Econometrics and economic statistics Books
Cambridge University Press Essays in Econometrics Collected Papers of Clive W J Granger Volume 2 Econometric Society Monographs Series Number 33
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£145.35
Cambridge University Press Essays in Econometrics Collected Papers of Clive W J Granger Volume 2 Econometric Society Monographs Series Number 33
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£36.87
Cambridge University Press Patterns of Speculation
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£77.39
Cambridge University Press Econometric Theory and Practice
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£43.70
Cambridge University Press Semiparametric Regression for the Applied Econometrician
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£50.35
Cambridge University Press The Role of Social Capital in Development An Empirical Assessment
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£91.20
Cambridge University Press The Structural Econometric Time Series Analysis Approach
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£85.50
Cambridge University Press Essays in Panel Data Econometrics
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£74.10
Cambridge University Press Logit Models from Economics and Other Fields
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£74.09
Cambridge University Press Time Series Models for Business and Economic Forecasting
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£85.49
Cambridge University Press Statistics Econometrics and Forecasting
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£103.55
Cambridge University Press Stochastic Optimization in Continuous Time
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£51.30
Cambridge University Press Applied Time Series Econometrics
Book SynopsisThe cointegration revolution has had a substantial impact on applied analysis. The methods for conducting this analysis are sketched out, reminding the reader of the ideas underlying them and giving sufficient background for empirical work. The treatment can be used as a textbook for courses on applied time series econometrics.Table of ContentsPreface; Notation and abbreviations; List of contributors; Part I. Initial Tasks and Overview Helmut Lütkepohl: 1. Introduction; 2. Setting up an econometric project; 3. Getting data; 4. Data handling; 5. Outline of chapters; Part II. Univariate Time Series Analysis Helmut Lütkepohl: 6. Characteristics of time series; 7. Stationary and integrated stochastic processes; 8. Some popular time series models; 9. Parameter estimation; 10. Model specification; 11. Model checking; 12. Unit root tests; 13. Forecasting univariate time series; 14. Examples; 15. Where to go from here; Part III. Vector Autoregressive and Vector Error Correction Models Helmut Lütkepohl: 16. Introduction; 17. VARs and VECMs; 18. Estimation; 19. Model specification; 20. Model checking; 21. Forecasting VAR processes and VECMs; 22. Granger-causality analysis; 23. An example; 24. Extensions; Part IV. Structural Vector Autoregressive Modelling and Impulse Responses Jörg Breitung, Ralf Brüggemann and Helmut Lütkepohl: 25. Introduction; 26. The models; 27. Impulse response analysis; 28. Estimation of structural parameters; 29. Statistical inference for impulse responses; 30. Forecast error variance decomposition; 31. Examples; 32. Conclusions; Part V. Conditional Heteroskedasticity Helmut Herwartz: 33. Stylized facts of empirical price processes; 34. Univariate GARCH models; 35. Multivariate GARCH models; Part VI. Smooth Transition Regression Modelling Timo Teräsvirta: 36. Introduction; 37. The model; 38. The modelling cycle; 39. Two empirical examples; 40. Final remarks; Part VII. Nonparametric Time Series Modelling Rolf Tschernig: 41. Introduction; 42. Local linear estimation; 43. Bandwidth and lag selection; 44. Diagnostics; 45. Modelling the conditional volatility; 46. Local linear seasonal modelling; 47. Example I: average weekly working hours in the United States; 48. Example II: XETRA dax index; Part VIII. The Software JMulTi Markus Krätzig: 49. Introduction to JMulTi; 50. Numbers, dates and variables in JMulTi; 51. Handling data sets; 52. Selecting, transforming and creating time series; 53. Managing variables in JMulTi; 54. Notes for econometric software developers; 55. Conclusion; References; Index.
