Econometrics and economic statistics Books

Book SynopsisThis volume uses state of the art models from the frontier of macroeconomics to answer key questions about how the economy functions and how policy should be conducted. The contributions cover a wide range of issues in macroeconomics and macroeconomic policy. They combine high level mathematics with economic analysis, and highlight the need to update our mathematical toolbox in order to understand the increased complexity of the macroeconomic environment. The volume represents hard evidence of high research intensity in many fields of macroeconomics, and warns against interpreting the scope of macroeconomics too narrowly. The mainstream business cycle analysis, based on dynamic stochastic general equilibrium (DSGE) modelling of a particular type, has been criticised for its inability to predict or resolve the recent financial crisis. However, macroeconomic research on financial, information, and learning imperfections had not yet made their way into many of the pre-crisis DSGE models bTable of ContentsPART 1: FINANCIAL CRISIS AND RECOVERY ; PART 2: LEARNING, INCENTIVES, AND PUBLIC POLICY

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Book SynopsisProviding 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.Trade Review'. . . 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 *'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 *'. . . 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 *'. . . 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 *'. . . 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 *Table of Contents1 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

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Book SynopsisTo aid understanding of why saving rates differ across countries, this volume provides analyses of patterns of household saving behaviour in the G7 nations, except France. Micro data sets within each country and statistics on patterns of saving by age, income and other factors are included.Table of ContentsAcknowledgments Introduction 1 James M. Poterba 1: Household Data on Saving Behavior in Canada 11 John B. Burbidge, James B. Davies. 2: Personal Saving in the United States 57 Orazio P. Attanasio 3: Household Saving Behavior in Japan 125 Noriyuki Takayama, Yukinobu Kitamura. 4: Household Saving Behavior in the United Kingdom 169 James Banks, Richard Blundell. 5: Savings in Germany - Part 2: Behavior 207 Axel Borsch-Supan 6: Personal Saving in Italy 237 Tullio Jappelli, Marco Pagano. Contributors 269 Author Index 271 Subject Index 273

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Book SynopsisTable of ContentsPART 1: BASIC CONCEPTS OF OM AND VALUE CHAINS. 1. Operations Management and Value Chains. 2. Measuring Performance in Operations and Value Chains. 3. Operations Strategy. 4. Technology and Operations Management. PART 2: DESIGNING OPERATIONS AND SUPPLY CHAINS 5. Goods and Service Design. 6. Supply Chain Design. 7. Process Selection, Design, and Improvement. 8. Facility and Work Design. PART 3: MANAGING OPERATIONS AND SUPPLY CHAINS. 9. Forecasting and Demand Planning. 10. Capacity Management. 11. Process Analysis and Resource Utilization. 12. Managing Inventories in Supply Chains. 13. Supply Chain Management and Logistics. 14. Resource Management. 15. Operations Scheduling and Sequencing. 16. Quality Management. 17. Quality Control & SPC. 18. Lean Operating Systems. 19. Project Management. Supplement A: Probability and Statistics. Supplement B: Decision Analysis. Supplement C: Break-Even Analysis. Supplement D: Linear Optimization. Supplement E: The Transportation and Assignment Problems. Supplement F: Queuing Models. Supplement G: Simulation. Appendix A: Areas for the Cumulative Standard Normal Distribution. Appendix B: Factors for Control Charts. Endnotes. Glossary. Index.

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Book SynopsisGain a thorough understanding of today's supply management process from a managerial perspective with the current, complete coverage found in PURCHASING AND SUPPLY CHAIN MANAGEMENT, 7E. This edition draws from the authors' extensive first-hand experiences and relationships with executives and practitioners worldwide to highlight critical developments in the field. You examine recent advancements in supply chain fraud management, artificial intelligence, analytics, procurement automation and robotic process automation. New content also discusses supply chain fraud management and mitigation, emerging technology in real-time supply chain control towers, use of blockchain and the creation of Centers of Excellence. Carefully selected topics correspond to hiring requirements for supply chain positions today to help position you as a strong candidate. Former students agree that this book provides solid preparation for successfully entering today's workforce in procurement and supply managemenTable of ContentsPart 1: INTRODUCTION. 1. Introduction to Purchasing and Supply Chain Management. Part 2: PURCHASING OPERATIONS AND STRUCTURE. 2. The Purchasing Process. 3. Purchasing Policies and Procedures. 4. Supply Management Integration for Competitive Advantage. 5. Purchasing and Supply Management Organization. Part 3: STRATEGIC SOURCING. 6. Supply Management and Commodity Strategy Development. 7. Supplier Evaluation and Selection. 8. Supplier Quality Management. 9. Supplier Management and Development: Creating a World-Class Supply Base. 10. Worldwide Sourcing. Part 4: STRATEGIC SOURCING PROCESS. 11. Strategic Cost Management. 12. Purchasing and Supply Chain Analysis: Tools and Techniques. 13. Negotiation and Conflict Management. 14. Contract Management. 15. Purchasing Law and Ethics. Part 5: CRITICAL SUPPLY CHAIN ELEMENTS. 16. Lean Supply Chain Management. 17. Purchasing Services. 18. Supply Chain Information Systems and Electronic Sourcing. 19. Performance Measurement and Evaluation. Part 6: FUTURE DIRECTIONS. 20. Purchasing and Supply Strategy Trends.

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Book SynopsisAn Introduction to Economic Dynamics provides a framework for students to appreciate and understand the basic intuition behind economic models and to experiment with those models using simulation techniques in MATLAB. This book goes beyond the often-limited scope of other texts on economic models, which have largely focused on elucidating static equilibrium models. Comparative static analysis inhibits students from asking how the equilibrium position is achieved from an initial out-of-equilibrium position and limits their understanding of the dynamics that underlie such analysis. In this textbook, readers are introduced to ten well-established macroeconomic models including Keynesian multiplier models, Samuelson's multiplier and Solow's growth model and guided through the dynamical systems behind each model. Every chapter begins with an overview of the economic problem which the model is designed to help solve followed by an explanation of the mathematics of the moTrade Review "This book is a welcome addition to the literature on economic dynamics. Its clear writing style and the emphasis on coding using MATLAB® make it a compelling text for introducing undergraduate economics students to stability issues, cycles, and growth. The emphasis on both standard models like the Solow growth model and less standard ones such as the Goodwin growth cycle appeal to a broad spectrum of economists in the profession, and the highly competent authors have put years of experience with the material into a highly accessible textbook. I highly recommend the book to anyone willing to incorporate numerical methods into macroeconomic courses."Daniele Tavani, Associate Professor, Colorado State UniversityTable of Contents1. Introduction to Economic Dynamics 2. The Cobweb Model 3. Expectation Dynamics in the Cobweb Model 4. Keynesian Multiplier Model 5. The IS/LM Model 6. Debt, Deficit and Stabilization Policy 7. Expectation Dynamics and Hyperinflation 8. The Dornbusch Exchange Rate Overshooting Model 9. The Solow-Swan Growth Model 10. An Endogenous Growth Model 11. Business Cycles I: Samuelson’s Multiplier-Accelerator Model 12. Business Cycles II: The Real Business Cycle Model Appendix A. Difference and Differential Equations Appendix B. Matlab Codes

