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Fitting the Bivariate Mixed Poisson Regression Model by Maximum Simulated Likelihood

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Listed:
  • Jenkins, Stephen

    (London School of Economics)

  • Rios-Avila, Fernando

    (London School of Economics)

Abstract

We introduce bimpoisson, a program to fit the bivariate mixed Poisson regression model by maximum simulated likelihood (MSL) as per Munkin and Trivedi (The Econometrics Journal, 1999). Options include: sampling function or standard MSL; pseudo-random uniform or Halton draws, antithetic acceleration, and a first-order bias correction. We also provide post-estimation tools to predict conditional count probabilities and expected counts. We examine bimpoisson’s performance using Monte Carlo simulation analysis and provide two empirical illustrations using data from Xu and Hardin (The Stata Journal, 2016) and Munkin and Trivedi (1999). We provide practical advice about which MSL estimator and types of draws and number to use.

Suggested Citation

  • Jenkins, Stephen & Rios-Avila, Fernando, 2026. "Fitting the Bivariate Mixed Poisson Regression Model by Maximum Simulated Likelihood," IZA Discussion Papers 18606, IZA Network @ LISER.
    • Handle: RePEc:iza:izadps:dp18606
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    References listed on IDEAS

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    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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