£103.11
Cambridge University Press Foundations of Dynamic Economic Analysis
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£85.49
Cambridge University Press Introductory Econometrics Using Monte Carlo Simulation with Microsoft Excel By Humberto Barreto Frank Howland March 2006
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£67.44
Cambridge University Press Weather Derivative Valuation
Book SynopsisCovers the meteorological, statistical, financial and mathematical issues that arise in the pricing and risk management of weather derivatives. Written by consultants who work within the weather derivative industry, this book, first published in 2005, is packed with practical information and theoretical insight into the world of weather derivative pricing.Trade ReviewReview of the hardback: 'Weather Derivative Valuation draws on both finance and meteorology, with a healthy dose of mathematics and statistics, to provide the practitioner with a comprehensive guide to the various methods for pricing and hedging weather derivative contracts. While no perfect model may exist, Jewson and Brix give the reader the background necessary to make informed choices between competing techniques.' William Gebhardt, Merrill LynchReview of the hardback: 'The weather derivatives market is exciting, dynamic and growing. This book is the most complete treatment I have seen of the many issues surrounding valuation of weather derivatives, starting from the basic principles, and then covering all the bases including meteorological data analysis, pricing, portfolio management, incorporation of forecasts and risk management. As a practitioner in the market, I found this book comprehensive and excellently written. Jewson and Brix have taken a complex subject and made it both interesting to read and easy to understand. I would have no hesitation in recommending it to others, both experts in the field and those approaching the subject for the first time.' Gearóid Lane, CentricaReview of the hardback: 'The book covers all of the latest topics in weather derivative pricing, valuation and risk management in a way that is rigorous, and yet also accessible to the non-mathematician. Highly recommended for all involved in weather derivatives, whether they are hedgers, traders, investors, marketers or risk managers.' Martin Jones, Chief Investment Officer, Coriolis Capital LimitedTable of ContentsList of figures; List of tables; Acknowledgements; 1. Weather derivatives and the weather derivatives market; 2. Data cleaning and trends; 3. The valuation of single contracts using burn analysis; 4. The valuation of single contracts using index modelling; 5. Further topics in the valuation of single contracts; 6. Valuation of single contracts using daily methods; 7. Modelling portfolios; 8. Managing portfolios; 9. Introduction to meteorological forecasts; 10. The use of meteorological forecasts in pricing; 11. Arbitrage pricing models; 12. Risk management; 13. Modelling non-temperature data; Appendices; References; Index.
£96.00
Cambridge University Press Identification and Inference for Econometric Models
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£95.95
Cambridge University Press Computational and Mathematical Modeling in the Social Sciences
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£51.30
Cambridge University Press Experimental Auctions Methods and Applications in Economic and Marketing Research Quantitative Methods for Applied Economics and Business Research
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£52.25
Cambridge University Press Driving Forces in Physical Biological and Socioeconomic Phenomena
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£70.29
Cambridge University Press Confidence Likelihood Probability
Book SynopsisThis is the first book to develop a methodology of confidence distributions, with a lively mix of theory, illustrations, applications and exercises.Trade Review'This book presents a detailed and wide-ranging account of an approach to inference that moves the discipline towards increased cohesion, avoiding the artificial distinction between testing and estimation. Innovative and thorough, it is sure to have an impact both in the foundations of inference and in a wide range of practical applications of inference.' Nancy Reid, University Professor of Statistical Sciences, University of Toronto'I recommend this book very enthusiastically to any researcher interested in learning more about advanced likelihood theory, based on concepts like confidence distributions and fiducial distributions, and their links with other areas. The book explains in a very didactical way the concepts, their use, their interpretation, etc., illustrated by an impressive number of examples and data sets from a wide range of areas in statistics.' Ingrid Van Keilegom, Université Catholique de LouvainTable of Contents1. Confidence, likelihood, probability: an invitation; 2. Interference in parametric models; 3. Confidence distributions; 4. Further developments for confidence distribution; 5. Invariance, sufficiency and optimality for confidence distributions; 6. The fiducial argument; 7. Improved approximations for confidence distributions; 8. Exponential families and generalised linear models; 9. Confidence distributions in higher dimensions; 10. Likelihoods and confidence likelihoods; 11. Confidence in non- and semiparametric models; 12. Predictions and confidence; 13. Meta-analysis and combination of information; 14. Applications; 15. Finale: summary, and a look into the future.
