Data for: Unveiling covariate inclusion structures in economic growth regressions using latent class analysis

Abstract of associated article: We propose the use of Latent Class Analysis methods to analyze the covariate inclusion patterns across specifications resulting from Bayesian model averaging exercises. Using Dirichlet Process clustering, we are able to identify and describe dependency structures among variables in terms of inclusion in the specifications that compose the model space. We apply the method to two datasets of potential determinants of economic growth. Clustering the posterior covariate inclusion structure of the model space formed by linear regression models reveals interesting patterns of complementarity and substitutability across economic growth determinants.

关联论文摘要：本文提出采用潜在类别分析（Latent Class Analysis）方法，对贝叶斯模型平均（Bayesian model averaging）实验中不同模型设定下的协变量纳入模式展开分析。借助狄利克雷过程聚类（Dirichlet Process clustering），我们能够识别并刻画构成模型空间的各类模型设定中变量间的依赖结构。我们将该方法应用于两组包含经济增长潜在决定因素的数据集。对由线性回归模型构成的模型空间的后验协变量纳入结构进行聚类后，揭示出经济增长各决定因素间存在有趣的互补与替代模式。