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Semiparametric Count Data Modeling with an Application to Health Service Demand

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  • Bach, P.
  • Farbmacher, H.
  • Spindler, M.

Abstract

Heterogeneous effects are prevalent in many economic settings. As the functional form between outcomes and regressors is often unknown apriori, we propose a semiparametric negative binomial count data model based on the local likelihood approach and generalized product kernels, and apply the estimator to model demand for health care. The local likelihood framework allows us to leave the functional form of the conditional mean unspecified while still exploiting basic assumptions in the count data literature (e.g., non-negativity). The generalized product kernels allow us to simultaneously model discrete and continuous regressors, which reduces the curse of dimensionality and increases its applicability as many regressors in the demand model for health care are discrete.

Suggested Citation

  • Bach, P. & Farbmacher, H. & Spindler, M., 2016. "Semiparametric Count Data Modeling with an Application to Health Service Demand," Health, Econometrics and Data Group (HEDG) Working Papers 16/20, HEDG, c/o Department of Economics, University of York.
  • Handle: RePEc:yor:hectdg:16/20
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    References listed on IDEAS

    as
    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273, December.
    2. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    3. José Santos & M. Neves, 2008. "A local maximum likelihood estimator for Poisson regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 68(3), pages 257-270, November.
    4. Amy Finkelstein & Sarah Taubman & Bill Wright & Mira Bernstein & Jonathan Gruber & Joseph P. Newhouse & Heidi Allen & Katherine Baicker, 2012. "The Oregon Health Insurance Experiment: Evidence from the First Year," The Quarterly Journal of Economics, Oxford University Press, vol. 127(3), pages 1057-1106.
    5. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    6. McLeod, Logan, 2011. "A nonparametric vs. latent class model of general practitioner utilization: Evidence from Canada," Journal of Health Economics, Elsevier, vol. 30(6), pages 1261-1279.
    7. Yee, Thomas W., 2014. "Reduced-rank vector generalized linear models with two linear predictors," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 889-902.
    8. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, number 8355.
    9. Hayfield, Tristen & Racine, Jeffrey S., 2008. "Nonparametric Econometrics: The np Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i05).
    10. 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 313-336, May-June.
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    More about this item

    Keywords

    semiparametric; nonparametric; count data; health care demand;

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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