Copula-based random effects models for clustered data

Santiago Pereda-Fernández

No 1092, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area

Abstract: Sorting and spillovers can create correlation in individual outcomes. In this situation, standard discrete choice estimators cannot consistently estimate the probability of joint and conditional events, and alternative estimators can yield incoherent statistical models or intractable estimators. I propose a random effects estimator that models the dependence among the unobserved heterogeneity of individuals in the same cluster using a parametric copula. This estimator makes it possible to compute joint and conditional probabilities of the outcome variable, and is statistically coherent. I describe its properties, establishing its efficiency relative to standard random effects estimators, and propose a specification test for the copula. The likelihood function for each cluster is an integral whose dimension equals the size of the cluster, which may require high-dimensional numerical integration. To overcome the curse of dimensionality from which methods like Monte Carlo integration suffer, I propose an algorithm that works for Archimedean copulas. I illustrate this approach by analysing labour supply in married couples.

Keywords: Copula; high-dimensional integration; nonlinear panel data. (search for similar items in EconPapers)
JEL-codes: C23 C25 J22 (search for similar items in EconPapers)
Date: 2016-12
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Copula-Based Random Effects Models for Clustered Data (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:bdi:wptemi:td_1092_16

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