This volume in the Econometric Exercises series contains questions and answers to provide students with useful practice, as they attempt to master Bayesian econometrics. In addition to many theoretical exercises, this book contains exercises designed to develop the computational tools used in modern Bayesian econometrics. The latter half of the book contains exercises that show how these theoretical and computational skills are combined in practice, to carry out Bayesian inference in a wide variety of models commonly used by econometricians. Aimed primarily at advanced undergraduate and graduate students studying econometrics, this book may also be useful for students studying finance, marketing, agricultural economics, business economics or, more generally, any field which uses statistics. The book also comes equipped with a supporting website containing all the relevant data sets and MATLAB computer programs for solving the computational exercises.

• Text offers 173 extended problems and complete solutions in Bayesian econometrics for upper-level undergraduates and above • May also be used in courses in statistics and applied mathematics; comes with extensive programs on accompanying website • Senior authors are internationally renowned Bayesian analysts

### Contents

Preface; 1. The subjective interpretation of probability; 2. Bayesian inference; 3. Point estimation; 4. Frequentist properties of Bayesian estimators; 5. Interval estimation; 6. Hypothesis testing; 7. Prediction; 8. Choice of prior; 9. Asymptotic Bayes; 10. The linear regression model; 11. Basics of Bayesian computation; 12. Hierarchical models; 13. The linear regression model with general covariance matrix; 14. Latent variable models; 15. Mixture models; 16. Bayesian model averaging and selection; 17. Some stationary time series models; 18. Some nonstationary time series models; Appendix; Index.

### Reviews

'I am deeply impressed by this articulate, outstanding work. It has the same high level of precision as Poirier's 1995 text on intermediate statistics and econometrics for MIT Press. The authors have taken the time and effort to explain as much as possible. Chapter 14 on latent variable models is probably the most important chapter offering new work. The authors' explanations are extensive for each of their models, and a reader who is interested in just one of the models will not have to rely on the results from any of the other models.' Frank Kleibergen, Brown University

'This is a very well written book on Bayesian econometrics with rigorous derivations and exercises. It will indeed be a book that is on the required reading list for an advanced course on Bayesian econometrics. The books by Poirier and Lancaster [Blackwell, 2004] do not have the nice set of exercises presented here.' Herman van Dijk, Erasmus University, Netherlands