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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. © Springer Science + Business Media, Inc. 2005.

Suggested Citation

  • Patrick Waelbroeck, 2005. "Computational issues in the sequential probit model: A Monte Carlo study," ULB Institutional Repository 2013/165966, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:ulb:ulbeco:2013/165966
    Note: SCOPUS: ar.j
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    Citations

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    Cited by:

    1. John-Paul Ferguson, 2008. "The Eyes of the Needles: A Sequential Model of Union Organizing Drives, 1999–2004," ILR Review, Cornell University, ILR School, vol. 62(1), pages 3-21, October.
    2. 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.
    3. David, Bounie & Abel, François & Patrick, Waelbroeck, 2016. "Debit card and demand for cash," Journal of Banking & Finance, Elsevier, vol. 73(C), pages 55-66.
    4. Raphaële Préget, 2011. "What is the cost of low participation in French Timber auctions?," Post-Print hal-00670762, HAL.
    5. Daisuke Nagakura & Masahito Kobayashi, 2009. "Testing The Sequential Logit Model Against The Nested Logit Model," The Japanese Economic Review, Japanese Economic Association, vol. 60(3), pages 345-361, September.
    6. Maksym, Obrizan, 2010. "A Bayesian Model of Sample Selection with a Discrete Outcome Variable," MPRA Paper 28577, University Library of Munich, Germany.

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