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. Semiparametric regression; 10. Multivariate responses; 11. Time series; 12. Endogenous covariates and sample selection; A. Probability distributions and matrix theorems; B. Computer programs for MCMC calculations.
This textbook is an introduction to econometrics from the Bayesian viewpoint. The second edition includes new material.
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.
'Edward Greenberg's Introduction to Bayesian Econometrics provides
clear and concise coverage of Bayesian theory, computational
methods, and important applications. Three years of teaching from
its first edition convince me that it is a splendid textbook. The
second edition is further enhanced by more applications and new
guidance on use of free R software.' John P. Burkett, University of
'The apple has not fallen far from the tree, as this second edition of Introduction to Bayesian Econometrics continues in the fine tradition of its predecessor. Along with considerable new material, this second edition contains a thoughtful discussion of important models in time series and financial econometrics (including ARCH/GARCH and stochastic volatility models), as well as an introduction to flexible Bayesian techniques for distribution and regression function modeling. Throughout the text Greenberg engages the reader with an accessible writing style, real data applications, and references to the R programming language. There is much to be learned within these pages. Students and researchers in statistics, biostatistics, economics, and the social sciences will find this to be a tremendously valuable resource.' Justin Tobias, Purdue University
Review of the first edition: '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 and then builds on these fundamental ideas to help the reader develop an in-depth understanding of 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
Review of the first edition: 'This concise textbook covers the theoretical underpinnings of econometrics, 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 PhD-level textbook and as a source of reference for researchers entering the field.' Rainer Winkelmann, University of Zurich
Review of the first edition: '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 strong background in calculus.' Arnold Zellner, University of Chicago