Jointness in Bayesian Variable Selection With Applications to Growth Regression

Abstract

We present a measure of jointness to explore dependence among regressors, in the context of Bayesian model selection. The jointness measure proposed here equals the posterior odds ratio between those models that include a set of variables and the models that only include proper subsets. We illustrate its application in cross-country growth regressions using two datasets from Fernandez et al (2001b) and Sala-i-Martin et al(2004).

Keywords: Bayesian model averaging; Complements; Model uncertainty; Posterior odds; Substitutes

JEL Classification: C7, C11