Contemporary Bayesian Econometrics And Statistics

Tools to improve decision making in an imperfect world

This publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and Statistics provides readers with state-of-the-art simulation methods and models that are used to solve complex real-world problems. Armed with a strong foundation in both theory and practical problem-solving tools, readers discover how to optimize decision making when faced with problems that involve limited or imperfect data.

The book begins by examining the theoretical and mathematical foundations of Bayesian statistics to help readers understand how and why it is used in problem solving. The author then describes how modern simulation methods make Bayesian approaches practical using widely available mathematical applications software. In addition, the author details how models can be applied to specific problems, including:
* Linear models and policy choices
* Modeling with latent variables and missing data
* Time series models and prediction
* Comparison and evaluation of models

The publication has been developed and fine- tuned through a decade of classroom experience, and readers will find the author's approach very engaging and accessible. There are nearly 200 examples and exercises to help readers see how effective use of Bayesian statistics enables them to make optimal decisions. MATLAB? and R computer programs are integrated throughout the book. An accompanying Web site provides readers with computer code for many examples and datasets.

This publication is tailored for research professionals who use econometrics and similar statistical methods in their work. With its emphasis on practical problem solving and extensive use of examples and exercises, this is also an excellent textbook for graduate-level students in a broad range of fields, including economics, statistics, the social sciences, business, and public policy.

Geweke (economics, statistics, U. of Iowa) gives a thorough understanding of Bayesian analysis grounded in the theory of inference and optimal decision-making. He covers the basics of the elements of Bayesian inference, posterior simulation, linear models, modeling with latent variables, modeling for time series and Bayesian investigation, and includes such topics in inference as hierarchical priors and latent variables, improper prior distributions, prior robustness and the density ratio class, asymptotic analysis, and the likelihood principle. Geweke also details how models can be applied to specific problems, including linear models and policy choices, modeling with latent variables and missing data, prediction with time series models, and comparison and evaluation of models. Annotation © 2006 Book News, Inc., Portland, OR

JOHN GEWEKE, PHD, is Harlan McGregor Chair in Economic Theory and Professor of Economics and Statistics at the University of Iowa. He is an elected Fellow of the Econometric Society and the American Statistical Association, former President of the International Society for Bayesian Analysis, and coeditor of the Journal of Econometrics.