£72.19
Cambridge University Press Principles of Statistical Inference
Book SynopsisNo one is better placed than D. R. Cox to give the comprehensive, balanced account of the theory of statistical inference, its main ideas and controversies, that is now needed. This book is for every serious user or student of statistics - for anyone serious about the scientific understanding of uncertainty.Trade Review'A deep and beautifully elegant overview of statistical inference, from one of the towering figures who created modern statistics. This book should be essential reading for all who call themselves 'statistician'.' David Hand, Imperial College London'The explanations of key concepts are written so clearly … that they may be understood even if the mathematical details are skipped.' MAA Online'The text is very well written and gives a balanced view of the frequentist and Bayesian notions of probability, without favouring one over the other.' Journal of Applied Statistics'… ideally suited for statisticians at all levels who want to refresh their own understanding of the theory of statistical inference without having to wade through theorems and proofs.' Biometrics'This is a great book by a great statistician. Buy it and read it.' Journal of the American Statistical AssociationTable of ContentsPreface; 1. Preliminaries; 2. Some concepts and simple applications; 3. Significance tests; 4. More complicated situations; 5. Some interpretational issues; 6. Asymptotic theory; 7. Further aspects of maximum likelihood; 8. Additional objectives; 9. Randomization-based analysis; Appendix A. A brief history; Appendix B. A personal view; References; Author index; Index.
£80.74
Cambridge University Press An Information Theoretic Approach to Econometrics
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£62.70
Cambridge University Press Advances in Economics and Econometrics Volume 1 Theory and Applications Ninth World Congress 39 Econometric Society Monographs Series Number 41
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£65.55
Cambridge University Press Advances in Economics and Econometrics Volume 2 Theory and Applications Ninth World Congress Theory and Applications Ninth World Congress Volume II 3 Econometric Society Monographs
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£65.55
Cambridge University Press Advances in Economics and Econometrics Volume 3 Theory and Applications Ninth World Congress Econometric Society Monographs Series Number 43
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£55.10
Cambridge University Press Urban Labor Economics
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£68.40
Cambridge University Press Fundamentals of Nonparametric Bayesian Inference 44 Cambridge Series in Statistical and Probabilistic Mathematics Series Number 44
Book SynopsisExplosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machineTrade Review'Probabilistic inference of massive and complex data has received much attention in statistics and machine learning, and Bayesian nonparametrics is one of the core tools. Fundamentals of Nonparametric Bayesian Inference is the first book to comprehensively cover models, methods, and theories of Bayesian nonparametrics. Readers can learn basic ideas and intuitions as well as rigorous treatments of underlying theories and computations from this wonderful book.' Yongdai Kim, Seoul National University'Bayesian nonparametrics has seen amazing theoretical, methodological, and computational developments in recent years. This timely book gives an authoritative account of the current state of the art by two leading scholars in the field. They masterfully cover all major aspects of the discipline, with an emphasis on asymptotics, and achieve the rare feat of being simultaneously broad and deep, while preserving the utmost mathematical rigor. This book is, without doubt, a must-read for Ph.D. students and researchers in statistics and probability.' Igor Prünster, Università Commerciale Luigi Bocconi, Milan'Worth waiting for, this book gives a both global and precise overview on the fundamentals of Bayesian nonparametrics. It will be extremely valuable as a textbook for Masters and Ph.D. students, along with more experienced researchers, as the authors have managed to gather, link together, and present with great clarity a large part of the major advances in Bayesian nonparametric modeling and theory.' Judith Rousseau, Université Paris-Dauphine'This book can serve as a textbook for a graduate course on Bayesian nonparametrics. It can also be used as a reference book for researchers in both statistics and machine learning, as well as application areas such as econometrics and biostatistics.' Yuehua Wu, MathSciNetTable of ContentsPreface; Glossary of symbols; 1. Introduction; 2. Priors on function spaces; 3. Priors on spaces of probability measures; 4. Dirichlet processes; 5. Dirichlet process mixtures; 6. Consistency: general theory; 7. Consistency: examples; 8. Contraction rates: general theory; 9. Contraction rates: examples; 10. Adaptation and model selection; 11. Gaussian process priors; 12. Infinite-dimensional Bernstein–von Mises theorem; 13. Survival analysis; 14. Discrete random structures; Appendices; References; Author index; Subject index.
