended!\"--David Harris and Christopher L. Skeels, Economic Record\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eList of Figures vii  List of Tables ix  Preface xi  Acknowledgments xv  Organization of This Book xvii      PART I: PRELIMINARIES 1  Chapter 1: Questions about Questions 3  Chapter 2: The Experimental Ideal 11  2.1 The Selection Problem 12  2.2 Random Assignment Solves the Selection Problem 15  2.3 Regression Analysis of Experiments 22      PART II: THE CORE 25  Chapter 3: Making Regression Make Sense 27  3.1 Regression Fundamentals 28  3.2 Regression and Causality 51  3.3 Heterogeneity and Nonlinearity 68  3.4 Regression Details 91  3.5 Appendix: Derivation of the Average Derivative Weighting Function 110      Chapter 4: Instrumental Variables in Action: Sometimes You Get What You Need 113  4.1 IV and Causality 115  4.2 Asymptotic 2SLS Inference 138  4.3 Two-Sample IV and Split-Sample IV 147  4.4 IV with Heterogeneous Potential Outcomes 150  4.5 Generalizing LATE 173  4.6 IV Details 188  4.7 Appendix 216      Chapter 5: Parallel Worlds: Fixed Effects, Differences-in-Differences, and Panel Data 221  5.1 Individual Fixed Effects 221  5.2 Differences-in-Differences 227  5.3 Fixed Effects versus Lagged Dependent Variables 243  5.4 Appendix: More on Fixed Effects and Lagged Dependent Variables 246      PART III: EXTENSIONS 249  Chapter 6: Getting a Little Jumpy: Regression Discontinuity Designs 251  6.1 Sharp RD 251  6.2 Fuzzy RD Is IV 259      Chapter 7: Quantile Regression 269  7.1 The Quantile Regression Model 270  7.2 IV Estimation of Quantile Treatment Effects 283      Chapter 8: Nonstandard Standard Error Issues 293  8.1 The Bias of Robust Standard Error Estimates 294  8.2 Clustering and Serial Correlation in Panels 308  8.3 Appendix: Derivation of the Simple Moulton Factor 323      Last Words 327  Acronyms and Abbreviations 329  Empirical Studies Index 335  References 339  Index 361","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48865518158167,"sku":"9780691120355","price":38.25,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691120355.jpg?v=1722274354"},{"product_id":"asset-price-dynamics-volatility-and-prediction-9780691134796","title":"Asset Price Dynamics Volatility and Prediction","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eMoving beyond purely theoretical models, the author applies methods supported by empirical research of equity and foreign exchange markets to show how daily and more frequent asset prices, and the prices of option contracts, can be used to construct and assess predictions about future prices, their volatility, and their probability distributions.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003eWinner of the 2005 BestBook Award, Riskbook.com \"This book provides thorough, well-presented and concise coverage of asset price dynamics and manages to combine new developments, established issues, theory and application in a practical and refreshing manner. It is well illustrated with time series graphs and tables and has a good balance between theoretical concepts and their practical applications with a mathematical treatment that is not too specialized.\"--Anthony F. Gyles, RSS\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface xiii      Chapter 1: Introduction 1  1.1 Asset Price Dynamics 1  1.2 Volatility 1  1.3 Prediction 2  1.4 Information 2  1.5 Contents 3  1.6 Software 5  1.7 Web Resources 6      PART I: Foundations 7      Chapter 2: Prices and Returns 9  2.1 Introduction 9  2.2 Two Examples of Price Series 9  2.3 Data-Collection Issues 10  2.4 Two Returns Series 13  2.5 Definitions of Returns 14  2.6 Further Examples of Time Series of Returns 19      Chapter 3: Stochastic Processes: Definitions and Examples 23  3.1 Introduction 23  3.2 Random Variables 24  3.3 Stationary Stochastic Processes 30  3.4 Uncorrelated Processes 33  3.5 ARMA Processes 36  3.6 Examples of ARMA 1 1 Specifications 44  3.7 ARIMA Processes 46  3.8 ARFIMA Processes 46  3.9 Linear Stochastic Processes 48  3.10 Continuous-Time Stochastic Processes 49  3.11 Notation for Random Variables and Observations 50      Chapter 4: Stylized Facts for Financial Returns 51  4.1 Introduction 51  4.2 Summary Statistics 52  4.3 Average Returns and Risk Premia 53  4.4 Standard Deviations 57  4.5 Calendar Effects 59  4.6 Skewness and Kurtosis 68  4.7 The Shape of the Returns Distribution 69  4.8 Probability Distributions for Returns 73  4.9 Autocorrelations of Returns 76  4.10 Autocorrelations of Transformed Returns 82  4.11 Nonlinearity of the Returns Process 92  4.12 Concluding Remarks 93  4.13 Appendix: Autocorrelation Caused by Day-of-the-Week Effects 94  4.14 Appendix: Autocorrelations of a Squared Linear Process 95  PART II: Conditional Expected Returns 97  Chapter 5: The Variance-Ratio Test of the Random Walk Hypothesis 99  5.1 Introduction 99  5.2 The Random Walk Hypothesis 100  5.3 Variance-Ratio Tests 102  5.4 An Example of Variance-Ratio Calculations 105  5.5 Selected Test Results 107  5.6 Sample Autocorrelation Theory 112  5.7 Random Walk Tests Using Rescaled Returns 115  5.8 Summary 120      Chapter 6: Further Tests of the Random Walk Hypothesis 121  6.1 Introduction 121  6.2 Test Methodology 122  6.3 Further Autocorrelation Tests 126  6.4 Spectral Tests 130  6.5 The Runs Test 133  6.6 Rescaled Range Tests 135  6.7 The BDS Test 136  6.8 Test Results for the Random Walk Hypothesis 138  6.9 The Size and Power of Random Walk Tests 144  6.10 Sources of Minor Dependence in Returns 148  6.11 Concluding Remarks 151  6.12 Appendix: the Correlation between Test Values for Two Correlated Series 153  6.13 Appendix: Autocorrelation Induced by Rescaling Returns 154      Chapter 7: Trading Rules and Market Efficiency 157  7.1 Introduction 157  7.2 Four Trading Rules 158  7.3 Measures of Return Predictability 163  7.4 Evidence about Equity Return Predictability 166  7.5 Evidence about the Predictability of Currency and Other Returns 168  7.6 An Example of Calculations for the Moving-Average Rule 172  7.7 Efficient Markets: Methodological Issues 175  7.8 Breakeven Costs for Trading Rules Applied to Equities 176  7.9 Trading Rule Performance for Futures Contracts 179  7.10 The Efficiency of Currency Markets 181  7.11 Theoretical Trading Profits for Autocorrelated Return Processes 184  7.12 Concluding Remarks 186      PART III: Volatility Processes 187      Chapter 8: An Introduction to Volatility 189  8.1 Definitions of Volatility 189  8.2 Explanations of Changes in Volatility 191  8.3 Volatility and Information Arrivals 193  8.4 Volatility and the Stylized Facts for Returns 195  8.5 Concluding Remarks 196      Chapter 9: ARCH Models: Definitions and Examples 197  9.1 Introduction 197  9.2 ARCH(1) 198  9.3 GARCH 1 1 199  9.4 An Exchange Rate Example of the GARCH 1 1 Model 205  9.5 A General ARCH Framework 212  9.6 Nonnormal Conditional Distributions 217  9.7 Asymmetric Volatility Models 220  9.8 Equity Examples of Asymmetric Volatility Models 222  9.9 Summary 233      Chapter 10: ARCH Models: Selection and Likelihood Methods 235  10.1 Introduction 235  10.2 Asymmetric Volatility: Further Specifications and Evidence 235  10.3 Long Memory ARCH Models 242  10.4 Likelihood Methods 245  10.5 Results from Hypothesis Tests 251  10.6 Model Building 256  10.7 Further Volatility Specifications 261  10.8 Concluding Remarks 264  10.9 Appendix: Formulae for the