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Using copulas to estimate the coefficient of a binary endogenous regressor in a Poisson regression: Application to the effect of insurance on doctor visits

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  • David Zimmer

Abstract

This paper presents a copula‐based method for identifying and estimating the coefficient of a binary endogenous regressor in a Poisson regression. The method offers advantages over existing approaches. Most importantly, it relies upon standard maximum likelihood approaches, and it does not require numerical integration. Further, as part of its implementation, the method provides a convenient test for the presence of endogeneity. The empirical application investigates the effect of insurance status (a binary measure) on doctor visits (a count measure).

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  • David Zimmer, 2018. "Using copulas to estimate the coefficient of a binary endogenous regressor in a Poisson regression: Application to the effect of insurance on doctor visits," Health Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 545-556, March.
    • Handle: RePEc:wly:hlthec:v:27:y:2018:i:3:p:545-556
    DOI: 10.1002/hec.3605
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    Cited by:

    1. Sukjin Han & Sungwon Lee, 2019. "Estimation in a generalization of bivariate probit models with dummy endogenous regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 994-1015, September.
    2. Giampiero Marra & Rosalba Radice & David M. Zimmer, 2020. "Estimating the binary endogenous effect of insurance on doctor visits by copula‐based regression additive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 953-971, August.

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