E Ley and MFJ Steel
Jointness in Bayesian Variable Selection with Applications to Growth Regression
Date: August 2006
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 Fernadez et al (2001) and Sala-i-Martin et al (2004).
Keywords: Bayesian model averaging; Complements; Model uncertainty; Posterior odds; Substitutes