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Introduction to Modern Bayesian Econometrics


About two hundred and forty years ago, an English clergyman named Thomas Bayes developed a method to calculate the chances of uncertain events in the light of accumulating evidence. Though his method has extensive applications to the work of economists, it is only recent advances in computing that have made it possible to exploit its full power.

In this new and expanding area, Tony Lancaster’s text provides a comprehensive introduction to the Bayesian way of doing applied economics. Using clear explanations and practical illustrations and problems, the text presents innovative, computer-intensive ways for applied economists to use the Bayesian method. In addition, each chapter includes numerical and graphical examples and demonstrates their solutions using the S programming language and Bugs software.

Tony Lancaster is Herbert H. Goldberger Professor of Economics and Professor of Community Health at Brown University. He is the author of The Econometric Analysis of Transition Data (1990), an Econometric Society Monograph.

1. The Bayesian Algorithm.

2. Prediction and Model Checking.

3. Linear Regression.

4. Bayesian Calculations.

5. Nonlinear Regression Models.

6. Randomized, Controlled and Observational Data.

7. Models for Panel Data.

8. Instrumental Variables.

9. Some Time Series Models.

Appendix 1: A Conversion Manual.

Appendix 2: Programming.

Appendix 3: BUGS.