Multivariate probit regression using simulated maximum likelihood
AbstractWe discuss the application of the GHK simulation method to maximum likelihood estimation of the multivariate probit regression model, and describe and illustrate a Stata program mvprobit for this purpose.
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Bibliographic InfoPaper provided by Stata Users Group in its series United Kingdom Stata Users' Group Meetings 2003 with number 10.
Date of creation: 16 Mar 2003
Date of revision:
Other versions of this item:
- Lorenzo Cappellari & Stephen P. Jenkins, 2003. "Multivariate probit regression using simulated maximum likelihood," Stata Journal, StataCorp LP, vol. 3(3), pages 278-294, September.
- NEP-ALL-2003-05-29 (All new papers)
- NEP-DCM-2003-05-29 (Discrete Choice Models)
- NEP-LTV-2003-05-29 (Unemployment, Inequality & Poverty)
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- Vassilis A. Hajivassiliou & Axel Borsch-Supan, 1990.
"Smooth Unbiased Multivariate Probability Simulators for Maximum Likelihood Estimation of Limited Dependent Variable Models,"
Cowles Foundation Discussion Papers
960, Cowles Foundation for Research in Economics, Yale University.
- 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.
- Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
- V A Hajivassiliou, 1997. "Some Practical Issues in Maximum Simulated Likelihood," STICERD - Econometrics Paper Series /1997/340, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
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