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

28 Pages Posted: 7 Jun 2004

Date Written: May 2004

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 likelihood and by Bayesian MCMC algorithms. We provide Monte Carlo evidence on the relative performance of both estimators. Given the numerical difficulties associated with the estimation procedures, we advise the applied researcher to use both stochastic optimization algorithms in the Simulated Maximum Likelihood approach as well as Bayesian MCMC algorithms to check the compatibility of the results.

Keywords: Sequential probit, simulated maximum likelihood, simulated annealing, Metropolis-Gibbs

JEL Classification: C11, C15, C35, C63

Suggested Citation

Waelbroeck, Patrick, Computational Issues in the Sequential Probit Model: A Monte Carlo Study (May 2004). Available at SSRN: https://ssrn.com/abstract=555081 or http://dx.doi.org/10.2139/ssrn.555081

Patrick Waelbroeck (Contact Author)

Telecom ParisTech ( email )

46 rue Barrault
F-75634 Paris, Cedex 13
France

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