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Computational Issues in the Sequential Probit Model: A Monte Carlo Study

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  • Patrick Waelbroeck

Abstract

We 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

Suggested Citation

  • Patrick Waelbroeck, 2005. "Computational Issues in the Sequential Probit Model: A Monte Carlo Study," Computational Economics, Springer;Society for Computational Economics, vol. 26(2), pages 141-161, October.
  • Handle: RePEc:kap:compec:v:26:y:2005:i:2:p:141-161 DOI: 10.1007/s10614-005-0667-7
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    References listed on IDEAS

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    1. 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.
    2. 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 609-632, November.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Schmidt, Peter, 1977. "Estimation of seemingly unrelated regressions with unequal numbers of observations," Journal of Econometrics, Elsevier, vol. 5(3), pages 365-377, May.
    8. 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.
    9. repec:dau:papers:123456789/13785 is not listed on IDEAS
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    Citations

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    Cited by:

    1. David, Bounie & Abel, François & Patrick, Waelbroeck, 2016. "Debit card and demand for cash," Journal of Banking & Finance, Elsevier, vol. 73(C), pages 55-66.
    2. Maksym, Obrizan, 2010. "A Bayesian Model of Sample Selection with a Discrete Outcome Variable," MPRA Paper 28577, University Library of Munich, Germany.
    3. Gebrenegus Ghilagaber & Paraskevi Peristera, 2014. "Sequential probit modelling of family and community effects on educational progress among children to Polish and Turkish immigrants in Sweden," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(6), pages 3243-3252, November.
    4. Raphaële Préget, 2011. "What is the cost of low participation in French Timber auctions?," Post-Print hal-00670762, HAL.

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