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Estimation of Multivariate Probit Models via Bivariate Probit.

ABSTRACT: This paper suggests the utility of estimating multivariate probit (MVP) models using a chain of bivariate probit estimators. The proposed approach is based on Stata's biprobit and suest procedures and is driven by a Mata function. Two potential advantages over Stata's mvprobit procedure are suggested: significant reductions in computation time; and essentially unlimited dimensionality of the outcome set. The time savings arise because the proposed approach does not rely simulation methods; the dimension advantage arises because only pairs of outcomes are considered at each estimation stage. Importantly, the proposed approach provides a consistent estimator of all the MVP model's parameters under the same assumptions required for consistent estimation via mvprobit, and simulation exercises reported below suggest no loss of estimator precision relative to mvprobit.

SUBMITTER: Mullahy J

PROVIDER: S-EPMC6957230 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Estimation of Multivariate Probit Models via Bivariate Probit.

Mullahy John J

The Stata journal 20160301 1

This paper suggests the utility of estimating multivariate probit (MVP) models using a chain of bivariate probit estimators. The proposed approach is based on Stata's biprobit and suest procedures and is driven by a Mata function. Two potential advantages over Stata's mvprobit procedure are suggested: significant reductions in computation time; and essentially unlimited dimensionality of the outcome set. The time savings arise because the proposed approach does not rely simu ...[more]

PMID: 31933544

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