Edward GreenbergCambridge University PressEdition: 2, 11/12/2012EAN 9781107015319, ISBN10: 1107015316Hardcover, 270 pages, 25.7 x 17.5 x 2 cmLanguage: EnglishThis 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.Part I. Fundamentals of Bayesian Inference1. Introduction2. Basic concepts of probability and inference3. Posterior distributions and inference4. Prior distributionsPart II. Simulation5. Classical simulation6. Basics of Markov chains7. Simulation by MCMC methodsPart III. Applications8. Linear regression and extensions9. Semiparametric regression10. Multivariate responses11. Time series12. Endogenous covariates and sample selectionA. Probability distributions and matrix theoremsB. Computer programs for MCMC calculations.Advance praise: '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
