Introduction to Bayesian Econometrics / Edition 2 by Edward Greenberg | 9781107015319 | Hardcover | Barnes & Noble
Introduction to Bayesian Econometrics / Edition 2

Introduction to Bayesian Econometrics / Edition 2

by Edward Greenberg
     
 

ISBN-10: 1107015316

ISBN-13: 9781107015319

Pub. Date: 11/30/2012

Publisher: Cambridge University Press

This textbook, now in its second edition, is an introduction to econometrics from the Bayesian viewpoint. It begins with an explanation of the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency. It then turns to the definitions of the likelihood function, prior distributions, and

Overview

This textbook, now in its second edition, is an introduction to econometrics from the Bayesian viewpoint. It begins with an explanation of the basic ideas of subjective probability and shows how subjective probabilities must obey the usual rules of probability to ensure coherency. It then turns to the definitions of the likelihood function, prior distributions, and posterior distributions. It explains how posterior distributions are the basis for inference and explores their basic properties. The Bernoulli distribution is used as a simple example. 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, which leads to an explanation of classical and Markov chain Monte Carlo (MCMC) methods of simulation. The latter is proceeded by a brief introduction to Markov chains. 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, which has become the most widely used environment for Bayesian statistics.

Product Details

ISBN-13:
9781107015319
Publisher:
Cambridge University Press
Publication date:
11/30/2012
Edition description:
New Edition
Pages:
272
Product dimensions:
7.20(w) x 10.00(h) x 0.80(d)

Table of Contents

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.

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