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Econometrics 2017, 5(1), 10;

A Note on Identification of Bivariate Copulas for Discrete Count Data

Department of Economics, Indiana University Bloomington, 100 South Woodlawn Avenue, Bloomington, IN 47405-7104, USA
Department of Economics, Western Kentucky University, 1906 College Heights Blvd., Bowling Green, KY 42101, USA
Author to whom correspondence should be addressed.
Academic Editor: Marc S. Paolella
Received: 5 August 2016 / Revised: 26 January 2017 / Accepted: 7 February 2017 / Published: 15 February 2017
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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. View Full-Text
Keywords: ties; Monte Carlo; Gaussian; Clayton; Gumbel ties; Monte Carlo; Gaussian; Clayton; Gumbel
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Trivedi, P.; Zimmer, D. A Note on Identification of Bivariate Copulas for Discrete Count Data. Econometrics 2017, 5, 10.

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