£75.04
Cambridge University Press Error and Inference
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£55.10
Cambridge University Press RATS Handbook to Accompany Introductory Econometrics for Finance
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£86.56
Cambridge University Press Imperfect Perception and Stochastic Choice in Experiments
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£17.00
Cambridge University Press Quantitative Risk and Portfolio Management
Book SynopsisA 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.Trade Review'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'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'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'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'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 FundTable of ContentsPreface; 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.
£51.29
Cambridge University Press Time Series for Economics and Finance
Book Synopsis
£114.00
Cambridge University Press Ethics in Econometrics
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£81.00
Cambridge University Press Introduction to Catastrophe Risk Modelling
978-1009437387
£104.50
Cambridge University Press Imperfect Perception and Stochastic Choice in Experiments
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£47.49
Cambridge University Press Advances in Economics and Econometrics Volume 2
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£123.50
Cambridge University Press Dynamic Programming
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£35.14
Cambridge University Press Dynamic Programming
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£99.00
Cambridge University Press Brownian Motion the Fredholm Determinant and Time Series Analysis
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£112.50
Cambridge University Press One Hundred Years of Game Theory
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£103.50
Cambridge University Press Regression and Other Stories
Book SynopsisMost textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors'' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.Trade Review'Gelman, Hill and Vehtari provide an introductory regression book that hits an amazing trifecta: it motivates regression using real data examples, provides the necessary (but not superfluous) theory, and gives readers tools to implement these methods in their own work. The scope is ambitious - including introductions to causal inference and measurement - and the result is a book that I not only look forward to teaching from, but also keeping around as a reference for my own work.' Elizabeth Tipton, Northwestern University'Regression and Other Stories is simply the best introduction to applied statistics out there. Filled with compelling real-world examples, intuitive explanations, and practical advice, the authors offer a delightfully modern perspective on the subject. It's an essential resource for students and practitioners across the statistical and social sciences.' Sharad Goel, Department of Management Science and Engineering, Stanford University'With modern software it is very easy to fit complex regression models, and even easier to get their interpretation completely wrong. This wonderful book, summarising the authors' years of experience, stays away from mathematical proofs, and instead focuses on the insights to be gained by careful plotting and modelling of data. In particular the chapters on causal modelling, and the challenges of working with selected samples, provide some desperately needed lessons.' David Spiegelhalter, University of Cambridge'Gelman and Hill, have done it again, this time with Aki Vehtari. They have written a textbook that should be on every applied quantitative researcher's bookshelf. Most importantly they explain how to do and interpret regression with real world, complicated examples. Practicing academics in addition to students will benefit from giving this book a close read.' Christopher Winship, Harvard University, Massachusetts'Comprehensive and charming, this regression manual belongs on every regressor's shelf.' Joshua Angrist, Massachusetts Institute of TechnologyTable of ContentsPreface; Part I. Fundamentals: 1. Overview; 2. Data and measurement; 3. Some basic methods in mathematics and probability; 4. Statistical inference; 5. Simulation; Part II. Linear Regression: 6. Background on regression modeling; 7. Linear regression with a single predictor; 8. Fitting regression models; 9. Prediction and Bayesian inference; 10. Linear regression with multiple predictors; 11. Assumptions, diagnostics, and model evaluation; 12. Transformations and regression; Part III. Generalized Linear Models: 13. Logistic regression; 14. Working with logistic regression; 15. Other generalized linear models; Part IV. Before and After Fitting a Regression: 16. Design and sample size decisions; 17. Poststratification and missing-data imputation; Part V. Causal Inference: 18. Causal inference and randomized experiments; 19. Causal inference using regression on the treatment variable; 20. Observational studies with all confounders assumed to be measured; 21. Additional topics in causal inference; Part VI. What Comes Next?: 22. Advanced regression and multilevel models; Appendices: A. Computing in R; B. 10 quick tips to improve your regression modelling; References; Author index; Subject index.
