Introduction to Bayesian Econometrics

Edward Greenberg

ISBN 10: 1107015316 / ISBN 13: 9781107015319

Published by Cambridge University Press, GB, 2012

About this Item

This textbook explains the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency. It defines the likelihood function, prior distributions and posterior distributions. It explains how posterior distributions are the basis for inference and explores their basic properties. Various methods of specifying prior distributions are considered, with special emphasis on subject-matter considerations and exchange ability. The regression model is examined to show how analytical methods may fail in the derivation of marginal posterior distributions. The remainder of the book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics and other applied fields. New to the second edition is a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH and stochastic volatility models. The new edition also emphasizes the R programming language.

Seller Inventory # LU-9781107015319

Bibliographic Details

• Title: Introduction to Bayesian Econometrics
• Publisher: Cambridge University Press, GB
• Publication Date: 2012
• Language: English
• ISBN 10: 1107015316
• ISBN 13: 9781107015319
• Binding: Hardback
• Edition: 2nd Edition
• Condition: New

About this title

Synopsis

This textbook, now in its second edition, is an introduction to econometrics from the Bayesian viewpoint. It begins with an explanation of the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency. It then turns to the definitions of the likelihood function, prior distributions, and posterior distributions. It explains how posterior distributions are the basis for inference and explores their basic properties. The Bernoulli distribution is used as a simple example. Various methods of specifying prior distributions are considered, with special emphasis on subject-matter considerations and exchange ability. The regression model is examined to show how analytical methods may fail in the derivation of marginal posterior distributions, which leads to an explanation of classical and Markov chain Monte Carlo (MCMC) methods of simulation. The latter is proceeded by a brief introduction to Markov chains. The remainder of the book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics, and other applied fields. New to the second edition is a chapter on semiparametric regression and new sections on the ordinal probit, item response, factor analysis, ARCH-GARCH, and stochastic volatility models. The new edition also emphasizes the R programming language, which has become the most widely used environment for Bayesian statistics.

About the Author

Edward Greenberg is Professor Emeritus of Economics at Washington University, St Louis, where he served as a Full Professor on the faculty from 1969 to 2005. Professor Greenberg also taught at the University of Wisconsin, Madison, and has been a Visiting Professor at the University of Warwick (UK), Technion University (Israel) and the University of Bergamo (Italy). A former holder of a Ford Foundation Faculty Fellowship, Greenberg is the author of the first edition of Introduction to Bayesian Econometrics (Cambridge University Press, 2008) and the co-author of four books: Wages, Regime Switching, and Cycles (1992), The Labor Market and Business Cycle Theories (1989), Advanced Econometrics (1983, revised 1991) and Regulation, Market Prices, and Process Innovation (1979). His published research has appeared in leading journals such as the American Economic Review, Econometrica, the Journal of Econometrics, the Journal of the American Statistical Association, Biometrika and the Journal of Economic Behavior and Organization. Professor Greenberg's current research interests include dynamic macroeconomics as well as Bayesian econometrics.