Bayesian Model Averaging 

Courses Bayesian and Classical Approaches to Inference and Model Averaging The course provides an introduction to Bayesian inference from the perspectives of a classically trained econometrician. Beginning with Bayes Theorem applied to random parameters, the material examines a number of key issues for classical estimation, and where appropriate considers the Bayesian analog. The material moves from the fundamental dichotomy between fixed and random quantities in classical estimation, and considers the role of the principle distinction between the two approaches  namely random versus fixed parameters. We examine how key notions such as convergence, and the use of simulation as an inference tool differs across the two approaches. The translation of fixed versus random effects panel data models into a Bayesian framework provides a convenient introduction to the use of hierarchical and nonhierarchical priors. The curse of dimensionality which plagues inference in a broad class of latent variable Given fundamental differences in the treatment of missing data between Classical and Bayesian approaches, we consider how the use of Data Augmentation presents a powerful tool to circumvent dimensionality problems in a class of Bayesian models. A number of applications are considered. These are Bayesian model averaging applied to the problem of conducting inference on the nature of financial crises. We also introduce new work on identifying complementarities between policy instruments in estimating model of economic growth. Finally, we apply model averaging to problems of economic forecasting. Course Outline 1. Introduction to Bayesian Inference Outline for Central Bank of Norway Course Gernot Doppelhofer Dr. Melvyn Weeks
