Computational Issues in the Sequential Probit Model: A Monte Carlo Study
AbstractWe discuss computational issues in the sequential probit model that have limited its use in applied research. We estimate parameters of the model by the method of simulated maximum likelihood (SML) and by Bayesian MCMC algorithms. We provide Monte Carlo evidence on the relative performance of both estimators and find that the SML procedure computes standard errors of the estimated correlation coefficients that are less reliable. Given the numerical difficulties associated with the estimation procedures, we advise the applied researcher to use both the stochastic optimization algorithm in the Simulated Maximum Likelihood approach and the Bayesian MCMC algorithm to check the compatibility of the results. Copyright Springer Science + Business Media, Inc. 2005
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Society for Computational Economics in its journal Computational Economics.
Volume (Year): 26 (2005)
Issue (Month): 2 (October)
Metropolis–Gibbs; sequential probit; simulated maximum likelihood; simulated annealing;
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.:
- Vassilis A. Hajivassiliou & Daniel L. McFadden & Paul Ruud, 1993.
"Simulation of Multivariate Normal Rectangle Probabilities and their Derivatives: Theoretical and Computational Results,"
_024, Yale University.
- Hajivassiliou, Vassilis & McFadden, Daniel & Ruud, Paul, 1996. "Simulation of multivariate normal rectangle probabilities and their derivatives theoretical and computational results," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 85-134.
- Kathy Cannings & Sophie Mahseredjian & Claude Montmarquette, 1994.
"Entrance Quotas and Admission to Medical Schools: A Sequential Probit Model,"
CIRANO Working Papers
- Cannings, Kathy & Montmarquette, Claude & Mahseredjian, Sophie, 1996. "Entrance quotas and admission to medical schools: a sequential probit model," Economics of Education Review, Elsevier, vol. 15(2), pages 163-174, April.
- Cannings, K. & Montmarquette, C. & Mahseredjian, S., 1994. "Entrance Quotas abs Admission to Medical Schools: A Sequential Probit Model," Cahiers de recherche 9418, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- Cannings, K. & Montmarquette, C. & Mahseredjian, S., 1994. "Entrance Quotas abs Admission to Medical Schools: a Sequential Probit Model," Cahiers de recherche 9418, Universite de Montreal, Departement de sciences economiques.
- John Geweke & Michael Keane & David Runkle, 1994.
"Alternative computational approaches to inference in the multinomial probit model,"
170, Federal Reserve Bank of Minneapolis.
- Geweke, John & Keane, Michael P & Runkle, David, 1994. "Alternative Computational Approaches to Inference in the Multinomial Probit Model," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 609-32, November.
- Keane, Michael P, 1992. "A Note on Identification in the Multinomial Probit Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 193-200, April.
- Schmidt, Peter, 1977. "Estimation of seemingly unrelated regressions with unequal numbers of observations," Journal of Econometrics, Elsevier, vol. 5(3), pages 365-377, May.
- Monjon, Stephanie & Waelbroeck, Patrick, 2003. "Assessing spillovers from universities to firms: evidence from French firm-level data," International Journal of Industrial Organization, Elsevier, vol. 21(9), pages 1255-1270, November.
- McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
- Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
- Maksym, Obrizan, 2010. "A Bayesian Model of Sample Selection with a Discrete Outcome Variable," MPRA Paper 28577, University Library of Munich, Germany.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Guenther Eichhorn) or (Christopher F. Baum).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.