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Multivariate probit regression using simulated maximum likelihood

Author

Listed:
  • Lorenzo Cappellari

    (Universita del Piemonte Orientale and University of Essex)

  • Stephen P. Jenkins

    (University of Essex)

Abstract

We discuss the application of the GHK simulation method for maximum likelihood estimation of the multivariate probit regression model and describe and illustrate a Stata program mvprobit for this purpose. Copyright 2003 by StataCorp LP.

Suggested Citation

  • Lorenzo Cappellari & Stephen P. Jenkins, 2003. "Multivariate probit regression using simulated maximum likelihood," Stata Journal, StataCorp LP, vol. 3(3), pages 278-294, September.
    • Handle: RePEc:tsj:stataj:v:3:y:2003:i:3:p:278-294
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    References listed on IDEAS

    as
    1. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    2. Borsch-Supan, Axel & Hajivassiliou, Vassilis A., 1993. "Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models," Journal of Econometrics, Elsevier, vol. 58(3), pages 347-368, August.
    3. V A Hajivassiliou, 1997. "Some Practical Issues in Maximum Simulated Likelihood," STICERD - Econometrics Paper Series 340, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
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