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A Semi-Nonparametric Estimator For Counts With An Endogenous Dummy Variable

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  • Andres Romeu-Santana

    (Universitat Autonoma de Barcelona)

  • Angel Marcos Vera-Hernndez

    (Universitat Autonoma de Barcelona)

Abstract

Treating endogeneity and flexibility in such a way that efficiency is not sacrificed constitutes a rising point of interest in count data models. Endogeneity typically appears when unobservable characteristics affect both the count outcome and individuals decision represented by the dummy variable. Thus, we use numerical quadrature to integrate out the unobserved components. Also count data often shows empirical distributions that fit poorly the standard models in the literature. We estimate by Full Information Maximum Likelihood, where we allow for a polynomial expansion of the Poisson specification. Due to the multipe local optima appearing, we apply the Simulated Annealing optimization algorithm. We also develop statistics for sensibility analysis. Model evaluation is performed using measures of goodness of fit, information criteria, likelihood ratio and scores tests. We test our model using data on the number of trips by households and number of physician office visits. In the first case, a low polynomial degree suffices to yield a good fit, while in the latter, results are still to come.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 37.

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Date of creation: 05 Jul 2000
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Handle: RePEc:sce:scecf0:37

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Postal: CEF 2000, Departament d'Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas, 25,27, 08005, Barcelona, Spain
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References

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  1. Alberto HOLLY & Lucien GARDIOL & Gianfranco DOMENIGHETTI & Brigitte BISIG, 1998. "An Econometric Model of Health Care Utilization and Health Insurance in Switzerland," Cahiers de Recherches Economiques du Département d'Econométrie et d'Economie politique (DEEP) 9803, Université de Lausanne, Faculté des HEC, DEEP.
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  3. Mullahy, John, 1998. "Much ado about two: reconsidering retransformation and the two-part model in health econometrics," Journal of Health Economics, Elsevier, vol. 17(3), pages 247-281, June.
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  5. Grogger, Jeffrey, 1990. "A simple test for exogeneity in probit, logit, and poisson regression models," Economics Letters, Elsevier, vol. 33(4), pages 329-332, August.
  6. Jerry A. Hausman & Bronwyn H. Hall & Zvi Griliches, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," NBER Technical Working Papers 0017, National Bureau of Economic Research, Inc.
  7. Donald S. Kenkel & Joseph V. Terza, 2001. "The effect of physician advice on alcohol consumption: count regression with an endogenous treatment effect," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(2), pages 165-184.
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  9. Chiappori, P.A. & Durand, F. & Geoffard, P.Y., 1998. "Moral Hazard and the Demand for Physician Services: First Lessons from a French Natural Experiment," DELTA Working Papers 98-05, DELTA (Ecole normale supérieure).
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  13. Deb, Partha & Trivedi, Pravin K, 1997. "Demand for Medical Care by the Elderly: A Finite Mixture Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 313-36, May-June.
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  22. Cameron, A Colin & Trivedi, Pravin K, 1986. "Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(1), pages 29-53, January.
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Cited by:
  1. Michael Creel, 2002. "Hausman Tests for Inefficient Estimators: Application to Demand for Health Care Service (revised)," UFAE and IAE Working Papers 509.02, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
  2. Michael D. Creel & Montserrat Farell, 2001. "Likelihood-Based Approaches to Modeling Demand for Medical Care," UFAE and IAE Working Papers 498.01, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).

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