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Book SynopsisMachine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out-of-reach. Machine learning for factor investing: Python version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics.The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additivTrade Review"Machine learning is considered promising for investment management applications, yet the associated low signal to noise ratio presents a high bar for improving on the incumbent quant asset management tooling. The book of Coqueret and Guida is a treat for those who do not want to lose sight of the machine learning forest for the trees. Whether you are an academic scholar or a finance practitioner, you will learn just what you need to rigorously investigate machine learning techniques for factor investing applications, along with plenty of useful code snippets." -Harald Lohre, Executive Director of Research at Robeco and Honorary Researcher at Lancaster University Management School"Written by two experts on quantitative finance, this book covers everything from basic materials to advanced techniques in the field of quantitative investment strategies: data processing, alpha signal generation, portfolio optimization, backtesting and performance evaluation. Concrete examples related to asset management problems illustrate each machine learning technique, such as neural network, lasso regression, autoencoder or reinforcement learning. With more than 20 coding exercises and solutions provided in Python, this publication is a must for both students, academics and professionals who are looking for an up-to-date technical exposition on quantitative asset management from basic smart beta portfolios to enhanced alpha strategies including factor investing."-Thierry Roncalli, Head of Quantitative Portfolio Strategy at Amundi Institute, Amundi Asset ManagementTable of ContentsPart 1. Introduction 1. Notations and data 2. Introduction 3. Factor investing and asset pricing anomalies 4. Data preprocessing Part 2. Common supervised algorithms 5. Penalized regressions and sparse hedging for minimum variance portfolios 6. Tree-based methods 7. Neural networks 8. Support vector machines 9. Bayesian methods Part 3. From predictions to portfolios 10. Validating and tuning 11. Ensemble models 12. Portfolio backtesting Part 4. Further important topics 13. Interpretability 14. Two key concepts: causality and non-stationarity 15. Unsupervised learning 16. Reinforcement learning Part 5. Appendix 17. Data description 18. Solutions to exercises

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Book SynopsisHigh-Performance Computing (HPC) delivers higher computational performance to solve problems in science, engineering and finance. There are various HPC resources available for different needs, ranging from cloud computingâ that can be used without much expertise and expense â to more tailored hardware, such as Field-Programmable Gate Arrays (FPGAs) or D-Waveâs quantum computer systems. High-Performance Computing in Finance is the first book that provides a state-of-the-art introduction to HPC for finance, capturing both academically and practically relevant problems. Table of ContentsPart I: Computationally Expensive Problems in the Financial Industry 1. Computationally Expensive Problems in Investment Banking 2. Using Market Sentiment to Enhance Second-Order Stochastic Dominance Trading Models 3. The Alpha Engine: Designing an Automated Trading Algorithm 4. Portfolio Liquidation and Ambiguity Aversion 5. Challenges in Scenario Generation: Modeling Market and Non-Market Risks in Insurance Part II: Numerical Methods in Financial High-Performance Computing (HPC) 6. Finite Difference Methods for Medium- and High-Dimensional Derivative Pricing PDEs 7. Multilevel Monte Carlo Methods for Applications in Finance 8. Fourier and Wavelet Option Pricing Methods 9. A Practical Robust Long-Term Yield Curve Model 10. Algorithmic Differentiation 11. Case Studies of Real-Time Risk Management via Adjoint Algorithmic Differentiation (AAD) 12. Tackling Reinsurance Contract Optimization by Means of Evolutionary Algorithms and HPC 13. Evaluating Blockchain Implementation of Clearing and Settlement at the IATA Clearing House Part III: HPC Systems: Hardware, Software, and Data with Financial Applications 14. Supercomputers 15. Multiscale Dataflow Computing in Finance 16. Manycore Parallel Computation 17. Practitioner’s Guide on the Use of Cloud Computing in Finance 18. Blockchains and Distributed Ledgers in Retrospective and Perspective 19. Optimal Feature Selection Using a Quantum Annealer