Score Vector 265  Chapter 11: Stochastic Volatility Models 267  11.1 Introduction 267  11.2 Motivation and Definitions 268  11.3 Moments of Independent SV Processes 270  11.4 Markov Chain Models for Volatility 271  11.5 The Standard Stochastic Volatility Model 278  11.6 Parameter Estimation for the Standard SV Model 283  11.7 An Example of SV Model Estimation for Exchange Rates 288  11.8 Independent SV Models with Heavy Tails 291  11.9 Asymmetric Stochastic Volatility Models 293  11.10 Long Memory SV Models 297  11.11 Multivariate Stochastic Volatility Models 298  11.12 ARCH versus SV 299  11.13 Concluding Remarks 301  11.14 Appendix: Filtering Equations 301      PART IV: High-Frequency Methods 303      Chapter 12: High-Frequency Data and Models 305  12.1 Introduction 305  12.2 High-Frequency Prices 306  12.3 One Day of High-Frequency Price Data 309  12.4 Stylized Facts for Intraday Returns 310  12.5 Intraday Volatility Patterns 316  12.6 Discrete-Time Intraday Volatility Models 321  12.7 Trading Rules and Intraday Prices 325  12.8 Realized Volatility: Theoretical Results 327  12.9 Realized Volatility: Empirical Results 332  12.10 Price Discovery 342  12.11 Durations 343  12.12 Extreme Price Changes 344  12.13 Daily High and Low Prices 346  12.14 Concluding Remarks 348  12.15 Appendix: Formulae for the Variance of the Realized Volatility Estimator 349      PART V: Inferences from Option Prices 351      Chapter 13: Continuous-Time Stochastic Processes 353  13.1 Introduction 353  13.2 The Wiener Process 354  13.3 Diffusion Processes 355  13.4 Bivariate Diffusion Processes 359  13.5 Jump Processes 361  13.6 Jump-Diffusion Processes 363  13.7 Appendix: a Construction of the Wiener Process 366      Chapter 14: Option Pricing Formulae 369  14.1 Introduction 369  14.2 Definitions, Notation, and Assumptions 370  14.3 Black-Scholes and Related Formulae 372  14.4 Implied Volatility 378  14.5 Option Prices when Volatility Is Stochastic 383  14.6 Closed-Form Stochastic Volatility Option Prices 388  14.7 Option Prices for ARCH Processes 391  14.8 Summary 394  14.9 Appendix: Heston's Option Pricing Formula 395      Chapter 15: Forecasting Volatility 397  15.1 Introduction 397  15.2 Forecasting Methodology 398  15.3 Two Measures of Forecast Accuracy 401  15.4 Historical Volatility Forecasts 403  15.5 Forecasts from Implied Volatilities 407  15.6 ARCH Forecasts that Incorporate Implied Volatilities 410  15.7 High-Frequency Forecasting Results 414  15.8 Concluding Remarks 420      Chapter 16: Density Prediction for Asset Prices 423  16.1 Introduction 423  16.2 Simulated Real-World Densities 424  16.3 Risk-Neutral Density Concepts and Definitions 428  16.4 Estimation of Implied Risk-Neutral Densities 431  16.5 Parametric Risk-Neutral Densities 435  16.6 Risk-Neutral Densities from Implied Volatility Functions 446  16.7 Nonparametric RND Methods 448  16.8 Towards Recommendations 450  16.9 From Risk-Neutral to Real-World Densities 451  16.10 An Excel Spreadsheet for Density Estimation 458  16.11 Risk Aversion and Rational RNDs 461  16.12 Tail Density Estimates 464  16.13 Concluding Remarks 465      Symbols 467  References 473  Author Index 503  Subject Index 513","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48865520124247,"sku":"9780691134796","price":66.3,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691134796.jpg?v=1722274363"},{"product_id":"mastering-metrics-9780691152844","title":"Mastering Metrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eApplied econometrics, known to aficionados as 'metrics, is the original data science. 'Metrics encompasses the statistical methods economists use to untangle cause and effect in human affairs. Through accessible discussion and with a dose of kung fu-themed humor, Mastering 'Metrics presents the essential tools of econometric research and demonstrat\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"I would be hard pressed to name another econometrics book that can be read for enjoyment yet provides useful quantitative insights.\"--M.S.R., Financial Analysts Journal\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eList of Figures vii  List of Tables ix  Introduction xi  1 Randomized Trials 1  1.1 In Sickness and in Health (Insurance) 1  1.2 The Oregon Trail 24  Masters of 'Metrics: From Daniel to R. A. Fisher 30  Appendix: Mastering Inference 33  2 Regression 47  2.1 A Tale of Two Colleges 47  2.2 Make Me a Match, Run Me a Regression 55  2.3 Ceteris Paribus? 68  Masters of 'Metrics: Galton and Yule 79  Appendix: Regression Theory 82  3 Instrumental Variables 98  3.1 The Charter Conundrum 99  3.2 Abuse Busters 115  3.3 The Population Bomb 123  Masters of 'Metrics: The Remarkable Wrights 139  Appendix: IV Theory 142  4 Regression Discontinuity Designs 147  4.1 Birthdays and Funerals 148  4.2 The Elite Illusion 164  Masters of 'Metrics: Donald Campbell 175  5 Differences-in-Differences 178  5.1 A Mississippi Experiment 178  5.2 Drink, Drank, ... 191  Masters of 'Metrics: John Snow 204  Appendix: Standard Errors for Regression DD 205  6 The Wages of Schooling 209  6.1 Schooling, Experience, and Earnings 209  6.2 Twins Double the Fun 217  6.3 Econometricians Are Known by Their ... Instruments 223  6.4 Rustling Sheepskin in the Lone Star State 235  Appendix: Bias from Measurement Error 240  Abbreviations and Acronyms 245  Empirical Notes 249  Acknowledgments 269  Index 271","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48865526088023,"sku":"9780691152844","price":31.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691152844.jpg?v=1722274392"},{"product_id":"structural-macroeconometrics-9780691152875","title":"Structural Macroeconometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eProvides an overview and exploration of methodologies, models, and techniques used to analyze forces shaping national economies. This title presents a range of methods for characterizing and evaluating empirical implications, including calibration exercises, method-of-moment procedures, and likelihood-based procedures, both classical and Bayesian.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"Structural Macroeconometrics is the ideal textbook for graduate students seeking an introduction to macroeconomics and econometrics, and for advanced students pursuing applied research in macroeconomics. The book's historical perspective, along with its broad presentation of alternative methodologies, makes it an indispensable resource for academics and professionals.