Bayesian econometric methods

This volume in the Econometric Exercises series 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 Notes

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
1 The subjective interpretation of probability	p. 1
2 Bayesian inference	p. 11
3 Point estimation	p. 29
4 Frequentist properties of Bayesian estimators	p. 37
5 Interval estimation	p. 51
6 Hypothesis testing	p. 59
7 Prediction	p. 71
8 Choice of prior	p. 79
9 Asymptotic Bayes	p. 91
10 The linear regression model	p. 107
11 Basics of Bayesian computation	p. 117
11.1 Monte Carlo integration	p. 119
11.2 Importance sampling	p. 124
11.3 Gibbs sampling and the Metropolis-Hastings algorithm	p. 128
11.4 Other (noniterative) methods for generating random variates	p. 157
12 Hierarchical models	p. 169
13 The linear regression model with general covariance matrix	p. 191
14 Latent variable models	p. 203
15 Mixture models	p. 253
15.1 Some scale mixture of normals models	p. 254
15.2 Other continuous and finite-mixture models	p. 260
16 Bayesian model averaging and selection	p. 281
16.1 Bayesian model averaging	p. 282
16.2 Bayesian variable selection and marginal likelihood calculation	p. 287
17 Some stationary time series models	p. 297
18 Some nonstationary time series models	p. 319
Appendix	p. 335
Bibliography	p. 343
Index	p. 353

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