Introduction to Modern Bayesian Econometrics
In this new and expanding area, Tony Lancaster's text is the
first comprehensive introduction to the Bayesian way of doing
applied economics.
* * Uses clear explanations and practical illustrations and
problems to present innovative, computer-intensive ways for applied
economists to use the Bayesian method;
* * Emphasizes computation and the study of probability
distributions by computer sampling;
* Covers all the standard econometric models, including linear
and non-linear regression using cross-sectional, time series, and
panel data;
* Details causal inference and inference about structural
econometric models;
* Includes numerical and graphical examples in each chapter,
demonstrating their solutions using the S programming language and
Bugs software
* Supported by online supplements, including Data Sets and
Solutions to Problems, at
www.blackwellpublishing.com/lancaster
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.
Index
brought about by modern computing and simulation methods from a
perspective that econometricians will find familiar. It works
through the implications for econometric practice using practical
examples and accessible computer software. Graduate students in
economics will find it highly accessible. Practitioners steeped in
classical econometric methods will find much that is new, exciting,
and useful here as well." John Geweke, University of
Iowa
"Lancaster's text gives an impressive overview of the
Bayesian point of view, and should prove a valuable resource to
econometricians of all persuasions." Werner Ploberger,
University of Rochester