\"--World Book Industry\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePreface xiii Preface to the First Edition xv   Part I Introduction Chapter 1: Background and Overview 3 1.1 Background 3 1.2 Overview 4   Chapter 2: Casting Models in Canonical Form 9 2.1 Notation 9 2.1.1 Log-Linear Model Representations 11 2.1.2 Nonlinear Model Representations 11 2.2 Linearization 12 2.2.1 Taylor Series Approximation 12 2.2.2 Log-Linear Approximations 14 2.2.3 Example Equations 15   Chapter 3: DSGE Models: Three Examples 18 3.1 Model I: A Real Business Cycle Model 20 3.1.1 Environment 20 3.1.2 The Nonlinear System 23 3.1.3 Log-Linearization 26 3.2 Model II: Monopolistic Competition and Monetary Policy 28 3.2.1 Environment 28 3.2.2 The Nonlinear System 33 3.2.3 Log-Linearization 34 3.3 Model III: Asset Pricing 38 3.3.1 Single-Asset Environment 38 3.3.2 Multi-Asset Environment 39 3.3.3 Alternative Preference Specifications 40   Part II Model Solution Techniques Chapter 4: Linear Solution Techniques 51 4.1 Homogeneous Systems 52 4.2 Example Models 54 4.2.1 The Optimal Consumption Model 54 4.2.2 Asset Pricing with Linear Utility 55 4.2.3 Ramsey's Optimal Growth Model 56 4.3 Blanchard and Kahn's Method 57 4.4 Sims' Method 61 4.5 Klein's Method 64 4.6 An Undetermined Coefficients Approach 66v    Chpater 5: Nonlinear Solution Techniques 69 5.1 Projection Methods 71 5.1.1 Overview 71 5.1.2 Finite Element Methods 72 5.1.3 Orthogonal Polynomials 73 5.1.4 Implementation 74 5.1.5 Extension to the l-dimensional Case 78 5.1.6 Application to the Optimal Growth Model 79 5.2 Iteration Techniques: Value-Function and Policy-Function Iterations 87 5.2.1 Dynamic Programming 87 5.2.2 Value-Function Iterations 89 5.2.3 Policy-Function Iterations 94 5.3 Perturbation Techniques 95 5.3.1 Notation 95 5.3.2 Overview 97 5.3.3 Application to DSGE Models 99 5.3.4 Application to an Asset-Pricing Model 105   Part III Data Preparation and Representation Chapter 6: Removing Trends and Isolating Cycles 113 6.1 Removing Trends 115 6.2 Isolating Cycles 120 6.2.1 Mathematical Background 120 6.2.2 Cramer Representations 124 6.2.3 Spectra 125 6.2.4 Using Filters to Isolate Cycles 126 6.2.5 The Hodrick-Prescott Filter 128 6.2.6 Seasonal Adjustment 130 6.2.7 Band Pass Filters 131 6.3 Spuriousness 134   Chapter 7: Summarizing Time Series Behavior When All Variables Are Observable 138 7.1 Two Useful Reduced-Form Models 139 7.1.1 The ARMA Model 139 7.1.2 Allowing for Heteroskedastic Innovations 145 7.1.3 The VAR Model 147 7.2 Summary Statistics 149 7.2.1 Determining Lag Lengths 157 7.2.2 Characterizing the Precision of Measurements 159 7.3 Obtaining Theoretical Predictions of Summary Statistics 162   Chapter 8: State-Space Representations 166 8.1 Introduction 166 8.1.1 ARMA Models 167 8.2 DSGE Models as State-Space Representations 169 8.3 Overview of Likelihood Evaluation and Filtering 171 8.4 The Kalman Filter 173 8.4.1 Background 173 8.4.2 The Sequential Algorithm 175 8.4.3 Smoothing 178 8.4.4 Serially Correlated Measurement Errors 181 8.5 Examples of Reduced-Form State-Space Representations 182 8.5.1 Time-Varying Parameters 182 8.5.2 Stochastic Volatility 185 8.5.3 Regime Switching 186 8.5.4 Dynamic Factor Models 187   Part IV Monte Carlo Methods Chapter 9: Monte Carlo Integration: The Basics 193 9.1 Motivation and Overview 193 9.2 Direct Monte Carlo Integration 196 9.2.1 Model Simulation 198 9.2.2 Posterior Inference via Direct Monte Carlo Integration 201 9.3 Importance Sampling 202 9.3.1 Achieving Efficiency: A First Pass 206 9.4 Efficient Importance Sampling 211 9.5 Markov Chain Monte Carlo Integration 215 9.5.1 The Gibbs Sampler 216 9.5.2 Metropolis-Hastings Algorithms 218   Chapter 10: Likelihood Evaluation and Filtering in State-Space Representations Using Sequential Monte Carlo Methods 221 10.1 Background 221 10.2 Unadapted Filters 224 10.3 Conditionally Optimal Filters 228 10.4 Unconditional Optimality: The EIS Filter 233 10.4.1 Degenerate Transitions 235 10.4.2 Initializing the Importance Sampler 236 10.4.3 Example 239 10.5 Application to DSGE Models 241 10.5.1 Initializing the Importance Sampler 243 10.5.2 Initializing the Filtering Density 245 10.5.3 Application to the RBC Model 246   Part V Empirical Methods Chapter 11: Calibration 253 11.1 Historical Origins and Philosophy 253 11.2 Implementation 258 11.3 The Welfare Cost of Business Cycles 261 11.4 Productivity Shocks and Business Cycle Fluctuations 268 11.5 The Equity Premium Puzzle 273 11.6 Critiques and Extensions 276 11.6.1 Critiques 276 11.6.2 Extensions 279   Chapter 12: Matching Moments 285 12.1 Overview 285 12.2 Implementation 286 12.2.1 The Generalized Method of Moments 286 12.2.2 The Simulated Method of Moments 294 12.2.3 Indirect Inference 297 12.3 Implementation in DSGE Models 300 12.3.1 Analyzing Euler Equations 300 12.3.2 Analytical Calculations Based on Linearized Models 301 12.3.3 Simulations Involving Linearized Models 306 12.3.4 Simulations Involving Nonlinear Approximations 307 12.4 Empirical Application: Matching RBC Moments 308   Chapter 13: Maximum Likelihood 314 13.1 Overview 314 13.2 Introduction and Historical Background 316 13.3 A Primer on Optimization Algorithms 318 13.3.1 Simplex Methods 319 13.3.2 Derivative-Based Methods 328 13.4 Ill-Behaved Likelihood Surfaces: Problems and Solutions 330 13.4.1 Problems 330 13.4.2 Solutions 331 13.5 Model Diagnostics and Parameter Stability 334 13.6 Empirical Application: Identifying Sources of Business Cycle Fluctuations 337   Chapter 14: Bayesian Methods 351 14.1 Overview of Objectives 351 14.2 Preliminaries 352 14.3 Using Structural Models as Sources of Prior Information for Reduced-Form Analysis 355 14.4 Implementing Structural Models Directly 360 14.5 Model Comparison 361 14.6 Using an RBC Model as a Source of Prior Information for Forecasting 364 14.7 Estimating and Comparing Asset-Pricing Models 373 14.7.1 Estimates 380 14.7.2 Model Comparison 384   References 387 Index 401","brand":"Princeton University Press","offers":[{"title":"Default Title","offer_id":48865526153559,"sku":"9780691152875","price":63.75,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780691152875.jpg?v=1722274392"},{"product_id":"fooled-by-randomness-9780812975215","title":"Fooled by Randomness","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e","brand":"Random House Publishing Group","offers":[{"title":"Default Title","offer_id":48865982972247,"sku":"9780812975215","price":15.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780812975215.jpg?v=1722276493"},{"product_id":"trading-with-ichimoku-9780857196156","title":"Trading with Ichimoku","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eThe English language edition of the successful French publication.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eThe Ichimoku Kinko Hyo trading indicator is an information-rich and extremely reliable tool that can be employed across all time frames. Once you have learned the subtleties of the method and understand its unique system of validating price movements, it will improve your trading.\u003cbr\u003e\u003cbr\u003e\u003ci\u003eTrading with Ichimoku\u003c\/i\u003e is a practical handbook explaining the different elements of the Ichimoku system of chart reading, from the description of each of its five lines to their interpretation within a wider process of trading analysis.\u003cbr\u003e\u003cbr\u003eYou will rapidly conclude that even though there are only five lines to look at on Ichimoku charts, the information given is more than enough to achieve a detailed and broad view of market and what the price action reveals.\u003cbr\u003e\u003cbr\u003ePart 1 is devoted to the theoretical description of the various components making up Ichimoku.\u003cbr\u003e\u003cbr\u003ePart 2 explains how to trade with Ichimoku Kinko Hyo through several examples in various time frames.\u003cbr\u003e\u003cbr\u003ePart 3 introduces trading methods that combine classical trading tools with Ichimoku Kinko Hyo.\u003cbr\u003e\u003cbr\u003eExplanations and examples are illustrated throughout with detailed colour charts.\u003cbr\u003e\u003cbr\u003eWhether you are a beginner or an accomplished trader, you should add a knowledge of Ichimoku to your armoury to improve your analysis and your results.