Jointness of growth determinants
提出一种衡量解释变量间依赖关系的新指标“联合性”，基于模型空间的后验分布考虑模型不确定性，区分变量是互补还是替代，并用跨国数据展示67个增长决定因素间的联合性对推断和政策有重要影响。
Abstract 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.