£75.04
Cambridge University Press Stochastic Processes
Book SynopsisThis definitive textbook provides a solid introduction to stochastic processes, covering both theory and applications. It is written by one of the world's leading information theorists, evolving over twenty years of graduate classroom teaching, and is accompanied by over 300 exercises, with online solutions for instructors.Table of Contents1. Introduction and review of probability; 2. Poisson processes; 3. Gaussian random vectors and processes; 4. Finite-state Markov chains; 5. Renewal processes; 6. Countable-state Markov chains; 7. Markov processes with countable state spaces; 8. Detection, decisions, and hypothesis testing; 9. Random walks, large deviations, and martingales; 10. Estimation.
£64.59
Cambridge University Press Optimization Models
Book SynopsisEmphasizing practical understanding over the technicalities of specific algorithms, this elegant textbook is an accessible introduction to the field of optimization, focusing on powerful and reliable convex optimization techniques. Students and practitioners will learn how to recognize, simplify, model and solve optimization problems - and apply these principles to their own projects. A clear and self-contained introduction to linear algebra demonstrates core mathematical concepts in a way that is easy to follow, and helps students to understand their practical relevance. Requiring only a basic understanding of geometry, calculus, probability and statistics, and striking a careful balance between accessibility and rigor, it enables students to quickly understand the material, without being overwhelmed by complex mathematics. Accompanied by numerous end-of-chapter problems, an online solutions manual for instructors, and relevant examples from diverse fields including engineering, data Trade Review'In Optimization Models, Calafiore and El Ghaoui have created a beautiful and very much needed on-ramp to the world of modern mathematical optimization and its wide range of applications. They lead an undergraduate, with not much more than basic calculus behind her, from the basics of linear algebra all the way to modern optimization-based machine learning, image processing, control, and finance, to name just a few applications. Until now, these methods and topics were accessible only to graduate students in a few fields, and the few undergraduates who brave the daunting prerequisites. The book's seamless integration of mathematics and applications, and its focus on modeling practical problems and algorithmic solution methods, will be very appealing to a wide audience.' Stephen Boyd, Stanford University, CaliforniaTable of Contents1. Introduction; Part I. Linear Algebra: 2. Vectors; 3. Matrices; 4. Symmetric matrices; 5. Singular value decomposition; 6. Linear equations and least-squares; 7. Matrix algorithms; Part II. Convex Optimization: 8. Convexity; 9. Linear, quadratic and geometric models; 10. Second-order cone and robust models; 11. Semidefinite models; 12. Introduction to algorithms; Part III. Applications: 13. Learning from data; 14. Computational finance; 15. Control problems; 16. Engineering design.
