Default Priors and Predictive Performance in Bayesian Model Averaging, with Application to Growth Determinants

Abstract

Bayesian model averaging (BMA) has become widely accepted as a way of accounting for model uncertainty, notably in regression models for identifying the determinants of economic growth. To implement BMA the user must specify a prior distribution in two parts: a prior for the regression parameters and a prior over the model space. Here we address the issue of which default prior to use for BMA in linear regression. We compare 12 candidate parameter priors: the Unit Information Prior (UIP) corresponding to the BIC or Schwarz approximation to the integrated likelihood, a proper data-dependent prior, and 10 priors considered by Fernandez et al. (2001b). We also compare the uniform model prior to others that favor smaller models. We compare them on the basis of crossvalidated predictive performance on a well-known growth dataset and on two simulated examples from the literature. We found that the UIP with uniform model prior generally outperformed the other priors considered. It also identified the largest set of growth determinants.

Keywords: Growth Determinants, Model Uncertainty, Bayesian Model Averaging (BMA), Parameter and Model Prior Elicitation, Predictive Performance

JEL Classification: O51, O52, O53