Model uncertainty is a key component of statistical data analysis and an integral part in the inferential process. Because theory typically implies a range of possible competing empirical specifications, accounting for model uncertainty is crucial for understanding the processes under investigation, and is a necessary step in interpreting model parameters and performing predictions. Bayesian model comparison and model averaging is an active research area that continues to generate new ideas and innovative approaches.

A research conference will be held at the University of California, Irvine, February 22-23, 2014. The aim of this conference is to produce a research volume that examines key aspects of modern Bayesian research on model comparison and model averaging. The volume will address important challenges in this area with the goal of improving theoretical foundations and practical implementation. Selected papers will appear in Advances in Econometrics, Volume 34. The volume will be edited by Dale J. Poirier and Ivan Jeliazkov.

Advances in Econometrics is a research annual whose editorial policy is to publish original articles that contain enough details so that economists and econometricians who are not experts in the topics will find them accessible and useful in their research. Authors should be able to provide, upon request, computer programs and data used in their articles. For more information on the Advances in Econometrics series and the contents of previous volumes, see http://faculty.smu.edu/millimet/AiE.html.