£59.84
Cambridge University Press Handbook of Computational Social Choice
Book SynopsisThe rapidly growing field of computational social choice, at the intersection of computer science and economics, deals with the computational aspects of collective decision making. This handbook, written by thirty-six prominent members of the computational social choice community, covers the field comprehensively. Chapters devoted to each of the field''s major themes offer detailed introductions. Topics include voting theory (such as the computational complexity of winner determination and manipulation in elections), fair allocation (such as algorithms for dividing divisible and indivisible goods), coalition formation (such as matching and hedonic games), and many more. Graduate students, researchers, and professionals in computer science, economics, mathematics, political science, and philosophy will benefit from this accessible and self-contained book.Trade Review'The book offers to noneconomists an outstanding self-contained introduction to normative themes in contemporary economics and to economists a thorough discussion of the computational limits of their art. But I also recommend it to anyone with a taste for axiomatics: it is replete with new and open questions that will be with us for some time.' Hervé Moulin, from the Foreword'… anyone who knows a fair amount about the field will find much enjoyable reading in any given chapter. Those who wish to know more should first read the compact but well-organized overview of many of the classical questions in Chapter 2, and then skip to a self-contained chapter of one's choice. Bribery? The internet? Cake cutting? It's all there, waiting for discovery.' Karl-Dieter Crisman, MAA Reviews'Since the field of computational social choice is growing rapidly, a handbook such as this at this juncture is the need of the hour. The handbook is the product of the efforts of 36 outstanding members of the computational social choice community. It provides elaborate initiations to the major areas of the field. The handbook has already become an authoritative reference work and has been cited over 100 times since its publication. It contains many interesting open questions which will serve as fodder for hungry researchers … The book is a treasure trove of ideas from economics and computer science. Academicians, professionals, researchers, and students in many disciplines including economics, computer science, game theory, mathematics, philosophy, and political science will gain from this approachable and self-contained handbook.' S. V. Nagaraj, SIGACT News'As a final comment, let me say that this Handbook is a most remarkable volume. I was unable to detect defects or weaknesses. All chapters are well written, with an obvious objective regarding readership. Introduction sections are clear. The authors are capable of transmitting their knowledge, whatever the difficulty. I can only repeat myself by saying that it is highly recommended to all social scientists and all computer scientists interested in voting and in social choice in general.' Maurice Salles, OEconomia'If readers are looking for a short and concise introduction to (computational) social choice and for in-depth descriptions of essential theoretical problems and computational solutions covering a wide range of topics (voting, allocation, etc.), then this handbook may really be useful.' Roman Seidl, Journal of Artificial Societies and Social SimulationTable of ContentsForeword Hervé Moulin; 1. Introduction to computational social choice Felix Brandt, Vincent Conitzer, Ulle Endriss, Jérôme Lang and Ariel D. Procaccia; Part I. Voting: 2. Introduction to the theory of voting William S. Zwicker; 3. Tournament solutions Felix Brandt, Markus Brill and Paul Harrenstein; 4. Weighted tournament solutions Felix Fischer, Olivier Hudry and Rolf Niedermeier; 5. Dodgson's rule and Young's rule Ioannis Caragiannis, Edith Hemaspaandra and Lane A. Hemaspaandra; 6. Barriers to manipulation in voting Vincent Conitzer and Toby Walsh; 7. Control and bribery in voting Piotr Faliszewski and Jörg Rothe; 8. Rationalizations of voting rules Edith Elkind and Arkadii Slinko; 9. Voting in combinatorial domains Jérôme Lang and Lirong Xia; 10. Incomplete information and communication in voting Craig Boutilier and Jeffrey S. Rosenschein; Part II. Fair Allocation: 11. Introduction to the theory of fair allocation William Thomson; 12. Fair allocation of indivisible goods Sylvain Bouveret, Yann Chevaleyre and Nicolas Maudet; 13. Cake cutting algorithms Ariel D. Procaccia; Part III. Coalition Formation: 14. Matching under preferences Bettina Klaus, David F. Manlove and Francesca Rossi; 15. Hedonic games Haris Aziz and Rahul Savani; 16. Weighted voting games Georgios Chalkiadakis and Michael Wooldridge; Part IV. Additional Topics: 17. Judgment aggregation Ulle Endriss; 18. The axiomatic approach and the internet Moshe Tennenholtz and Aviv Zohar; 19. Knockout tournaments Virginia Vassilevska-Williams.