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003eReviews from French readers:\u003cbr\u003e\u003cbr\u003e?I highly recommend this book for anyone who wants to learn to use Ichimoku.?\u003cbr\u003e\u003cbr\u003e?Clear explanations and especially great tips on how to trade.?\u003cbr\u003e\u003cbr\u003e?Very informative book with clear and precise examples.?\u003cbr\u003e\u003cbr\u003e?Good balance between the theory, analysis and trading.?\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003eAbout the authorIntroductionPart 1: Ichimoku Theory1. Theory2. Reading Ichimoku charts3. AnalysisPart 2: Ichimoku in Practice4. Trading5. Advanced techniquesPart 3: Ichimoku and Other Indicators6. FibonacciPart 4: The Art of Disciplined Trading7. Conclusion","brand":"Harriman House Publishing","offers":[{"title":"Default Title","offer_id":48866071085399,"sku":"9780857196156","price":27.99,"currency_code":"GBP","in_stock":true}]},{"product_id":"essential-mathematics-for-economics-and-business-9781118358290","title":"Essential Mathematics for Economics and Business","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e* Now in 4 colour and accompanied by an outstanding suite of resources.    * Combines a non-rigorous approach to mathematics with applications in economics and business.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMathematical Preliminaries 1\u003c\/p\u003e \u003cp\u003e1.1 Some Mathematical Preliminaries 2\u003c\/p\u003e \u003cp\u003e1.2 Arithmetic Operations 3\u003c\/p\u003e \u003cp\u003e1.3 Fractions 6\u003c\/p\u003e \u003cp\u003e1.4 Solving Equations 11\u003c\/p\u003e \u003cp\u003e1.5 Currency Conversions 14\u003c\/p\u003e \u003cp\u003e1.6 Simple Inequalities 18\u003c\/p\u003e \u003cp\u003e1.7 Calculating Percentages 21\u003c\/p\u003e \u003cp\u003e1.8 The Calculator. Evaluation and Transposition of Formulae 24\u003c\/p\u003e \u003cp\u003e1.9 Introducing Excel 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Straight Line and Applications 37\u003c\/p\u003e \u003cp\u003e2.1 The Straight Line 38\u003c\/p\u003e \u003cp\u003e2.2 Mathematical Modelling 54\u003c\/p\u003e \u003cp\u003e2.3 Applications: Demand, Supply, Cost, Revenue 59\u003c\/p\u003e \u003cp\u003e2.4 More Mathematics on the Straight Line 76\u003c\/p\u003e \u003cp\u003e2.5 Translations of Linear Functions 82\u003c\/p\u003e \u003cp\u003e2.6 Elasticity of Demand, Supply and Income 83\u003c\/p\u003e \u003cp\u003e2.7 Budget and Cost Constraints 91\u003c\/p\u003e \u003cp\u003e2.8 Excel for Linear Functions 92\u003c\/p\u003e \u003cp\u003e2.9 Summary 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSimultaneous Equations 101\u003c\/p\u003e \u003cp\u003e3.1 Solving Simultaneous Linear Equations 102\u003c\/p\u003e \u003cp\u003e3.2 Equilibrium and Break-even 111\u003c\/p\u003e \u003cp\u003e3.3 Consumer and Producer Surplus 128\u003c\/p\u003e \u003cp\u003e3.4 The National Income Model and the IS-LM Model 133\u003c\/p\u003e \u003cp\u003e3.5 Excel for Simultaneous Linear Equations 137\u003c\/p\u003e \u003cp\u003e3.6 Summary 142\u003c\/p\u003e \u003cp\u003eAppendix 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNon-linear Functions and Applications 147\u003c\/p\u003e \u003cp\u003e4.1 Quadratic, Cubic and Other Polynomial Functions 148\u003c\/p\u003e \u003cp\u003e4.2 Exponential Functions 170\u003c\/p\u003e \u003cp\u003e4.3 Logarithmic Functions 184\u003c\/p\u003e \u003cp\u003e4.4 Hyperbolic (Rational) Functions of the Form a\/(bx + c) 197\u003c\/p\u003e \u003cp\u003e4.5 Excel for Non-linear Functions 202\u003c\/p\u003e \u003cp\u003e4.6 Summary 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFinancial Mathematics 209\u003c\/p\u003e \u003cp\u003e5.1 Arithmetic and Geometric Sequences and Series 210\u003c\/p\u003e \u003cp\u003e5.2 Simple Interest, Compound Interest and Annual Percentage Rates 218\u003c\/p\u003e \u003cp\u003e5.3 Depreciation 228\u003c\/p\u003e \u003cp\u003e5.4 Net Present Value and Internal Rate of Return 230\u003c\/p\u003e \u003cp\u003e5.5 Annuities, Debt Repayments, Sinking Funds 236\u003c\/p\u003e \u003cp\u003e5.6 The Relationship between Interest Rates and the Price of Bonds 248\u003c\/p\u003e \u003cp\u003e5.7 Excel for Financial Mathematics 251\u003c\/p\u003e \u003cp\u003e5.8 Summary 254\u003c\/p\u003e \u003cp\u003eAppendix 256\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDifferentiation and Applications 259\u003c\/p\u003e \u003cp\u003e6.1 Slope of a Curve and Differentiation 260\u003c\/p\u003e \u003cp\u003e6.2 Applications of Differentiation, Marginal Functions, Average Functions 270\u003c\/p\u003e \u003cp\u003e6.3 Optimisation for Functions of One Variable 286\u003c\/p\u003e \u003cp\u003e6.4 Economic Applications of Maximum and Minimum Points 304\u003c\/p\u003e \u003cp\u003e6.5 Curvature and Other Applications 320\u003c\/p\u003e \u003cp\u003e6.6 Further Differentiation and Applications 334\u003c\/p\u003e \u003cp\u003e6.7 Elasticity and the Derivative 347\u003c\/p\u003e \u003cp\u003e6.8 Summary 357\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFunctions of Several Variables 361\u003c\/p\u003e \u003cp\u003e7.1 Partial Differentiation 362\u003c\/p\u003e \u003cp\u003e7.2 Applications of Partial Differentiation 380\u003c\/p\u003e \u003cp\u003e7.3 Unconstrained Optimisation 400\u003c\/p\u003e \u003cp\u003e7.4 Constrained Optimisation and Lagrange Multipliers 410\u003c\/p\u003e \u003cp\u003e7.5 Summary 422\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntegration and Applications 427\u003c\/p\u003e \u003cp\u003e8.1 Integration as the Reverse of Differentiation 428\u003c\/p\u003e \u003cp\u003e8.2 The Power Rule for Integration 429\u003c\/p\u003e \u003cp\u003e8.3 Integration of the Natural Exponential Function 435\u003c\/p\u003e \u003cp\u003e8.4 Integration by Algebraic Substitution 436\u003c\/p\u003e \u003cp\u003e8.5 The Definite Integral and the Area under a Curve 441\u003c\/p\u003e \u003cp\u003e8.6 Consumer and Producer Surplus 448\u003c\/p\u003e \u003cp\u003e8.7 First-order Differential Equations and Applications 456\u003c\/p\u003e \u003cp\u003e8.8 Differential Equations for Limited and Unlimited Growth 468\u003c\/p\u003e \u003cp\u003e8.9 Integration by Substitution and Integration by Parts website only\u003c\/p\u003e \u003cp\u003e8.10 Summary 474\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eLinear Algebra and Applications 477\u003c\/p\u003e \u003cp\u003e9.1 Linear Programming 478\u003c\/p\u003e \u003cp\u003e9.2 Matrices 488\u003c\/p\u003e \u003cp\u003e9.3 Solution of Equations: Elimination Methods 498\u003c\/p\u003e \u003cp\u003e9.4 Determinants 504\u003c\/p\u003e \u003cp\u003e9.5 The Inverse Matrix and Input\/Output Analysis 518\u003c\/p\u003e \u003cp\u003e9.6 Excel for Linear Algebra 531\u003c\/p\u003e \u003cp\u003e9.7 Summary 534\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDifference Equations 539\u003c\/p\u003e \u003cp\u003e10.1 Introduction to Difference Equations 540\u003c\/p\u003e \u003cp\u003e10.2 Solution of Difference Equations (First-order) 542\u003c\/p\u003e \u003cp\u003e10.3 Applications of Difference Equations (First-order) 554\u003c\/p\u003e \u003cp\u003e10.4 Summary 564\u003c\/p\u003e \u003cp\u003eSolutions to Progress Exercises 567\u003c\/p\u003e \u003cp\u003eWorked Examples 653\u003c\/p\u003e \u003cp\u003eIndex 659\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866369405271,"sku":"9781118358290","price":54.1,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118358290.jpg?v=1722278319"},{"product_id":"nonparametric-finance-9781119409106","title":"Nonparametric Finance","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eAn Introduction to Machine Learning in Finance, With Mathematical Background, Data Visualization, and R Nonparametric function estimation is an important part of machine learning, which is becoming increasingly important in quantitative finance. Nonparametric Finance provides graduate students and finance professionals with a foundation in nonparametric function estimation and the underlying mathematics. Combining practical applications, mathematically rigorous presentation, and statistical data analysis into a single volume, this book presents detailed instruction in discrete chapters that allow readers to dip in as needed without reading from beginning to end.    