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Book SynopsisThe Skeptical Environmentalist challenges widely held beliefs that the environmental situation is getting worse and worse. The author, himself a former member of Greenpeace, is critical of the way in which many environmental organisations make selective and misleading use of the scientific evidence. Using the best available statistical information from internationally recognised research institutes, BjÃrn Lomborg systematically examines a range of major environmental problems that feature prominently in headline news across the world. His arguments are presented in non-technical, accessible language and are carefully backed up by over 2500 footnotes allowing readers to check sources for themselves. Concluding that there are more reasons for optimism than pessimism, BjÃrn Lomborg stresses the need for clear-headed prioritisation of resources to tackle real, not imagined problems. The Skeptical Environmentalist offers readers a non-partisan stocktaking exercise that serves as a useful coTrade Review'This is one of the most valuable books on public policy - not merely on environmental policy - to have been written for the intelligent reader in the past ten years … The Skeptical Environmentalist is a triumph.' The Economist'… a superbly documented and readable book.' Wall Street Journal'The Skeptical Environmentalist should be read by every environmentalist, so that the appalling errors of fact the environmental movement has made in the past are not repeated. A brilliant and powerful book.' Matt Ridley, author of Genome'The Skeptical Environmentalist is perhaps the most important book about the environment since Rachel Carson's Silent Spring (1962) awakened the world to the dangers of unrestrained economic growth.' Jason Cowley, New Statesman'Bjorn Lomborg is an outstanding representative of the 'new breed' of political scientists - mathematically-skilled and computer-adept. In this book he shows himself also to be a hard-headed, empirically-oriented analyst. Surveying a vast amount of data and taking account of a wide range of more and less informed opinion about environmental threats facing the planet, he comes to a balanced assessment of which ones are real and which over-hyped. In vigorous informal style, he indicates what needs to be done to address the real environmental hazards - and what needs not to be done about those turning out to be pseudo-problems.' Jack Hirshleifer, University of California, Los Angeles'A new book is about to overturn our most basic assumptions about the world's environment. Far from going to hell in a handcart, it is improving by almost all measures. Those things not getting better are getting worse at a slower rate.' Anthony Browne, Observer'Lomborg's challenge will have to be met … he has given an important challenge to the scientific establishment that is not only good for science, but damned necessary to it.' Fortean Times'When Lomborg concludes that '… the loss of the world's rainforests, of fertile agricultural land, the ozone layer and of the climate balance are terrible …' I agree. But we also need debate, and this book provides us with that in generous amounts. If you, like I do, belong to the people who dare to think the world is making some progress, but always with mistakes to be corrected, this book makes important reading.' Lars Kristoferson, Secretary General, WWF Sweden'Lomborg's book has drawn considerable attention. Although it may cause problems for the more militant and political environmentalists, it should be welcomed by anyone genuinely concerned about the environment. … Lomborg's book sheds needed light on the real state of the world. I recommend it to anyone interested in our global environment … The Skeptical Environmentalist is the most valuable book available in many years on public policy in general, not only environmental policy in particular. It should be required reading for all legislators, government bureaucrats and corporate executives who preside over the ever-increasing array of environmental regulations and politics. John P. Bluemle, Geotimes'Whatever your standpoint, The Skeptical Environmentalist will make indispensable reading.' Mail on Sunday'A brilliant book … All in all, this is a must-have/must read book. Don't take my word for it. Check it out for yourself.' Clean Technologies and Environmental Policy'… his book is the Christmas present for a rational future.' The Chemical Engineer'… it should be welcomed by anyone genuinely concerned about the environment.' Environmental Geology'The Skeptical Environmentalist marks a critical environmental moment … We can forget those dreary old idols: Paul Ehrlich, Lester Brown with his Worldwatch Institute, Greenpeace and all the others. They have been exiled into the darkness. Eco-optimism can begin to rise over the Earth. After Lomborg, the environmental movement will begin to wither.' National Post'Bjorn Lomborg's book is hugely beneficial for a debate that has been one-sided.' The Financial TimesTable of ContentsIntroduction; Part I. The Litany: 1. Things are getting better; 2. Why do we hear so much bad news?; Part II. Human Welfare: 3. Measuring human welfare; 4. Life expectancy and health; 5. Food and hunger; 6. Prosperity; 7. Conclusion; Part III. Can Human Prosperity Continue?: 8. Are we living on borrowed time?; 9. Will we have enough food; 10. Forests - are we losing them?; 11. Energy; 12. Non-energy resources; 13. Water; 14. Conclusion; Part IV. Pollution: 15. Air pollution; 16. Acid rain and forest death; 17. Indoor air pollution; 18. Allergies and asthma; 19. Water pollution; 20. Waste: running out of space?; 21. Conclusion; Part V. Tomorrow's Problems: 22. Our chemical fears; 23. Biodiversity; 24. Global warming; Part VI. The Real State of the World: 25. Predicament or progress?; Notes; Bibliography.

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Book SynopsisThis book presents the econometric analysis of single-equation and simultaneous-equation models in which the jointly dependent variables can be continuous, categorical, or truncated. Despite the traditional emphasis on continuous variables in econometrics, many of the economic variables encountered in practice are categorical (those for which a suitable category can be found but where no actual measurement exists) or truncated (those that can be observed only in certain ranges). Such variables are involved, for example, in models of occupational choice, choice of tenure in housing, and choice of type of schooling. Models with regulated prices and rationing, and models for program evaluation, also represent areas of application for the techniques presented by the author.Trade Review'The book does an excellent job of surveying its chosen topics … such a comprehensive treatment as this book provides has previously been lacking. Thus the book fills an important gap in the literature. It will no doubt be widely read and used. It should be useful both to individuals interested in these topics at a theoretical level and those interested in applications. In the latter regard, an excellent feature of the book is that it contains a lot of empirical examples.' Journal of the American Statistical Association'… this book represents a significant contribution to the literature on limited dependent and qualitative variables. It should serve as a major reference for researchers doing empirical work with these models. It should also be useful to graduate students as well as econometric theorists.' American Journal of Agricultural Economics'To summarise, the book contains a very useful and clearly written account of many of the aspects of the limited dependent and qualitative variable models with an extensive use of empirical examples. The econometric issues raised by the models are neatly produced without taxing the reader too greatly. There will undoubtedly be other books appearing in the near future in the same area. It is to Maddala's credit that he has produced a book which, by virtue of its extensive coverage, will be very hard to beat.' The Economic JournalTable of ContentsPreface; 1. Introduction; 2. Discrete regression models; 3. Probabilistic-choice models; 4. Discriminant analysis; 5. Multivariate qualitative variables; 6. Censored and truncated regression models; 7. Simultaneous-equations models with truncated and censored variables; 8. Two-stage estimation methods; 9. Models with self-selectivity; 10. Disequilibrium models; 11. Some applications: unions and wages; Appendix: Some results on truncated distributions; Bibliography; Index.

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Book SynopsisSustainable investing has recently gained traction throughout the world. This trend has multiple sources, which span from genuine ethical concerns to hopes of performance boosting, and also encompass risk mitigation. The resulting appetite for green assets is impacting the decisions of many investors.  Perspectives in Sustainable Equity Investing is an up-to-date review of the academic literature on sustainable equity investing. It covers more than 800 academic sources grouped into six thematic chapters. Designed for corporate sustainability and financial management professionals, this is an ideal reference for ESG-driven financiers (both retail and institutional). Students majoring in finance or economics with some background or interest in ESG concerns would also find this compact overview useful. Key Features: Introduces the reader to terms and nomenclature used in the field. Surveys the link between sustainability and performance

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Book SynopsisThe subject of this textbook is to act as an introduction to data science / data analysis applied to finance, using R and its most recent and freely available extension libraries. The targeted academic level is undergrad students with a major in data science and/or finance and graduate students, and of course practitioners or professionals who need a desk reference. Assumes no prior knowledge of R The content has been tested in actual university classes Makes the reader proficient in advanced methods such as machine learning, time series analysis, principal component analysis and more Gives comprehensive and detailed explanations on how to use the most recent and free resources, such as financial and statistics libraries or open database on the internet Table of Contents1. Your Working Environment 2. Reading Data in R 3. Financial Data 4. Introduction to R 5. Functions 6. Data Transformation 7. Merging Data Sets 8. Graphing Using Ggplot 9. Returns and Returns-based Statistics 10. Portfolios 11. Modeling Returns and Simulations 12. Linear and Polynomial Regression 13. Fixed Income 14. Principal Component Analysis 15. Options 16. Value at Risk 17. Time Series Analysis 18. Machine Learning 19. Presenting the Results of Your Analyses 20. Appendix: Main Packages Seen in this Book

