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.

中文翻译：

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通过双变量 Probit 估计多元 Probit 模型。

本文提出了使用双变量概率估计器链来估计多变量概率 (MVP) 模型的效用。所提议的方法基于 Stata 的 biprobit 和 suest 程序，并由 Mata 函数驱动。提出了与 Stata 的 mvprobit 程序相比的两个潜在优势：显着减少计算时间；结果集的维度基本上是无限的。节省时间是因为所提出的方法不依赖于模拟方法；维度优势的出现是因为在每个估计阶段只考虑成对的结果。重要的是，在通过 mvprobit 进行一致估计所需的相同假设下，所提出的方法提供了所有 MVP 模型参数的一致估计器，