Bayesian Model Average Using Stata

Oregon Convention Center 

Model uncertainty accompanies many data analyses. Bayesian model averaging (BMA) helps address this uncertainty. For instance, in the regression context, we may want to know which predictors are important given the observed data? Or which models are more plausible? Or how do predictors relate to each other across different models? BMA can answer these and more questions. BMA uses the Bayes theorem to aggregate the results across multiple candidate models to account for model uncertainty during inference and prediction in a principled and universal way. In this workshop, I will describe the basics of BMA and demonstrate it with Stata's bma suite. I will also show how BMA can become a useful tool for your regression analysis, Bayesian or not! No prior knowledge is required, but familiarity with Bayesian analysis will prove useful.