Multivariate probit regression using simulated maximum likelihood
AbstractWe 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.
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Bibliographic InfoArticle provided by StataCorp LP in its journal Stata Journal.
Volume (Year): 3 (2003)
Issue (Month): 3 (September)
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Web page: http://www.stata-journal.com/
Other versions of this item:
- Lorenzo Cappellari & Stephen P. Jenkins, 2003. "Multivariate probit regression using simulated maximum likelihood," United Kingdom Stata Users' Group Meetings 2003 10, Stata Users Group.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
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