Estimation of limited dependent variable models with dummy endogenous regressors: Simple strategies for empirical practice
Estimation of limited dependent variable models with dummy endogenous regressors: Simple strategies for empirical practice
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DOI：
10.1198/07350010152472571
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发表时间：
2001-01-01
影响因子：
3
通讯作者：
Angrist, JD
中科院分区：
文献类型：
--
作者：
Angrist, JD
应用经济学家长期以来一直在努力解决如何在具有二元和非负结果的模型中容纳二元内生回归变量的问题。我在这里认为，有限的因变量的大部分困难来自于对结构参数的关注，如指数系数，而不是因果效应。一旦估计的对象被视为治疗的因果效应，就有几种简单的策略可用。这些模型包括传统的两阶段最小二乘法、条件均值的乘法模型、非线性因果模型的线性近似、分布效应模型。和分位数回归与内生二元回归。文章中讨论的估计策略，说明了使用多胞胎估计生育对就业状况和工作时间的影响。
Applied economists have long struggled with the question of how to accommodate binary endogenous regressors in models with binary and nonnegative outcomes. I argue here that much of the difficulty with limited dependent variables comes from a focus on structural parameters, such as index coefficients, instead of causal effects. Once the object of estimation is taken to be the causal effect of treatment, several simple strategies are available. These include conventional two-stags least squares, multiplicative models for conditional means, linear approximation of nonlinear causal models, models for distribution effects. and quantile regression with an endogenous binary regressor. The estimation strategies discussed in the article are illustrated by using multiple births to estimate the effect of childbearing on employment status and hours of work.