£51.29
Cambridge University Press Structural Vector Autoregressive Analysis
Book SynopsisStructural vector autoregressive (VAR) models are widely used in many fields of economics. This book traces the evolution of the structural VAR approach and reviews its econometric foundations. It provides guidance to empirical researchers as to the most appropriate methods of estimating and evaluating structural VAR models.Trade Review'The book by Kilian and Lütkepohl will become the new benchmark textbook for teaching structural vector autoregressive analysis. This book thus devotes considerable space to the issue of identification, including sign restrictions, to Bayesian methods, to Factor Vector Autoregressions and to non-fundamental shocks. These are key to understanding much of recent research. The authors do an excellent job of assembling and lucidly explaining it all. This book is destined to become a classic.' Harald Uhlig, University of Chicago'Structural vector autoregressions (SVARs) are an essential tool in empirical macroeconomics. This book provides a thorough and long-overdue digest of a literature that has been thriving for over 35 years and seen a lot of exciting developments in the past decade. The authors masterfully blend theoretical foundations, guidance for practitioners, and detailed empirical applications. This is a must-read for anyone working with SVARs.' Frank Schorfheide, University of PennsylvaniaTable of Contents1. Introduction; 2. Vector autoregressive models; 3. Vector error correction models; 4. Structural VAR tools; 5. Bayesian VAR analysis; 6. The relationship between VAR models and other macroeconometric models; 7. A historical perspective on causal inference in macroeconometrics; 8. Identification by short-run restrictions; 9. Estimation subject to short-run restrictions; 10. Identification by long-run restrictions; 11. Estimation subject to long-run restrictions; 12. Inference in models identified by short-run or long-run restrictions; 13. Identification by sign restrictions; 14. Identification by heteroskedasticity or non-gaussianity; 15. Identification based on extraneous data; 16. Structural VAR analysis in a data-rich environment; 17. Nonfundamental shocks; 18. Nonlinear structural VAR models; 19. Practical issues related to trends, seasonality, and structural change; References; Index.
£145.35
Cambridge University Press British Historical Statistics
Book SynopsisThis 1988 reference book provides the major economic and social statistical series for the British Isles from the twelfth century up until 198081. The text provides informed access to a wide range of economic data, without the labour of identifying sources or of transforming many different annual sources into a comparable time series.Table of ContentsPreface; List of abbreviations; 1. Population and vital statistics; 2. Labour force; 3. Agriculture; 4. Fuel and energy; 5. Metals; 6. Textiles; 7. Building; 8. Miscellaneous industrial statistics; 9. External trade; 10. Transport and communications; 11. Public finance; 12. Financial institutions; 13. Consumption; 14. Prices; 15. Miscellaneous statistics; 16. National accounts; Index.
£54.14
Cambridge University Press A Practitioners Guide to Stochastic Frontier Analysis Using Stata
Book SynopsisA Practitioner's Guide to Stochastic Frontier Analysis Using Stata provides practitioners with a step-by-step guide on how to conduct efficiency analysis using the stochastic frontier approach. Immensely helpful to the applied researcher, it bridges the chasm between theory and practice, expanding the range of applications in which production frontier analysis may be implemented.Trade Review'A competent empirical application of Stochastic Frontier Analysis (SFA) requires a clear understanding of both the production economics and the econometric theory behind the specified model side by side with adequate programming skills to write the necessary software codes. Apart from a clear exposition of the economic theory behind various stochastic frontier models that represent the technology (like the Distance Functions) and/or producer behavior (like the Cost or Profit Functions) and the relevant econometric theory, the authors offer detailed instructions on how to write the commands for various models in Stata and explain how to interpret the results. This book will prove to be invaluable for every serious researcher using SFA to measure production efficiency.' Subhash C. Ray, University of Connecticut'This book is a significant contribution to an applied introduction to stochastic frontier analysis. The authors explain clearly many of the models used in efficiency estimation, which has become a standard tool in the arsenal of applied economics. They explain clearly the models and the assumptions and provide a thorough introduction to estimating performance and efficiency for the practitioner. The many scientific fields in which efficiency and performance measurement are important will benefit immensely from the book not only because of its clarity and concreteness but also because the models are taken directly to practice using Stata, standard software used by many researchers. The combination of theory and practical application is masterfully done in this book, and practitioners in a vast number of fields will find it indispensable for their research.' Mike G. Tsionas, Athens University of Economics and BusinessTable of Contents1. Introduction; 2. Production, distance, cost, and profit functions; 3. Production frontier models; 4. Cost frontier models; 5. Profit frontier models; 6. Cost system models; 7. Profit system models; 8. Primal cost models; 9. Profit primal models; 10. Panel models; 11. Productivity and profitability; 12. Looking ahead.
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