Bayesian Econometrics

Bayesian Econometrics introduces the reader to the use of Bayesian methods in the field of econometrics at the advanced undergraduate or graduate level. The book is self-contained and does not require that readers have previous training in econometrics. The focus is on models used by applied economists and the computational techniques necessary to implement Bayesian methods when doing empirical work. The book includes numerous empirical examples and the website associated with it contains data sets and computer programs to help the student develop the computational skills of modern Bayesian econometrics.

Gary Koop is Professor of Economics at the University of Glasgow.

Preface.

1. An Overview of Bayesian Econometrics.

2. The Normal Linear Regression Model with Natural Conjugate Prior and a Single Explanatory Variable.

3. The Normal Linear Regression Model with Natural Conjugate Prior and Many Explanatory Variables.

4. The Normal Linear Regression Model with Other Priors.

5. The Nonlinear Regression Model.

6. The Linear Regression Model with General Error Covariance Matrix.

7. The Linear Regression Model with Panel Data.

8. Introduction to Time Series: State Space Models.

9. Qualitative and Limited Dependent Variable Models.

10. Flexible Models: Nonparametric and Semi-Parametric Methods.

11. Bayesian Model Averaging.

12. Other Models, Methods and Issues.

Appendix A: Introduction to Matrix Algebra.

Appendix B: Introduction to Probability and Statistics.

Bibliography.

Index.