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Jointness of Growth Determinants

  • Gernot Doppelhofer
  • Melvyn Weeks

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.

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Paper provided by CESifo Group Munich in its series CESifo Working Paper Series with number 1978.

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Date of creation: 2007
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Handle: RePEc:ces:ceswps:_1978
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