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Book SynopsisTwisted Logic: Puzzles, Paradoxes, and Big Questions delves into the intriguing world of twisted logic, where everyday conundrums, bewildering paradoxes, and life''s big questions are investigated and decoded. Crafted for the curious mind, this book sheds light on how our intuition and common sense can often mislead us. Without the need for technical jargon or mathematical prowess, it serves as your personal compass through fascinating intellectual landscapes and ultimate explorations. From the quirky corners of Bayesian reasoning to practical strategies in daily choices, this is your companion for a clearer way of thinking.Features: A comprehensive toolkit to refine your cognitive processes and avoid common pitfalls. Insights into the oddities of probability, strategy, and fate that govern our lives. A fresh perspective on everyday decisions and life''s larger dilemmas, including finding everything from a place to eat to a new h

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Book SynopsisComparison Between Continuous-Time and Discrete-Time: Event History Analysis With Stata elucidates the statistical concepts and empirical applications of both continuous-time and discrete-time event history models. Empirical scientists have increasingly collected data on the timing of events, with the understanding that events can occur at any point in time and may be recorded using different observation schemes. However, time-continuous processes are not always observed with fine-grained temporal resolution; in some cases, they are captured with larger observation intervals (monthly, yearly, biennially and quadrennially).This book introduces the need for discrete-time event history analysis methods and adeptly discusses the limitations of the discrete-time approach when compared to continuous-time models. It offers an in-depth comparison of these methods and sheds light on how they differ and align. By utilizing Stata and example data set, the book provides practical examples that showcase the implications of larger observation intervals in discrete-time applications.This book serves as an essential resource for researchers, students, and practitioners seeking to comprehend the nuances of event history analysis in both continuous and discrete-time frameworks. Its comprehensive exploration of statistical techniques and real-world applications will equip readers with a deeper understanding of the strengths and limitations of each method, thus enabling more informed and robust decision-making in empirical research.

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Book SynopsisThe use of simulation techniques has increased in importance in recent history, and simulation activities are an important resource for advanced preparation for the organization and execution of events. When formal mathematics is not enough, simulation may be the only option capable of approximating solutions. Simulation and Statistics with Excel: An Introduction to Business Students offers a non-rigorous and practical tour of the simulation procedure on computers, using a versatile and accessible resource, the Microsoft Excel spreadsheet. This book covers the concepts essential to understanding the basic principles and approaches of statistical simulation, allowing for the study of complex systems. Aimed at students in business and operational research beginning to use simulation as an instrument for understanding existing or proposed processes, this book will lay solid foundations in understanding simulation experimentation.Key Features:Provi

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Book SynopsisRecent years have seen an explosion in the volume and variety of data collected in scientific disciplines from astronomy to genetics and industrial settings ranging from Amazon to Uber. This graduate text equips readers in statistics, machine learning, and related fields to understand, apply, and adapt modern methods suited to large-scale data.Trade Review'Non-asymptotic, high-dimensional theory is critical for modern statistics and machine learning. This book is unique in providing a crystal clear, complete and unified treatment of the area. With topics ranging from concentration of measure to graphical models, the author weaves together probability theory and its applications to statistics. Ideal for graduate students and researchers. This will surely be the standard reference on the topic for many years.' Larry Wasserman, Carnegie Mellon University, Pennsylvania'Martin J. Wainwright brings his large box of analytical power tools to bear on the problems of the day - the analysis of models for wide data. A broad knowledge of this new area combines with his powerful analytical skills to deliver this impressive and intimidating work - bound to be an essential reference for all the brave souls that try their hand.' Trevor Hastie, Stanford University, California'This book provides an excellent treatment of perhaps the fastest growing area within high-dimensional theoretical statistics - non-asymptotic theory that seeks to provide probabilistic bounds on estimators as a function of sample size and dimension. It offers the most thorough, clear, and engaging coverage of this area to date, and is thus poised to become the definitive reference and textbook on this topic.' Genevera Allen, William Marsh Rice University, Texas'Statistical theory and practice have undergone a renaissance in the past two decades, with intensive study of high-dimensional data analysis. No researcher has deepened our understanding of high-dimensional statistics more than Martin Wainwright. This book brings the signature clarity and incisiveness of his published research into book form. It will be a fantastic resource for both beginning students and seasoned researchers, as the field continues to make exciting breakthroughs.' John Lafferty, Yale University, Connecticut'This is an outstanding book on high-dimensional statistics, written by a creative and celebrated researcher in the field. It gives comprehensive treatments on many important topics in statistical machine learning and, furthermore, is self-contained, from introductory materials to most updated results on various research frontiers. This book is a must-read for those who wish to learn and to develop modern statistical machine theory, methods and algorithms.' Jianqing Fan, Princeton University, New Jersey'This book provides an in-depth mathematical treatment and methodological intuition of high-dimensional statistics. The main technical tools from probability theory are carefully developed and the construction and analysis of statistical methods and algorithms for high-dimensional problems is presented in an outstandingly clear way. Martin J. Wainwright has written a truly exceptional, inspiring and beautiful masterpiece!' Peter Bühlmann, Eidgenössische Technische Hochschule Zürich'This new book by Martin J. Wainwright covers modern topics in high-dimensional statistical inference, and focuses primarily on explicit non-asymptotic results related to sparsity and non-parametric estimation. This is a must-read for all graduate students in mathematical statistics and theoretical machine learning, both for the breadth of recent advances it covers and the depth of results which are presented. The exposition is outstandingly clear, starting from the first introductory chapters on the necessary probabilistic tools. Then, the book covers state-of-the-art advances in high-dimensional statistics, with always a clever choice of results which have the perfect mix of significance and mathematical depth.' Francis Bach, INRIA Paris'Wainwright's book on those parts of probability theory and mathematical statistics critical to understanding of the new phenomena encountered in high dimensions is marked by the clarity of its presentation and the depth to which it travels. In every chapter he starts with intuitive examples and simulations which are systematically developed either into powerful mathematical tools or complete answers to fundamental questions of inference. It is not easy, but elegant and rewarding whether read systematically or dipped into as a reference.' Peter Bickel, University of California, Berkeley'… this is a very valuable book, covering a variety of important topics, self-contained and nicely written.' Fabio Mainardi, MAA Reviews'This is an excellent book. It provides a lucid, accessible and in-depth treatment of nonasymptotic high-dimensional statistical theory, which is critical as the underpinning of modern statistics and machine learning. It succeeds brilliantly in providing a self-contained overview of high-dimensional statistics, suitable for use in formal courses or for self-study by graduate-level students or researchers. The treatment is outstandingly clear and engaging, and the production is first-rate. It will quickly become essential reading and the key reference text in the field.' G. Alastair Young, International Statistical Review'Martin Wainwright takes great care to polish every sentence of each part of the book. He introduces state-of-the-art theory in every chapter, as should probably be expected from an acknowledged specialist of the field. But it is certainly an enormous amount of work to organize all these results in a complete, coherent, rigorous yet easy-to-follow theory. I am simply amazed by the quality of the writing. The explanations on the motivations (Chapter 1) and on the core of the theory are extremely pedagogical. The proofs of the main results are rigorous and complete, but most of them are also built in a way that makes them seem easier to the reader than they actually are. This is the kind of magic only a few authors are capable of.' Pierre Alquier, MatSciNet'... provides a masterful exposition of various mathematical tools that are becoming increasingly common in the analysis of contemporary statistical problems. In addition to providing a rigorous and comprehensive overview of these tools, the author delves into the details of many illustrative examples to provide a convincing case for the general usefulness of the methods that are introduced.' Po-Ling Lo, Bulletin of the American Mathematical Society'An excellent statistical masterpiece is in the hands of the reader, which is a must read book for all graduate students in both mathematical statistics and mathematical machine learning.' Rózsa Horváth-Bokor, ZB Math ReviewsTable of Contents1. Introduction; 2. Basic tail and concentration bounds; 3. Concentration of measure; 4. Uniform laws of large numbers; 5. Metric entropy and its uses; 6. Random matrices and covariance estimation; 7. Sparse linear models in high dimensions; 8. Principal component analysis in high dimensions; 9. Decomposability and restricted strong convexity; 10. Matrix estimation with rank constraints; 11. Graphical models for high-dimensional data; 12. Reproducing kernel Hilbert spaces; 13. Nonparametric least squares; 14. Localization and uniform laws; 15. Minimax lower bounds; References; Author index; Subject index.

