Introduction to Bayesian Econometrics

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.

• 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.

'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 Rhode Island