Estimation of multivariate probit models via bivariate probit

John Mullahy ()
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John Mullahy: University of Wisconsin–Madison

Stata Journal, 2016, vol. 16, issue 1, 37-51

Abstract: In this article, I suggest the utility of fitting multivariate probit models using a chain of bivariate probit estimators. This approach is based on Stata’s biprobit and suest commands and is driven by a Mata function, bvpmvp(). I discuss two potential advantages of the approach over the mvprobit command (Cappellari and Jenkins, 2003, Stata Journal 3: 278–294): significant reductions in computation time and essentially unlimited dimensionality of the outcome set. Computation time is reduced because the approach does not rely on simulation methods; unlimited dimensionality arises because only pairs of outcomes are con- sidered at each estimation stage. This approach provides a consistent estimator of all the multivariate probit model’s parameters under the same assumptions re- quired for consistent estimation via mvprobit, and simulation exercises I provide suggest no loss of estimator precision relative to mvprobit. Copyright 2016 by StataCorp LP.

Keywords: bvpmvp(); bvopmvop(); multivariate probit models; bivariate probit (search for similar items in EconPapers)
Date: 2016
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