Introduction to Bayesian econometrics

This book introduces the increasingly popular Bayesian approach to statistics to graduates and advanced undergraduates. In contrast to the long-standing frequentist approach to statistics, the Bayesian approach makes explicit use of prior information and is based on the subjective view of probability. Bayesian econometrics takes probability theory as applying to all situations in which uncertainty exists, including uncertainty over the values of parameters. A distinguishing feature of this book is its emphasis on classical and Markov chain Monte Carlo (MCMC) methods of simulation. The book is concerned with applications of the theory to important models that are used in economics, political science, biostatistics, and other applied fields. These include the linear regression model and extensions to Tobit, probit, and logit models; time series models; and models involving endogenous variables.

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 Multivariate responses

10 Time series

11 Endogenous covariates and sample selection

Appendix A Probability distributions and matrix theorems

Appendix B Computer programs for MCMC calculations

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