This article proposes a two recursive equation model to correct for endogeneity in latent class Probit models. Concretely, it is assumed that there exists an endogenous and continuous variable defined as a predictor, while unobserved heterogeneity is conceptualized as a vector of parameters that varies across individuals following a discrete distribution. A Maximum Likelihood Estimator is provided to estimate the model parameters based on normally distributed random terms and a free code in R software is provided to carry out the estimation procedure. A small Monte Carlo experiment is carried out to analyze the properties of the estimator. Finally, the estimator is applied to analyze the heterogeneous effects of weight on mental well-being.

本文提出了一个二递归方程模型来校正潜在类 Probit 模型中的内生性。具体而言，假设存在定义为预测变量的内生连续变量，而未观察到的异质性被概念化为参数向量，该向量在个体之间遵循离散分布而变化。提供最大似然估计器以基于正态分布随机项估计模型参数，并提供 R 软件中的免费代码来执行估计程序。进行了一个小的蒙特卡罗实验来分析估计器的特性。最后，应用估计器来分析体重对心理健康的异质影响。