Coverage includes statistical finance, risk management, portfolio management, and securities pricing to provide a practical knowledge base, and the introductory chapter introduces basic finance concepts for readers with a strictly mathematical background. Economic significance is emphasized over statistical significance thro\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Statistical Finance 2\u003c\/p\u003e \u003cp\u003e1.2 Risk Management 3\u003c\/p\u003e \u003cp\u003e1.3 Portfolio Management 5\u003c\/p\u003e \u003cp\u003e1.4 Pricing of Securities 6\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Statistical Finance 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Financial Instruments 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Stocks 13\u003c\/p\u003e \u003cp\u003e2.2 Fixed Income Instruments 19\u003c\/p\u003e \u003cp\u003e2.3 Derivatives 23\u003c\/p\u003e \u003cp\u003e2.4 Data Sets 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Univariate Data Analysis 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Univariate Statistics 34\u003c\/p\u003e \u003cp\u003e3.2 Univariate Graphical Tools 42\u003c\/p\u003e \u003cp\u003e3.3 Univariate ParametricModels 55\u003c\/p\u003e \u003cp\u003e3.4 Tail Modeling 61\u003c\/p\u003e \u003cp\u003e3.5 Asymptotic Distributions 83\u003c\/p\u003e \u003cp\u003e3.6 Univariate Stylized Facts 91\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Multivariate Data Analysis 95\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Measures of Dependence 95\u003c\/p\u003e \u003cp\u003e4.2 Multivariate Graphical Tools 103\u003c\/p\u003e \u003cp\u003e4.3 Multivariate ParametricModels 107\u003c\/p\u003e \u003cp\u003e4.4 Copulas 111\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Time Series Analysis 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Stationarity and Autocorrelation 122\u003c\/p\u003e \u003cp\u003e5.2 Model Free Estimation 128\u003c\/p\u003e \u003cp\u003e5.3 Univariate Time Series Models 135\u003c\/p\u003e \u003cp\u003e5.4 Multivariate Time Series Models 157\u003c\/p\u003e \u003cp\u003e5.5 Time Series Stylized Facts 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Prediction 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Methods of Prediction 164\u003c\/p\u003e \u003cp\u003e6.2 Forecast Evaluation 170\u003c\/p\u003e \u003cp\u003e6.3 Predictive Variables 175\u003c\/p\u003e \u003cp\u003e6.4 Asset Return Prediction 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Risk Management 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Volatility Prediction 195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Applications of Volatility Prediction 197\u003c\/p\u003e \u003cp\u003e7.2 Performance Measures for Volatility Predictors 199\u003c\/p\u003e \u003cp\u003e7.3 Conditional Heteroskedasticity Models 200\u003c\/p\u003e \u003cp\u003e7.4 Moving Average Methods 205\u003c\/p\u003e \u003cp\u003e7.5 State Space Predictors 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Quantiles and Value-at-Risk 219\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Definitions of Quantiles 220\u003c\/p\u003e \u003cp\u003e8.2 Applications of Quantiles 223\u003c\/p\u003e \u003cp\u003e8.3 Performance Measures for Quantile Estimators 227\u003c\/p\u003e \u003cp\u003e8.4 Nonparametric Estimators of Quantiles 233\u003c\/p\u003e \u003cp\u003e8.5 Volatility Based Quantile Estimation 240\u003c\/p\u003e \u003cp\u003e8.6 Excess Distributions in Quantile Estimation 258\u003c\/p\u003e \u003cp\u003e8.7 Extreme ValueTheory in Quantile Estimation 288\u003c\/p\u003e \u003cp\u003e8.8 Expected Shortfall 292\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Portfolio Management 297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Some Basic Concepts of Portfolio Theory 299\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Portfolios and Their Returns 300\u003c\/p\u003e \u003cp\u003e9.2 Comparison of Return andWealth Distributions 312\u003c\/p\u003e \u003cp\u003e9.3 Multiperiod Portfolio Selection 326\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Performance Measurement 337\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 The Sharpe Ratio 338\u003c\/p\u003e \u003cp\u003e10.2 Certainty Equivalent 346\u003c\/p\u003e \u003cp\u003e10.3 Drawdown 347\u003c\/p\u003e \u003cp\u003e10.4 Alpha and Conditional Alpha 348\u003c\/p\u003e \u003cp\u003e10.5 Graphical Tools of Performance Measurement 356\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Markowitz Portfolios 367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Variance Penalized Expected Return 369\u003c\/p\u003e \u003cp\u003e11.2 Minimizing Variance under a Sufficient Expected Return 372\u003c\/p\u003e \u003cp\u003e11.3 Markowitz Bullets 375\u003c\/p\u003e \u003cp\u003e11.4 Further Topics in Markowitz Portfolio Selection 381\u003c\/p\u003e \u003cp\u003e11.5 Examples of Markowitz Portfolio Selection 383\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Dynamic Portfolio Selection 385\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Prediction in Dynamic Portfolio Selection 387\u003c\/p\u003e \u003cp\u003e12.2 Backtesting Trading Strategies 393\u003c\/p\u003e \u003cp\u003e12.3 One Risky Asset 394\u003c\/p\u003e \u003cp\u003e12.4 Two Risky Assets 405\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Pricing of Securities 419\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Principles of Asset Pricing 421\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction to Asset Pricing 422\u003c\/p\u003e \u003cp\u003e13.2 Fundamental Theorems of Asset Pricing 430\u003c\/p\u003e \u003cp\u003e13.3 Evaluation of Pricing and Hedging Methods 456\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Pricing by Arbitrage 459\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Futures and the Put–Call Parity 460\u003c\/p\u003e \u003cp\u003e14.2 Pricing in Binary Models 466\u003c\/p\u003e \u003cp\u003e14.3 Black–Scholes Pricing 485\u003c\/p\u003e \u003cp\u003e14.4 Black–Scholes Hedging 505\u003c\/p\u003e \u003cp\u003e14.5 Black–Scholes Hedging and Volatility Estimation 515\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Pricing in IncompleteModels 521\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Quadratic Hedging and Pricing 522\u003c\/p\u003e \u003cp\u003e15.2 Utility Maximization 523\u003c\/p\u003e \u003cp\u003e15.3 Absolutely Continuous Changes of Measures 530\u003c\/p\u003e \u003cp\u003e15.4 GARCH Market Models 534\u003c\/p\u003e \u003cp\u003e15.5 Nonparametric Pricing Using Historical Simulation 545\u003c\/p\u003e \u003cp\u003e15.6 Estimation of the Risk-Neutral Density 551\u003c\/p\u003e \u003cp\u003e15.7 Quantile Hedging 555\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Quadratic and Local Quadratic Hedging 557\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Quadratic Hedging 558\u003c\/p\u003e \u003cp\u003e16.2 Local Quadratic Hedging 583\u003c\/p\u003e \u003cp\u003e16.3 Implementations of Local Quadratic Hedging 595\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Option Strategies 615\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Option Strategies 616\u003c\/p\u003e \u003cp\u003e17.2 Profitability of Option Strategies 625\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Interest Rate Derivatives 649\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Basic Concepts of Interest Rate Derivatives 650\u003c\/p\u003e \u003cp\u003e18.2 Interest Rate Forwards 659\u003c\/p\u003e \u003cp\u003e18.3 Interest Rate Options 666\u003c\/p\u003e \u003cp\u003e18.4 Modeling Interest Rate Markets 669\u003c\/p\u003e \u003cp\u003eReferences 673\u003c\/p\u003e \u003cp\u003eIndex 681\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866394571095,"sku":"9781119409106","price":100.76,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119409106.jpg?v=1722278446"},{"product_id":"a-guide-to-modern-econometrics-9781119472117","title":"A Guide to Modern Econometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 About Econometrics\u003c\/p\u003e \u003cp\u003e1.2 The Structure of This Book\u003c\/p\u003e \u003cp\u003e1.3 Illustrations and Exercises\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 An Introduction to Linear Regression \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Ordinary Least Squares as an Algebraic Tool\u003c\/p\u003e \u003cp\u003e2.2 The Linear Regression Model\u003c\/p\u003e \u003cp\u003e2.3 Small Sample Properties of the OLS Estimator\u003c\/p\u003e \u003cp\u003e2.4 Goodness-of-fit\u003c\/p\u003e \u003cp\u003e2.5 Hypothesis Testing\u003c\/p\u003e \u003cp\u003e2.6 Asymptotic Properties of the OLS Estimator\u003c\/p\u003e \u003cp\u003e2.7 Illustration: The Capital Asset Pricing Model\u003c\/p\u003e \u003cp\u003e2.8 Multicollinearity\u003c\/p\u003e \u003cp\u003e2.9 Missing Data, Outliers and Influential Observations\u003c\/p\u003e \u003cp\u003e2.10 Prediction\u003c\/p\u003e \u003cp\u003eWrap-up\u003c\/p\u003e \u003cp\u003eExercises\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Interpreting and Comparing Regression Models \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Interpreting the Linear Model\u003c\/p\u003e \u003cp\u003e3.2 Selecting the Set of Regressors\u003c\/p\u003e \u003cp\u003e3.3 Misspecifying the Functional Form\u003c\/p\u003e \u003cp\u003e3.4 Illustration: Explaining House Prices\u003c\/p\u003e \u003cp\u003e3.5 Illustration: Predicting Stock Index Returns\u003c\/p\u003e \u003cp\u003e3.6 Illustration: Explaining Individual Wages\u003c\/p\u003e \u003cp\u003eWrap-up\u003c\/p\u003e \u003cp\u003eExercises\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Heteroskedasticity and Autocorrelation \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Consequences for the OLS Estimator\u003c\/p\u003e \u003cp\u003e4.2 Deriving an Alternative Estimator\u003c\/p\u003e \u003cp\u003e4.3 Heteroskedasticity\u003c\/p\u003e \u003cp\u003e4.4 Testing for Heteroskedasticity\u003c\/p\u003e \u003cp\u003e4.5 Illustration: Explaining Labour Demand\u003c\/p\u003e \u003cp\u003e4.6 Autocorrelation\u003c\/p\u003e \u003cp\u003e4.7 Testing for First-order Autocorrelation\u003c\/p\u003e \u003cp\u003e4.8 Illustration: The Demand for Ice Cream\u003c\/p\u003e \u003cp\u003e4.9 Alternative Autocorrelation Patterns\u003c\/p\u003e \u003cp\u003e4.10 What to do When you Find Autocorrelation?\u003c\/p\u003e \u003cp\u003e4.11 Illustration: Risk Premia in Foreign Exchange Markets\u003c\/p\u003e \u003cp\u003eWrap-up\u003c\/p\u003e \u003cp\u003eExercises\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Endogenous Regressors, Instrumental Variables and GMM \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 A Review of the Properties of the OLS Estimator\u003c\/p\u003e \u003cp\u003e5.2 Cases Where the OLS Estimator Cannot be Saved\u003c\/p\u003e \u003cp\u003e5.3 The Instrumental Variables Estimator\u003c\/p\u003e \u003cp\u003e5.4 Illustration: Estimating the Returns to Schooling\u003c\/p\u003e \u003cp\u003e5.5 Alternative Approaches to Estimate Causal Effects\u003c\/p\u003e \u003cp\u003e5.6 The Generalized Instrumental Variables Estimator\u003c\/p\u003e \u003cp\u003e5.7 Institutions and Economic Development\u003c\/p\u003e \u003cp\u003e5.8 The Generalized Method of Moments\u003c\/p\u003e \u003cp\u003e5.9 Illustration: Estimating Intertemporal Asset Pricing Models\u003c\/p\u003e \u003cp\u003eWrap-up\u003c\/p\u003e \u003cp\u003eExercises\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Maximum Likelihood Estimation and Specification Tests \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 An Introduction to Maximum Likelihood\u003c\/p\u003e \u003cp\u003e6.2 Specification Tests\u003c\/p\u003e \u003cp\u003e6.3 Tests in the Normal Linear Regression Model\u003c\/p\u003e \u003cp\u003e6.4 Quasi-maximum Likelihood and Moment Conditions Tests\u003c\/p\u003e \u003cp\u003eWrap-up\u003c\/p\u003e \u003cp\u003eExercises\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Models with Limited Dependent Variables \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Binary Choice Models\u003c\/p\u003e \u003cp\u003e7.2 Multiresponse Models\u003c\/p\u003e \u003cp\u003e7.3 Models for Count Data\u003c\/p\u003e \u003cp\u003e7.4 Tobit Models\u003c\/p\u003e \u003cp\u003e7.5 Extensions of Tobit Models\u003c\/p\u003e \u003cp\u003e7.6 Sample Selection Bias\u003c\/p\u003e \u003cp\u003e7.7 Estimating Treatment Effects\u003c\/p\u003e \u003cp\u003e7.7.1 Regression-based Estimators\u003c\/p\u003e \u003cp\u003e7.8 Duration Models\u003c\/p\u003e \u003cp\u003eWrap-up\u003c\/p\u003e \u003cp\u003eExercises\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Univariate Time Series Models \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction\u003c\/p\u003e \u003cp\u003e8.2 General ARMA Processes\u003c\/p\u003e \u003cp\u003e8.3 Stationarity and Unit Roots\u003c\/p\u003e \u003cp\u003e8.4 Testing for Unit Roots\u003c\/p\u003e \u003cp\u003e8.5 Illustration: Long-run Purchasing Power Parity (Part 1)\u003c\/p\u003e \u003cp\u003e8.6 Estimation of ARMA Models\u003c\/p\u003e \u003cp\u003e8.7 Choosing a Model\u003c\/p\u003e \u003cp\u003e8.8 Illustration: The Persistence of Inflation\u003c\/p\u003e \u003cp\u003e8.9 Forecasting with ARMA Models\u003c\/p\u003e \u003cp\u003e8.10 Illustration: The Expectations Theory of the Term Structure\u003c\/p\u003e \u003cp\u003e8.11 Autoregressive Conditional Heteroskedasticity\u003c\/p\u003e \u003cp\u003e8.12 What about Multivariate Models?