This book introduces the increasingly popular Bayesian approach to statistics to graduates and advanced undergraduates. In contrast to the long-standing frequentist approach to statistics, the Bayesian approach makes explicit use of prior information and is based on the subjective view of probability. Bayesian econometrics takes probability theory as applying to all situations in which uncertainty exists, including uncertainty over the values of parameters. A distinguishing feature of this book is its emphasis on classical and Markov chain Monte Carlo (MCMC) methods of simulation. The book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics, and other applied fields. These include the linear regression model and extensions to Tobit, probit, and logit models; time series models; and models involving endogenous variables.

“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