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Book SynopsisWho decides how official statistics are produced? Do politicians have control or are decisions left to independent statistical agencies? Interviews with statisticians in Australia, Canada, Sweden, the UK and the USA reveal that the power over statistics is distributed differently across countries, and this book explains why.Trade Review'In this dramaturgical sociology of statisticians in five liberal democracies, Cosmo Howard has produced a wonderfully rich set of insights into expertise, authority and the public performances of statistical systems. This enjoyable and in-depth examination uncovers the struggles for credibility of both politicians and statisticians, in the twenty-first century. In doing so it highlights the importance of performative strategies for all experts.' Jenny M Lewis, Professor, University of Melbourne, AustraliaTable of ContentsIntroduction; 1. Credibility and official statistics; 2. Australia; 3. Canada; 4. Sweden; 5. The United Kingdom; 6. The United States of America; 7. Conclusions; Notes; List of cited interviews by order of appearance; References; Index.

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Book SynopsisThis is the seventh book in a series of discussions about the great minds in the history and theory of finance.Table of Contents1) 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.

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Book SynopsisDelivering cutting-edge coverage that includes the latest thinking and practices from the field, QUALITY AND PERFORMANCE EXCELLENCE, 8e presents the basic principles and tools associated with quality and performance excellence. Packed with relevant, real-world examples, the text thoroughly illustrates how these principles and methods have been put into effect in a variety of organizations. It also highlights the relationship between basic principles and the popular theories and models studied in management courses. The eighth edition reflects the 2015-16 Baldrige criteria and includes new boxed features, experiential exercises, and up-to-date case studies that give you practical experience working with real-world issues. Many cases focus on large and small companies in manufacturing and service industries in North and South America, Europe, and Asia-Pacific. In addition, chapters now open with a Performance Excellence Profile highlighting a recent Baldrige recipient.Table of ContentsPart I: FOUNDATIONS OF QUALITY AND PERFORMANCE EXCELLENCE. 1. Introduction to Quality and Performance Excellence. 2. Frameworks for Quality and Performance Excellence. 3. Tools and Techniques for Quality Design and Control. 4. Tools and Techniques for Quality Improvement. Part II: PERFORMANCE EXCELLENCE, STRATEGY, AND ORGANIZATIONAL THEORY. 5. Competitive Advantage and Strategic Management for Performance Excellence. 6. Quality in Customer-Supplier Relationships. 7. Designing Organizations for Performance Excellence. Part III: PERFORMANCE EXCELLENCE AND ORGANIZATIONAL BEHAVIOR. 8. Quality Teamwork. 9. Engagement, Empowerment, and Motivation. Part IV: LEADERSHIP AND ORGANIZATIONAL CHANGE. 10. Leadership for Performance Excellence. 11. Performance Excellence and Organizational Change.

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Book SynopsisRisk Analysis in Engineering and Economics is required reading for decision making under conditions of uncertainty. The author describes the fundamental concepts, techniques, and applications of the subject in a style tailored to meet the needs of students and practitioners of engineering, science, economics, and finance. Drawing on his extensive experience in uncertainty and risk modeling and analysis, the author covers everything from basic theory and key computational algorithms to data needs, sources, and collection. He emphasizes practical use of the methods presented and carefully examines the limitations, advantages, and disadvantages of each to help readers translate the discussed techniques into real-world solutions.This Second Edition: Introduces the topic of risk finance Incorporates homeland security applications throughout Offers additional material on predictive risk management Includes a wealth of neTrade ReviewPraise for the First Edition "This book ambitiously tackles risk analysis from the ground up. … an excellent reference for individuals studying in a variety of risk-related disciplines. … enlightening [examples]…attractive to students approaching risk studies from a variety of substantive backgrounds. … Advanced students are likely to find Ayyub's treatments…exceedingly valuable."—Journal of the American Statistical Association, June 2004, Vol. 99, No. 466 "… a good textbook and reference. The mix of engineering and economics is well balanced. … Valuable for students after college and useful for professional engineers."—Technometrics, August 2004, Vol. 46, No. 3 Praise for the Second Edition "… one of a kind in the market. … I am not aware of books that cover the subject in as comprehensive a manner."—Professor Nii O. Attoh-Okine, University of Delaware, Newark, USA Table of ContentsIntroduction. Risk Analysis Methods. System Definition and Structure. Reliability Assessment. Failure Consequences and Severity. Engineering Economics and Finance. Risk Control Methods. Data for Risk Studies. Fundamentals of Probability and Statistics. Failure Data. References and Bibliography. Index.

