Introduction to Bayesian Econometrics / Edition 2

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Overview

This concise textbook is an introduction to econometrics at the graduate or advanced undergraduate level. It differs from other books in econometrics in its use of the Bayesian approach to statistics. This approach, in contrast to the frequentist approach to statistics, makes explicit use of prior information and is based on the subjective view of probability, which takes probability theory as applying to all situations in which uncertainty exists, including uncertainty over the values of parameters.

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Editorial Reviews

From the Publisher
“This book provides an excellent introduction to Bayesian econometrics and statistics with many references to the recent literature that will be very helpful for students and others who have a good background in the calculus. Basic Bayesian estimation, testing, prediction and decision techniques are clearly explained with applications to a broad range of models and many computed examples are provided to illustrate general principles. Classical and modern computing techniques are clearly explained and applied to solve central inference problems. Also, references to downloadable computer algorithms are included in this impressive book.” - Arnold Zellner, Graduate School of Business, University of Chicago

“This concise book provides an excellent introduction to modern, simulation-based Bayesian econometrics. It covers the theoretical underpinnings, the MCMC algorithm, and a large number of important econometric applications in an accessible yet rigorous manner. I highly recommend Greenberg’s book as a Ph.D.-level textbook and as a source of reference for researchers entering the field.” - Rainer Winkelmann, University of Zurich

“Professor Greenberg has assembled a tremendously valuable resource for anyone who wants to learn more about the Bayesian world. The book begins at an introductory level that should be accessible to a wide range of readers. Professor Greenberg then builds on these fundamental ideas to help the reader develop an in-depth understanding of the major concepts and methods used in modern Bayesian econometrics. The explanations are very clearly written, and the content is supported with many detailed examples and real-data applications.” - Douglas J. Miller, University of Missouri - Columbia

“In Introduction to Bayesian Econometrics, Greenberg skillfully guides us through the fundamentals of Bayesian inference, provides a detailed review of methods for posterior simulation and carefully illustrates the use of such methods for fitting a wide array of popular micro-econometric and time series models. The writing style is accessible and lucid, the coverage is comprehensive, and the associated web site provides data and computer code to clearly illustrate how modern Bayesian methods are implemented in practice. This text is a must-have for the Bayesian and will appeal to statisticians/econometricians of all persuasions.” - Justin L. Tobias, Iowa State University

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Product Details

  • ISBN-13: 9781107015319
  • Publisher: Cambridge University Press
  • Publication date: 11/30/2012
  • Edition description: New Edition
  • Edition number: 2
  • Pages: 272
  • Product dimensions: 7.20 (w) x 10.00 (h) x 0.80 (d)

Meet the Author

Edward Greenberg is Professor Emeritus of Economics at Washington University in 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, Professor Greenberg is coauthor of four books: Wages, Regime Switching, and Cycles (1992), The Labor Market and Business Cycle Theories (1989), Advanced Econometrics (1991) and Regulation, Market Prices, and Process Innovation (1979). His published research has appeared in leading journals such as the American Economic Review, Econometrica, Journal of Econometrics, 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.

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Table of Contents

Part I. Fundamentals of Bayesian Inference: 1. Introduction; 2. Basic concepts of probability and inference; 3. Posterior distributions and inference; 4. Prior distributions; Part II. Simulation: 5. Classical simulation; 6. Basics of Markov chains; 7. Simulation by MCMC methods; Part III. Applications: 8. Linear regression and extensions; 9. Multivariate responses; 10. Time series; 11. Endogenous covariates and sample selection; Appendix A. Probability distributions and matrix theorems. Appendix B: Computer programs for MCMC calculations.

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