Bayesian Econometric Methods

by Gary Koop , Dale J. Poirier , Justin L. Tobias

ISBN13: 9780521855716

ISBN10: 0521855713

Format: Hardcover

Pub. Date: 2007-01-15

Publisher(s): Cambridge University Press

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Summary

A new book in the Econometric Exercises series, this volume contains questions and answers to provide students with useful practice, as they attempt to master Bayesian econometrics. In addition to many theoretical exercises, this book contains exercises designed to develop the computational tools used in modern Bayesian econometrics. The latter half of the book contains exercises that show how these theoretical and computational skills are combined in practice, to carry out Bayesian inference in a wide variety of models commonly used by econometricians. Aimed primarily at advanced undergraduate and graduate students studying econometrics, this book may also be useful for students studying finance, marketing, agricultural economics, business economics or, more generally, any field which uses statistics. The book also comes equipped with a supporting website containing all the relevant data sets and MATLAB computer programs for solving the computational exercises.

Author Biography

Justin L. Tobias is Associate Professor of Economics, Iowa State University.

List of exercises	p. ix
Preface to the series	p. xv
Preface	p. xix
The subjective interpretation of probability	p. 1
Bayesian inference	p. 11
Point estimation	p. 29
Frequentist properties of Bayesian estimators	p. 37
Interval estimation	p. 51
Hypothesis testing	p. 59
Prediction	p. 71
Choice of prior	p. 79
Asymptotic Bayes	p. 91
The linear regression model	p. 107
Basics of Bayesian computation	p. 117
Monte Carlo integration	p. 119
Importance sampling	p. 124
Gibbs sampling and the Metropolis-Hastings algorithm	p. 128
Other (noniterative) methods for generating random variates	p. 157
Hierarchical models	p. 169
The linear regression model with general covariance matrix	p. 191
Latent variable models	p. 203
Mixture models	p. 253
Some scale mixture of normals models	p. 254
Other continuous and finite-mixture models	p. 260
Bayesian model averaging and selection	p. 281
Bayesian model averaging	p. 282
Bayesian variable selection and marginal likelihood calculation	p. 287
Some stationary time series models	p. 297
Some nonstationary time series models	p. 319
Appendix	p. 335
Bibliography	p. 343
Index	p. 353
Table of Contents provided by Ingram. All Rights Reserved.