A note on identification of bivariate copulas for discrete count data

Pravin Trivedi and David Zimmer

Copulas have enjoyed increased usage in many areas of econometrics, including applications with discrete outcomes. However, Genest and Nešlehová (2007) present evidence that copulas for discrete outcomes are not identified, particularly when those discrete outcomes follow count distributions. This paper confirms the Genest and Nešlehová result using a series of simulation exercises. The paper then proceeds to show that those identification concerns diminish if the model has a regression structure such that the exogenous variable(s) generates additional variation in the outcomes and thus more completely covers the outcome domain.

MoreLess

Year of publication:	March 2017
Authors:	Trivedi, Pravin K. ; Zimmer, David ; Chang, I-Lok
Published in:	Econometrics : open access journal. - Basel : MDPI, ISSN 2225-1146, ZDB-ID 2717594-7. - Vol. 5.2017, 1, p. 1-11
Subject:	ties \| Monte Carlo \| Gaussian \| Clayton \| Gumbel \| Monte-Carlo-Simulation \| Monte Carlo simulation \| Multivariate Verteilung \| Multivariate distribution \| Schätztheorie \| Estimation theory \| Statistische Verteilung \| Statistical distribution \| Generalisiertes lineares Modell \| Generalized linear model