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Simulation-Based Estimation of the Structural Errors-in-Variables Negative Binomial Regression Model with an Application

Author

Listed:
  • Jie Q. Guo

    (Wylie Hall 105 Department of Economics Indiana University)

  • Tong Li

    (Wylie Hall 105 Department of Economics Indiana University)

Abstract

This paper studies the effects and estimation of errors-in-variables negative binomial regression model. We prove that in the presence of measurement errors, in general, maximum likelihood estimator of the overdispersion using the observed data is biased upward. We adopt a structural approach assuming that the distribution of the latent variables is known and propose a simulation-based corrected maximum likelihood estimator and a simulation-based corrected score estimator to estimate the errors-in-variables negative binomial model. Though having similar asymptotic properties to the simulation-based corrected maximum likelihood estimator, the simulation-based corrected score estimator has a better finite sample performance as evidenced by the Monte Carlo studies. An application to the elderly demand for medical care using Medical Expenditure Panel Study is illustrated.

Suggested Citation

  • Jie Q. Guo & Tong Li, 2001. "Simulation-Based Estimation of the Structural Errors-in-Variables Negative Binomial Regression Model with an Application," Annals of Economics and Finance, Society for AEF, vol. 2(1), pages 101-122, May.
    • Handle: RePEc:cuf:journl:y:2001:v:2:i:1:p:101-122
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    Citations

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    Cited by:

    1. A. Colin Cameron & Tong Li & Pravin K. Trivedi & David M. Zimmer, 2004. "Modelling the differences in counted outcomes using bivariate copula models with application to mismeasured counts," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 566-584, December.
    2. A. Colin Cameron & Tong Li & Pravin K. Trivedi & David M. Zimmer, 2004. "Modelling the differences in counted outcomes using bivariate copula models with application to mismeasured counts," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 566-584, December.

    More about this item

    Keywords

    Count Data; Measurement Errors; Overdispersion; Simulation-based Corrected Score Estimator; Health Care Demand;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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