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Book SynopsisIntroduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data, both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework.The book can be used as textbook for a semester-long course on FDA for advanced undergraduate or MS statistics majors, as well as for MS and PhD students in other disciplines, including applied mathematics, environmental science, public health, medical research, geophysical sciences and economics. It can also be used for self-study and as a reference for researchers in those fields who wish to acquire solid understanding of FDA methodology and practical guidance for its implementation. Each chapter contains plentiful examples of relevant R code and theoretical and data analytic problems.The material of the book can be roughly divided into four parts of approximaTrade Review"This well-written book provides a great and intuitive introduction to functional data analysis (FDA) which has emerged as an important area in statistics and found tons of scientific applications...This book succeeds at introducing this novel statistical concept and methodology while keeps the level of mathematical and statistical sophistication required to understand at the level of an introductory graduate-level course, which makes for pleasant reading. A nice feature of the book is its strong focus on implementation using R, which makes it a great candidate of textbooks or reference books for (master-level) graduate students and applied researchers...Some unique features of this book as compared to existing ones include (1) its strong focus on implementation using R; (2) chapters on Sparse FDA, generalized functional linear models, functional time series, and spatial functional data; (3) well-designed exercises that can be used as homework problems." ~Xianyang Zhang, Texas A&M University"The main advantage of the book is its emphasis introducing the material through realistic examples and computational tools, while also providing mathematical guidance for the methodologies. Also, important topics like functional time series and spatial functional data are not adequately covered in comparable texts like Ramsay and Silverman, Ramsay and Hooker, Ferraty and Vieu, and Hsing and Eubank. In that respect, the book offers additional and practically relevant material and perspective." ~Debashis Paul, University of California, Davis"The classic tools from the field of functional data analysis are introduced comprehensively and immediately put into a framework of potential application. I would probably advise any reader that is new to functional data analysis to start by reading this book." ~Claudia Klüppelberg, Technische Universität München"Being more advanced and up to date than the Ramsay and Silverman, it complements various topics that are just briefly mentioned or not covered at all by Ramsay and Silverman." ~Laura Sangali, Politecnico di Milano"As a relatively young subfield of statistics, functional data analysis (FDA) has not had a large glut of textbooks pertaining to it. The most famous of the FDA books is the classic text by J. O. Ramsay and B. W. Silverman [Functional data analysis, Springer Ser. Statist., Springer, New York, 1997; second edition, 2005; MR2168993], which introduced many statisticians to the area. Ramsay and Silverman [Applied functional data analysis, Springer Ser. Statist., Springer, New York, 2002; MR1910407] provided a useful collection of FDA case studies, and Ramsay, G. Hooker and S. Graves [Func-tional data analysis with R and MATLAB, Use R, Springer, New York, 2009, doi:10. 1007/978-0-387-98185-7] presented R and MATLAB code for analyzing real functional data sets. [F. Ferraty and P. Vieu, Nonparametric functional data analysis, Springer Ser. Statist., Springer, New York, 2006; MR2229687] and [T. Hsing and R. L. Eubank, Theoretical foundations of functional data analysis, with an introduction to linear opera- tors, Wiley Ser. Probab. Stat., Wiley, Chichester, 2015; MR3379106] are well-respected theoretical presentations of FDA.This book by Kokoszka and Reimherr provides a nice mix of foundational material, accessible theory, and practical examples (including much R code). It is a valuable addition to the FDA literature, and is perhaps an ideal choice of a course textbook for either an undergraduate or graduate course in FDA, whereas several of the other textbooks are more valuable as references for researchers and practitioners than as tutorials for learners. At the end of each chapter is a nice variety of problems that instructors could use for homework assignments.Chapter 1 introduces basic terminology related to FDA, such as the ubiquitous tool of basis expansion and the distinction between dense and sparse functional data. Summary statistics and plots (sample mean and covariance functions, principal components analysis (PCA), functional boxplots) for FDA are briey presented. Chapter 2 continues basic FDA topics with a discussion of derivative information, penalized smoothing, and alignment/registration of curves.The theoretical underpinnings of FDA are presented quickly in Chapter 3, where topics such as square integrable functions, random functions following some distribution, and operator theory are defined briey. A fuller coverage of theoretical concerns is saved for (the optional in a course setting) Chapters 10 and 11. The heart of the book is Chapters 4 through 9, which cover functional linear models in detail, before moving on to specialized FDA topics such as sparse FDA, functionaltime series, and spatial functional data. Scalar-on-function regression, in which the response is a scalar and the predictor is a function, is treated in Chapter 4, and illustrated via the use of the refund package in R. Nonlinear scalar-on-function regression is briey mentioned. Chapter 5 covers both the function-on-scalar regression case and the fully functional regression model in which both response and predictor are functions. Testing and validation of the functional linear model are also shown. Chapter 6 covers functional generalized linear models (GLMs) which have a nonnormal scalar response and a functional predictor. The somewhatnebulous situation with functional-response GLMs is briey covered as well. The next chapter deals with sparse functional data, and presents methods for mean function estimation, covariance function estimation, PCA, and regression in the sparse case when relatively few points are measured for each observed curve. Functional time series occur when the sample functions are observed sequentially over time rather than cross-sectionally. The assumption of independent functional data fails in this case, and Chapter 8 presents a functional autoregressive model for such data that can be used for forecasting. Spatial functional data may commonly be encountered in geostatistics when curves are observed both over time and at various spatial locations. Chapter 9 discusses models for such data and prediction using functional kriging. Chapter 12 discusses treating a functional data set as a sample from some population of functions and performing inference on the population. Of particular interest are the methods presented for formal hypothesis tests and confidence bands about the population mean function.Clustering and classification of functional data are not discussed in detail in thisbook, nor is FDA on manifolds, although references are given to guide readers to recentresearch in these areas."~David Benner HitchcockTable of ContentsFirst steps in the analysis of functional data Basis expansions Sample mean and covariance Principal component functions Analysis of BOA stock returns Diffusion tensor imaging Problems Further topics in exploratory FDA Derivatives Penalized smoothing Curve alignment Further reading Problems Mathematical framework for functional data Square integrable functions Random functions Linear transformations Scalar- on - function regressionExamples Review of standard regression theory Difficulties specific to functional regression Estimation through a basis expansion Estimation with a roughness penalty Regression on functional principal components Implementation in the refund package Nonlinear scalar-on-function regression Problems Functional response models Least squares estimation and application to angular motionPenalized least squares estimation Functional regressors Penalized estimation in the refund package Estimation based on functional principal components Test of no effectVerification of the validity of a functional linear model Extensions and further reading Problems Functional generalized linear models Background Scalar-on-function GLM's Functional response GLM Implementation in the refund package Application to DTI Further reading Problems Sparse FDA Introduction Mean function estimationCovariance function estimation Sparse functional PCA Sparse functional regression Problems Functional time seriesFundamental concepts of time series analysis Functional autoregressive process Forecasting with the Hyndman-Ullah methodForecasting with multivariate predictors Long-run covariance function Testing stationarity of functional time series Generation and estimation of the FAR(1) model using package fda Conditions for the existence of the FAR(1) process Further reading and other topics Problems Spatial functional data and modelsFundamental concepts of spatial statistics Functional spatial fields Functional kriging Mean function estimation Implementation in the R package geofd Other topics and further reading Problems Elements of Hilbert space theoryHilbert space Projections and orthonormal sets Linear operators Basics of spectral theory Tensors ProblemsRandom functions Random elements in metric spaces Expectation and covariance in a Hilbert space Gaussian functions and limit theorems Functional principal components ProblemsInference from a random sampleConsistency of sample mean and covariance functions Estimated functional principal components Asymptotic normality Hypothesis testing about the mean Confidence bands for the mean Application to BOA cumulative returns Proof of Theorem Problems

