Estimation of multivariate probit models via bivariate probit
John Mullahy
University of Wisconsin–Madison
National University of Ireland Galway
and National Bureau of Economic Research
Madison, WI
[email protected]
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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 considered at each
estimation stage. This approach provides a consistent estimator of all the
multivariate probit model's parameters under the same assumptions required for
consistent estimation via mvprobit, and simulation exercises I provide
suggest no loss of estimator precision relative to mvprobit.
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John Mullahy
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bvpmvp(), bvopmvop(), multivariate probit models, bivariate probit
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