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 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. It includes numerous numerical examples and topics covered in the book include:
  • the regression model (and variants applicable for use with panel data
  • time series models
  • models for qualitative or censored data
  • nonparametric methods and Bayesian model averaging.

A website containing computer programs and data sets to help the student develop the computational skills of modern Bayesian econometrics can be found at:

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


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.



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