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Book SynopsisAspects of environmental change are some of the greatest challenges faced by policymakers today. The key issues addressed by environmental science are often empirical, and in many instances very detailed, sizable datasets are available. Researchers in this field should have a solid understanding of the econometric tools best suited for analysis of these data. While complex and expensive physical models of the environment exist, it is becoming increasingly clear that reduced-form econometric models have an important role to play in modeling environmental phenomena. In short, successful environmental modeling does not necessarily require a structural model, but the econometric methods underlying a reduced-form approach must be competently executed. Environmental Econometrics Using Stata provides an important starting point for this journey by presenting a broad range of applied econometric techniques for environmental econometrics and illustrating how they can be applied in Stata. The emphasis is not only on how to formulate and fit models in Stata but also on the need to use a wide range of diagnostic tests in order to validate the results of estimation and subsequent policy conclusions. This focus on careful, reproducible research should be appreciated by academic and non-academic researchers who are seeking to produce credible, defensible conclusions about key issues in environmental science. Table of Contents1 Introduction 2 Linear regression models 3 Beyond ordinary least squares 4 Introducing dynamics 5 Multivariate time-series models 6 Testing for nonstationarity 7 Modeling nonstationary variables 8 Forecasting 9 Structural time-series models 10 Nonlinear time-series models 11 Modeling time-varying variance 12 Longitudinal data models 13 Spatial models 14 Discrete dependent variables 15 Fractional integration A Using Stata

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Book SynopsisThis 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. 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.Table of ContentsIntroduction.- 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&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.

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Book SynopsisThis book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention to the deficiencies of standard statistical tools when used in the high-dimensional setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional asymptotics in an understandable and reader-friendly way. The aim of this book is to inspire applied statisticians, econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work.Table of ContentsForeword.- 1 Introduction.- 2 Traditional Estimators and Standard Asymptotics.- 3 Finite Sample Performance of Traditional Estimators.- 4 Traditional Estimators and High-Dimensional Asymptotics.- 5 Summary and Outlook.- Appendices.

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Book SynopsisThis Fourth Edition updates the "Solutions Manual for Econometrics" to match the Sixth Edition of the Econometrics textbook. It adds problems and solutions using latest software versions of Stata and EViews. Special features include empirical examples replicated using EViews, Stata as well as SAS. The book offers rigorous proofs and treatment of difficult econometrics concepts in a simple and clear way, and provides the reader with both applied and theoretical econometrics problems along with their solutions. These should prove useful to students and instructors using this book.Table of ContentsWhat Is Econometrics?.- A Review of Some Basic Statistical Concepts.- Simple Linear Regression.- Multiple Regression Analysis.- Violations of the Classical Assumptions.- Distributed Lags and Dynamic Models.- 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.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisPartial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification. This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software’s SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the “how-tos” of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.Table of ContentsAn Introduction to Structural Equation Modeling.- Introduction to R and RStudio.- Introduction to SEMinR.- Evaluation of Reflective Measurement Models.- Evaluation of Formative Measurement Models.- Evaluation of the Structural Model.- Mediation Analysis.- Moderation Analysis.

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Book SynopsisThis 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.Table of Contents1. 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.

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Book SynopsisThis book is a collection of state-of-the-art papers on the properties of business cycles and financial analysis. The individual contributions cover new advances in Markov-switching models with applications to business cycle research and finance. The introduction surveys the existing methods and new results of the last decade. Individual chapters study features of the U. S. and European business cycles with particular focus on the role of monetary policy, oil shocks and co­ movements among key variables. The short-run versus long-run consequences of an economic recession are also discussed. Another area that is featured is an extensive analysis of currency crises and the possibility of bubbles or fads in stock prices. A concluding chapter offers useful new results on testing for this kind of regime-switching behaviour. Overall, the book provides a state-of-the-art over­ view of new directions in methods and results for estimation and inference based on the use of Markov-switching time-series analysis. A special feature of the book is that it includes an illustration of a wide range of applications based on a common methodology. It is expected that the theme of the book will be of particular interest to the macroeconomics readers as well as econometrics professionals, scholars and graduate students. We wish to express our gratitude to the authors for their strong contributions and the reviewers for their assistance and careful attention to detail in their reports.Table of ContentsI Introduction and Overview.- New directions in business cycle research and financial analysis.- II The Business Cycle in the U.S..- Permanent and transitory components of recessions.- Can oil shocks explain asymmetries in the US Business Cycle?.- Markov switching in disaggregate unemployment rates.- III The Business Cycle in Other Countries.- A Markov-switching vector equilibrium correction model of the UK labour market.- Plucking models of business cycle fluctuations: Evidence from the G-7 countries.- IV Financial Applications.- Is there an asymmetric effect of monetary policy over time? A Bayesian analysis using Austrian data.- A regime-switching approach to the study of speculative attacks: A focus on EMS crises.- Fads or bubbles?.- Improving GARCH volatility forecasts with regime-switching GARCH.- V Methodological Contribution.- Power issues when testing the Markov switching model with the sup likelihood ratio test using U.S. Output.- List of Referees.

