Bayesian Model Averaging
Home | Papers | Software | Courses

ii. Jointness of Growth Determinants. Gernot Doppelhofer and Melvyn Weeks , Journal of Applied Econometrics, vol 24, 2, 2009.

This paper introduces a new measure of dependence or jointness among explanatory variables. Jointness is based on the joint posterior distribution of variables over the model space, thereby taking model uncertainty into account. By looking beyond marginal measures of variable importance, jointness reveals generally unknown forms of dependence. Positive jointness implies that regressors are complements, representing distinct, but mutually reinforcing effects. Negative jointness implies that explanatory variables are substitutes and capture similar underlying effects. In a cross-country dataset we show that jointness among 67 determinants of growth is important, affecting inference and informing economic policy.

Working Paper Version: Available at SSRN:
Journal of Applied Econometrics:

Tables link: (see nhh)