Introduction to Bayesian Econometrics

This textbook is an introduction to econometrics from the Bayesian viewpoint. The second edition includes new material.

Edward Greenberg (Author)

9781107015319, Cambridge University Press

Hardback, published 12 November 2012

270 pages, 29 b/w illus. 19 tables
25.7 x 17.5 x 2 cm, 0.64 kg

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

This 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 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.

Subject Areas: Computer science [UY], Applied mathematics [PBW], Probability & statistics [PBT], Econometrics [KCH]