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Paper No. 06-12

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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).

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