\u003c\/p\u003e \u003cp\u003eWrap-up\u003c\/p\u003e \u003cp\u003eExercises\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Multivariate Time Series Models \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Dynamic Models with Stationary Variables\u003c\/p\u003e \u003cp\u003e9.2 Models with Nonstationary Variables\u003c\/p\u003e \u003cp\u003e9.3 Illustration: Long-run Purchasing Power Parity (Part 2)\u003c\/p\u003e \u003cp\u003e9.4 Vector Autoregressive Models\u003c\/p\u003e \u003cp\u003e9.5 Cointegration: the Multivariate Case\u003c\/p\u003e \u003cp\u003e9.6 Illustration: Money Demand and Inflation\u003c\/p\u003e \u003cp\u003eWrap-up\u003c\/p\u003e \u003cp\u003eExercises\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Models Based on Panel Data \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction to Panel Data Modelling\u003c\/p\u003e \u003cp\u003e10.2 The Static Linear Model\u003c\/p\u003e \u003cp\u003e10.3 Illustration: Explaining Individual Wages\u003c\/p\u003e \u003cp\u003e10.4 Dynamic Linear Models\u003c\/p\u003e \u003cp\u003e10.5 Illustration: Explaining Capital Structure\u003c\/p\u003e \u003cp\u003e10.6 Panel Time Series\u003c\/p\u003e \u003cp\u003e10.7 Models with Limited Dependent Variables\u003c\/p\u003e \u003cp\u003e10.8 Incomplete Panels and Selection Bias\u003c\/p\u003e \u003cp\u003e10.9 Pseudo Panels and Repeated Cross-sections\u003c\/p\u003e \u003cp\u003eWrap-up\u003c\/p\u003e \u003cp\u003eA Vectors and Matrices\u003c\/p\u003e \u003cp\u003eA.1 Terminology\u003c\/p\u003e \u003cp\u003eA.2 Matrix Manipulations\u003c\/p\u003e \u003cp\u003eA.3 Properties of Matrices and Vectors\u003c\/p\u003e \u003cp\u003eA.4 Inverse Matrices\u003c\/p\u003e \u003cp\u003eA.5 Idempotent Matrices\u003c\/p\u003e \u003cp\u003eA.6 Eigenvalues and Eigenvectors\u003c\/p\u003e \u003cp\u003eA.7 Differentiation\u003c\/p\u003e \u003cp\u003eA.8 Some Least Squares Manipulations\u003c\/p\u003e \u003cp\u003eB Statistical and Distribution Theory\u003c\/p\u003e \u003cp\u003eB.1 Discrete Random Variables\u003c\/p\u003e \u003cp\u003eB.2 Continuous Random Variables\u003c\/p\u003e \u003cp\u003eB.3 Expectations and Moments\u003c\/p\u003e \u003cp\u003eB.4 Multivariate Distributions\u003c\/p\u003e \u003cp\u003eB.5 Conditional Distributions\u003c\/p\u003e \u003cp\u003eB.6 The Normal Distribution\u003c\/p\u003e \u003cp\u003eB.7 Related Distributions\u003c\/p\u003e \u003cp\u003eBibliograph\u003c\/p\u003e \u003cp\u003eIndex\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866397913431,"sku":"9781119472117","price":45.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119472117.jpg?v=1722278454"},{"product_id":"statistics-for-business-and-economics-global-edition-9781292436845","title":"Statistics for Business and Economics Global","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eDr. Bill Carlson\u003c\/strong\u003e is professor emeritus of economics at St. Olaf College, where he taught for 31 years, serving several times as department chair and in various administrative functions, including director of academic computing. He has also held leave assignments with the U.S. government and the University of Minnesota in addition to lecturing at many different universities. He was elected an honorary member of Phi Beta Kappa. In addition, he spent 10 years in private industry and contract research prior to beginning his career at St. Olaf. His education includes engineering degrees from Michigan Technological University (BS) and from the Illinois Institute of Technology (MS) and a PhD in quantitative management from the Rackham Graduate School at the University of Michigan. Numerous research projects related to management, highway safety, and statistical education have produced more than50 publications. He received the Metropolitan Insurance Award of Merit for Safet\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003col\u003e\n\u003cli\u003eDescribing Data: Graphical\u003c\/li\u003e\n\u003cli\u003eDescribing Data: Numerical\u003c\/li\u003e\n\u003cli\u003eProbability\u003c\/li\u003e\n\u003cli\u003eDiscrete Random Variables and Probability Distributions\u003c\/li\u003e\n\u003cli\u003eContinuous Random Variables and Probability Distributions\u003c\/li\u003e\n\u003cli\u003eSampling and Sampling Distributions\u003c\/li\u003e\n\u003cli\u003eEstimation: Single Population\u003c\/li\u003e\n\u003cli\u003eEstimation: Additional Topics\u003c\/li\u003e\n\u003cli\u003eHypothesis Testing: Single Population\u003c\/li\u003e\n\u003cli\u003eHypothesis Testing: Additional Topics\u003c\/li\u003e\n\u003cli\u003eSimple Regression\u003c\/li\u003e\n\u003cli\u003eMultiple Regression\u003c\/li\u003e\n\u003cli\u003eAdditional Topics in Regression Analysis\u003c\/li\u003e\n\u003cli\u003eAnalysis of Categorical Data\u003c\/li\u003e\n\u003cli\u003eAnalysis of Variance\u003c\/li\u003e\n\u003cli\u003eTime-Series Analysis and Forecasting\u003c\/li\u003e\n\u003cli\u003eAdditional Topics in Sampling\u003c\/li\u003e\n\u003c\/ol\u003e","brand":"Pearson Education Limited","offers":[{"title":"Default Title","offer_id":48866532655447,"sku":"9781292436845","price":51.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781292436845.jpg?v=1722279106"},{"product_id":"contemporary-project-management-9781337406451","title":"Contemporary Project Management","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eLearn to master the most proven methods in project management as well as exciting new techniques emerging from current industry and today's most recent research with Kloppenborg's CONTEMPORARY PROJECT MANAGEMENT, 4E. This edition introduces time-tested manual techniques and progressive automated techniques, all consistent with the latest PMBOK Guide and standards and integrated with Microsoft Project 2016. The book's focused approach is ideal for building strong portfolios that showcase project management skills for future interviews. All content is consistent with the knowledge areas and processes of the 6th edition of the PMBOK Guide to give you an advantage as you prepare to become a Certified Associate in Project Management (CAPM) or Certified Project Management Professional (PMP), if desired.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart I: ORGANIZING PROJECTS. 1. Introduction to Project Management. 2. Project Selection and Prioritization. 3. Chartering Projects.  Part II: LEADING PROJECTS. 4. Organizational Capability: Structure, Culture, and Roles. 5. Leading and Managing Project Teams. 6. Stakeholder Analysis and Communication Planning. Part III: PLANNING PROJECTS. 7. Scope Planning. 8. Scheduling Projects. 9. Resourcing Projects. 10. Budgeting Projects. 11. Project Risk Planning. 12. Project Quality Planning and Project Kick-off. Part IV: PERFORMING PROJECTS. 13. Project Supply Chain Management. 14. Determining Project Progress and Results. 15. Finishing the Project and Realizing the Benefits. Appendix A  PMP and CAPM Exam Prep Suggestions Appendix B  Agile Differences Covered Appendix C  Answers to Selected Exercises Appendix D  Project Deliverables Appendix E  Strengths Themes as Used in Project Management (Available Online) Glossary Terms consistent the PMBOK�� Guide and multiple other PMI Guides and Standards. Index.","brand":"Cengage Learning, Inc","offers":[{"title":"Default Title","offer_id":48866571616599,"sku":"9781337406451","price":83.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781337406451.jpg?v=1722279271"},{"product_id":"introductory-econometrics-9781337558860","title":"Introductory Econometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. The Nature of Econometrics and Economic Data. Part I: REGRESSION ANALYSIS WITH CROSS-SECTIONAL DATA. 2. The Simple Regression Model. 3. Multiple Regression Analysis: Estimation. 4. Multiple Regression Analysis: Inference. 5. Multiple Regression Analysis: OLS Asymptotics. 6. Multiple Regression Analysis: Further Issues. 7. Multiple Regression Analysis with Qualitative Information. 