Copula-Based Random Effects Models for Clustered Data
针对二元选择面板数据，提出一种用Copula刻画同一聚类内个体未观测异质性相关性的随机效应估计量，并给出渐近效率讨论、模型设定检验及高维积分算法，应用于已婚夫妇劳动供给分析。
In a binary choice panel data framework, probabilities of the outcomes of several individuals depend on the correlation of the unobserved heterogeneity. I propose a random effects estimator that models the correlation of the unobserved heterogeneity among individuals in the same cluster using a copula. I discuss the asymptotic efficiency of the estimator relative to standard random effects estimators, and to choose the copula I propose a specification test. The implementation of the estimator requires the numerical approximation of high-dimensional integrals, for which I propose an algorithm that works for Archimedean copulas that does not suffer from the curse of dimensionality. This method is illustrated with an application of labor supply in married couples, finding that about one half of the difference in probability of a woman being employed when her husband is also employed, relative to those whose husband is unemployed, is explained by correlation in the unobservables. Supplementary materials for this article are available online.