Please read our student and staff community guidance on COVID-19
Skip to main content Skip to navigation

Paper No. 07-08

Download 07-8

E Ley and MFJ Steel

On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression

Date: April 2007 

Abstract: We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. We examine the effect of a variety of prior assumptions on the inference concerning model size, posterior inclusion probabilities of regressors and on predictive performance. We illustrate these issues in the context of crosscountry growth regressions using three datasets with 41 to 67 potential drivers of growth and 72 to 93 observations. Finally, we recommend priors for use in this and related contexts.

Keywords: Model size; Model uncertainty; Posterior odds; Prediction; Prior odds; Robustness JEL Classification System. C11, O47