This book is a volume in the Econometric Exercises series. It teaches principles of Bayesian econometrics by posing a series of theoretical and applied questions, and providing detailed solutions to those questions. The text is primarily suitable for graduate study in econometrics, though it can be used for advanced undergraduate courses, and should generate interest from students in related fields, including finance, marketing, agricultural economics, business economics, and other disciplines that employ statistical methods. The book provides a detailed treatment of a wide array of models commonly employed by economists and statisticians, including linear regression-based models, hierarchical models, latent variable models, mixture models, and time series models. Basics of random variable generation and simulation via Markov Chain Monte Carlo (MCMC) methods are also provided. Finally, posterior simulators for each type of model are rigorously derived, and MATLAB computer programs for fitting these models (using both actual and generated data sets) are provided on the Web site accompanying the text. Book jacket.

Gary Koop is Professor of Economics at the University of Strathclyde Dale J. Poirier is Professor of Economics at the University of California, Irvine Justin L. Tobias is Associate Professor of Economics, Iowa State University