Computational issues in the sequential probit model: A Monte Carlo study

Patrick Waelbroeck

ULB Institutional Repository from ULB -- Universite Libre de Bruxelles

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.

Keywords: Metropolis-Gibbs; Sequential probit; Simulated annealing; Simulated maximum likelihood (search for similar items in EconPapers)
Date: 2005-10
Note: SCOPUS: ar.j
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Citations: View citations in EconPapers (6)

Published in: Computational economics (2005) v.26 n° 2,p.141-161

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Journal Article: Computational Issues in the Sequential Probit Model: A Monte Carlo Study (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:ulb:ulbeco:2013/165966

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