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Book SynopsisThis book provides advanced theoretical and applied tools for the implementation of modern micro-econometric techniques in evidence-based program evaluation for the social sciences. The author presents a comprehensive toolbox for designing rigorous and effective ex-post program evaluation using the statistical software package Stata. For each method, a statistical presentation is developed, followed by a practical estimation of the treatment effects. By using both real and simulated data, readers will become familiar with evaluation techniques, such as regression-adjustment, matching, difference-in-differences, instrumental-variables, regression-discontinuity-design, and synthetic control method, and are given practical guidelines for selecting and applying suitable methods for specific policy contexts.The second revised and extended edition features two new chapters on some recent development of difference-in-differences. Specifically, chapter 5 introduces advanced difference-in-differences methods when many times are available and treatment can be either time-varying or fixed at a specific time. Chapter 6 introduces the synthetic control method, a treatment effect estimation approach suitable when only one unit is treated. Both chapters present applications using the software Stata.Table of ContentsChapter 1. An Introduction to the Econometrics of Program Evaluation.- Chapter 2. Methods Based on Selection on Observables.- Chapter 3. Methods Based on Selection on Unobservables.- Chapter 4. Local Average Treatment Effect and Regression-Discontinuity-Design.- Chapter 5. Difference-in-differences with many pre- and post-treatment times.- Chapter 6. Synthetic Control Method

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Book SynopsisThis volume provides a practical guide to building and using simulation models for international trade theory and policy. Through a sequence of carefully constructed and fully documented programs, the volume illustrates how numerical simulation can be used to analyze a wide array of problems. Modern computable general equilibrium (CGE) models for trade policy are challenging in their complexity, but can be thought of as constructions of much simpler building blocks. By developing the building blocks in a consistent manner, and gradually putting them together in more complex and interesting ways, the volume makes CGE accessible to anyone with a background in microeconomics/trade theory. The volume will be useful to graduate students and researchers in international trade looking for a detailed guide to building simulation models and to developing the skill set necessary to enter into the world of CGE modeling.Table of ContentsIntroduction; Utility Maximization; Cost Minimization; Long-Run Production; Short-Run Production; Dual Approach; Transition; Higher Dimensions; Autarky; Small Country Trading Equilibrium; Large Country Trading Equilibrium; Armington Preferences; Intermediate Inputs; Joint Production; Non-Traded Goods; Tariffs and Other Distortions; Single Country Competitive CGE; Imperfect Competition; Multiple Households; Imperfect Factor Mobility; Two Country Trading Equilibrium; Higher Dimensions and Trade; Multi-Country Competitive CGE; Appendices.

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Book SynopsisBy means of a series of exchanges between the editors and leaders from the philosophy of science, statistics and economics, this volume offers a cumulative introduction connecting problems of traditional philosophy of science to problems of inference in statistical and empirical modelling practice.Trade Review'Mayo and Spanos's collection has injected new ideas into the study of scientific inference. This book offers a welcome bridge between current philosophy of science and scientific practice, providing the reader with new insights on important topics such as statistical inference, reliability, theory testing, causal modeling, and the relation between theory and experiment. The book will have a wide and enthusiastic readership among philosophers and scientists.' Cristina Bicchieri, University of Pennsylvania'Error and Inference straddles philosophy and practice; its lessons should be taken seriously in both. The editors suppose that venerable philosophical problems surrounding induction, scientific inference, and objectivity can be solved. The essays in the book give support to that perspective. They also show that pressing practical problems of scientific inference and testing gain marked benefit from careful attention to philosophers' accounts of what makes for evidence, rationality, and objectivity.' Nancy Cartwright, London School of Economics'The error-probabilistic approach developed by Deborah Mayo and Aris Spanos is the main alternative to Bayesianism in contemporary philosophy of science. In this superb volume Mayo and Spanos face their critics and show that error-probabilism is able to solve most theoretical puzzles of statistical testing. If some issue in the field of inductive inference is bothering you, you will probably find an answer in this book.' Francesco Guala, University of Milan'Mayo, an empirically minded philosopher, and Spanos, a philosophically minded economist, have succeeded beautifully in orchestrating a lively debate over methodological issues related to statistics and empirical testing that - unlike too much of the philosophy of science - speaks to the genuine issues that the practitioners of empirical sciences face daily. Their important volume deserves a broad readership.' Kevin Hoover, Duke University'Mayo and Spanos continue their campaign to bring confirmation theory face-to-face with the methods of scientists, and now extend it to the history of science and to general theories too. This book begins with a fine introduction to Mayo's error-statistical approach that makes the book a useful teaching tool. But then it carries forward the discussion of this approach with challenging papers from Glymour, Laudan, Achinstein, Worrall, and others.' Alexander Rosenberg, Duke University'This is a wonderful volume. It contains original and stimulating essays by leading figures from both philosophy and statistics on notions of evidence and testing; on how these interact with ideas about causation, explanation, and scientific rationality; and much more besides. The volume also features detailed and illuminating exchanges between the contributors. A must-read for anyone with an interest in these topics.' Jim Woodward, California Institute of TechnologyTable of ContentsPart I. Introduction and Background: 1. Philosophy of methodological practice Deborah Mayo; 2. Error statistical philosophy Deborah Mayo and Aris Spanos; Part II: 3. Severe testing, error statistics, and the growth of theoretical knowledge Deborah Mayo; Part III: 4. Can scientific theories be warranted? Alan Chalmers; 5. Can scientific theories be warranted with severity? Exchanges with Alan Chalmers Deborah Mayo; Part IV: 6. Critical rationalism, explanation and severe tests Alan Musgrave; 7. Towards progressive critical rationalism: exchanges with Alan Musgrave Deborah Mayo; Part V: 8. Error, tests and theory-confirmation John Worrall; 9. Has Worrall saved his theory (on ad hoc saves) in a non ad hoc manner? Exchanges with Worrall Deborah Mayo; Part VI: 10. Mill's sins, or Mayo's errors? Peter Achinstein; 11. Sins of the Bayesian epistemologist: exchanges with Achinstein Deborah Mayo; Part VII: 12. Theory testing in economics and the error statistical perspective Aris Spanos; Part VIII: 13. Frequentist statistics as a theory of inductive inference Deborah Mayo and David Cox; 14. Objectivity and conditionality in Frequentist inference David Cox and Deborah Mayo; 15. An error in the argument from WCP and S to the SLP Deborah Mayo; 16. On a new philosophy of Frequentist inference: exchanges with Cox and Mayo Aris Spanos; Part IX: 17. Explanation and truth Clark Glymour; 18. Explanation and testing: exchanges with Glymour Deborah Mayo; 19. Graphical causal modeling and error statistics: exchanges with Glymour Aris Spanos; Part X: 20. Legal epistemology: the anomaly of affirmative defenses Larry Laudan; 21. Error and the law: exchanges with Laudan Deborah Mayo.

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