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双侧测量误差下的分布效应：代际收入流动应用AI 解读

This paper considers identification and estimation of distributional effect parameters that depend on the joint distribution of an outcome and another variable of interest (“treatment”) in a setting with “two-sided” measurement error—that is, where both variables are possibly measured with error. Examples of these parameters in the context of intergenerational income mobility include transition matrices, rank-rank correlations, and the poverty rate of children as a function…

内生解释变量与内生状态切换回归的控制函数估计AI 解读

We derive simple, multi-step estimation methods for a linear model with heterogeneous coefficients when there are both continuous and discrete endogenous explanatory variables. We consider both cross-sectional and panel data settings. When we extend our model to panel data, we use the Chamberlain–Mundlak device to allow heterogeneity to be correlated with time-varying explanatory variables. We apply the panel data methods we propose to estimation of a housing budget share…

函数系数工具变量模型的局部线性 GMM 估计及教育回报应用AI 解读

We consider the local linear generalized method of moment (GMM) estimation of functional coefficient models with a mix of discrete and continuous data and in the presence of endogenous regressors. We establish the asymptotic normality of the estimator and derive the optimal instrumental variable that minimizes the asymptotic variance-covariance matrix among the class of all local linear GMM estimators. Data-dependent bandwidth sequences are also allowed for. We propose a…

基于随机系数模型的代际收入流动性替代指标AI 解读

SUMMARY We propose an alternative measure of the degree to which income status is transmitted from one generation to another. Our indicator of intergenerational income mobility is based on a random coefficient model, which allows for variation in intergenerational mobility across families due to multiplicative unobserved family‐specific characteristics. This alternative measure suggests that intergenerational income persistence is typically stronger than when…

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