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Jointness of growth determinants
Author(s) -
Doppelhofer Gernot,
Weeks Melvyn
Publication year - 2009
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.1046
Subject(s) - economics , econometrics , variable (mathematics) , inference , mathematics , computer science , mathematical analysis , artificial intelligence
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. Copyright © 2009 John Wiley & Sons, Ltd.