8. Heteroskedasticity. 9. More on Specification and Data Problems. Part II: REGRESSION ANALYSIS WITH TIME SERIES DATA. 10. Basic Regression Analysis with Time Series Data. 11. Further Issues in Using OLS with Time Series Data. 12. Serial Correlation and Heteroskedasticity in Time Series Regressions. Part III: ADVANCED TOPICS. 13. Pooling Cross Sections Across Time: Simple Panel Data Methods. 14. Advanced Panel Data Methods. 15. Instrumental Variables Estimation and Two Stage Least Squares. 16. Simultaneous Equations Models. 17. Limited Dependent Variable Models and Sample Selection Corrections. 18. Advanced Time Series Topics. 19. Carrying Out an Empirical Project. Math Refresher A: Basic Mathematical Tools. Math Refresher B: Fundamentals of Probability. Math Refresher C: Fundamentals of Mathematical Statistics. Math Refresher D: Summary of Matrix Algebra. Math Refresher E: The Linear Regression Model in Matrix Form. Answers to Exploring Further Chapter Exercises. Statistical Tables. References. Glossary. Index.","brand":"Cengage Learning, Inc","offers":[{"title":"Default Title","offer_id":48866571977047,"sku":"9781337558860","price":71.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781337558860.jpg?v=1722279274"},{"product_id":"a-guide-to-econometrics-9781405182577","title":"A Guide to Econometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eThis is the perfect (and essential) supplement for all econometrics classes--from a rigorous first undergraduate course, to a first master''s, to a PhD course.\u003c\/b\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExplains what is going on in textbooks full of proofs and formulas\u003c\/li\u003e \u003cli\u003eOffers intuition, skepticism, insights, humor, and practical advice (dos and don'ts)\u003c\/li\u003e \u003cli\u003eContains new chapters that cover instrumental variables and computational considerations\u003c\/li\u003e \u003cli\u003eIncludes additional information on GMM, nonparametrics, and an introduction to wavelets\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“The exceptional success of this work is due to its clarity and economy of expression and the accessibility of the subject matter to a broad range of scholars. Now in its sixth edition, this guide brings practitioners and researchers up to date on the popular techniques in estimation. It holds a unique position among econometric texts. Highly recommended.” (\u003ci\u003eChoice\u003c\/i\u003e, November 2008)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface x\u003c\/p\u003e \u003cp\u003eDedication xii\u003c\/p\u003e \u003cp\u003e1. Introduction 1\u003c\/p\u003e \u003cp\u003e2. Criteria for Estimators 11\u003c\/p\u003e \u003cp\u003e3. The Classical Linear Regression Model 40\u003c\/p\u003e \u003cp\u003e4. Interval Estimation and Hypothesis Testing 51\u003c\/p\u003e \u003cp\u003e5. Specification 71\u003c\/p\u003e \u003cp\u003e6. Violating Assumption One: Wrong Regressors, Nonlinearities, and Parameter Inconstancy 93\u003c\/p\u003e \u003cp\u003e7. Violating Assumption Two: Nonzero Expected Disturbance 109\u003c\/p\u003e \u003cp\u003e8. Violating Assumption Three: Nonspherical Disturbances 112\u003c\/p\u003e \u003cp\u003e9. Violating Assumption Four: Instrumental Variable Estimation 137\u003c\/p\u003e \u003cp\u003e10. Violating Assumption Four: Measurement Errors and Autoregression 157\u003c\/p\u003e \u003cp\u003e11. Violating Assumption Four: Simultaneous Equations 171\u003c\/p\u003e \u003cp\u003e12. Violating Assumption Five: Multicollinearity 192\u003c\/p\u003e \u003cp\u003e13. Incorporating Extraneous Information 203\u003c\/p\u003e \u003cp\u003e14. The Bayesian Approach 213\u003c\/p\u003e \u003cp\u003e15. Dummy Variables 232\u003c\/p\u003e \u003cp\u003e16. Qualitative Dependent Variables 241\u003c\/p\u003e \u003cp\u003e17. Limited Dependent Variables 262\u003c\/p\u003e \u003cp\u003e18. Panel Data 281\u003c\/p\u003e \u003cp\u003e19. Time Series Econometrics 296\u003c\/p\u003e \u003cp\u003e20. Forecasting 331\u003c\/p\u003e \u003cp\u003e21. Robust Estimation 345\u003c\/p\u003e \u003cp\u003e22. Applied Econometrics 361\u003c\/p\u003e \u003cp\u003e23. Computational Considerations 385\u003c\/p\u003e \u003cp\u003eAppendix A: Sampling Distributions, the Foundation of Statistics 403\u003c\/p\u003e \u003cp\u003eAppendix B: All about Variance 407\u003c\/p\u003e \u003cp\u003eAppendix C: A Primer on Asymptotics 412\u003c\/p\u003e \u003cp\u003eAppendix D: Exercises 417\u003c\/p\u003e \u003cp\u003eAppendix E: Answers to Even-numbered Questions 479\u003c\/p\u003e \u003cp\u003eGlossary 503\u003c\/p\u003e \u003cp\u003eBibliography 511\u003c\/p\u003e \u003cp\u003eName Index 563\u003c\/p\u003e \u003cp\u003eSubject Index 573\u003c\/p\u003e","brand":"John Wiley and Sons Ltd","offers":[{"title":"Default Title","offer_id":48866715205975,"sku":"9781405182577","price":23.7,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781405182577.jpg?v=1722279874"},{"product_id":"introduction-to-econometrics-9781408093757","title":"Introduction to Econometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThis title has been adapted for use in Europe, the Middle East and Africa and has been tailored to meet the demands of today's lecturers and students.Jeffrey M. Wooldridge's Introduction to Econometrics shows how econometrics is a useful tool for answering questions in business, policy evaluation and forecasting environments. Packed with timely, relevant applications, the text incorporates close to 100 intriguing data sets, available in six formats, with appendices and questions available online.Unique organization pioneered by the author clearly presents applications for today's students. This comprehensive econometrics text pioneered the approach of explicitly covering cross-sectional applications first, followed by time series applications, and, ultimately, panel data applications in the advanced chapters.Practical application prepares students to use econometrics in business today. This unique, comprehensive text applies econometrics to actual real business problems, demonstrating \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e1. The Nature of Econometrics and Economic Data  2. The Simple Regression Model  3. Multiple Regression Analysis: Estimation 4. Multiple Regression Analysis: Inference 5. Multiple Regression Analysis: OLS Asymptotics  6. Multiple Regression Analysis: Further Issues  7. Multiple Regression Analysis with Qualitative Information: Binary (or Dummy) Variables  8. Heteroskedasticity  9. More on Specification and Data Issues 10. Basic Regression Analysis with Time Series Data  11. Further Issues in Using OLS with Time Series Data  12. Serial Correlation and Heteroskedasticity  13. Pooling Cross Sections Across Time: Simple Panel Data Methods  14. Advanced Panel Data Methods  15. Instrumental Variables Estimation and Two Stage Least Squares 16. Simultaneous Equations Models  17. Limited Dependant Variable Models and Sample Selection Corrections  18. Advanced Time Series Topics  19. Carrying Out an Empirical Project","brand":"Cengage Learning EMEA","offers":[{"title":"Default Title","offer_id":48866794701143,"sku":"9781408093757","price":999.99,"currency_code":"GBP","in_stock":false}]}],"url":"https:\/\/bookcurl.com\/collections\/econometrics-and-economic-statistics.oembed?page=15","provider":"Book Curl","version":"1.0","type":"link"}