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Biases in Maximum Simulated Likelihood Estimation of Bivariate Models

Author

Listed:
  • Jumamyradov Maksat
  • Munkin Murat K.

    (Department of Economics, University of South Florida, Tampa, FL, USA)

Abstract

This paper finds that the maximum simulated likelihood (MSL) estimator produces substantial biases when applied to the bivariate normal distribution. A specification of the random parameter bivariate normal model is considered, in which a direct comparison between the MSL and maximum likelihood (ML) estimators is feasible. The analysis shows that MSL produces biased results for the correlation parameter. This paper also finds that the MSL estimator is biased for the bivariate Poisson-lognormal model, developed by Munkin and Trivedi (1999. “Simulated Maximum Likelihood Estimation of Multivariate Mixed-Poisson Regression Models, with Application.” The Econometrics Journal 2: 29–48). A simulation study is conducted, which shows that MSL leads to serious inferential biases, especially large when variance parameters in the true data generating process are small. The MSL estimator produces biases in the estimated marginal effects, conditional means and probabilities of count outcomes.

Suggested Citation

  • Jumamyradov Maksat & Munkin Murat K., 2022. "Biases in Maximum Simulated Likelihood Estimation of Bivariate Models," Journal of Econometric Methods, De Gruyter, vol. 11(1), pages 55-70, January.
    • Handle: RePEc:bpj:jecome:v:11:y:2022:i:1:p:55-70:n:6
    DOI: 10.1515/jem-2021-0003
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    More about this item

    Keywords

    Maximum Simulated Likelihood; Simulation Biases; Bivariate Models;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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