Estimating nonseparable models with mismeasured endogenous variables
含测量误差内生变量的非可分模型估计AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要以非可分结构系统识别内生变量的边际效应，并用条件独立性处理内生性，直接对应结构估计与因果推断。
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含测量误差内生变量的非可分模型估计
- 研究问题
- 研究非可分结构系统中，当内生解释变量存在测量误差时，如何识别和估计给定协变量的平均边际效应。
- 方法与思路
- 作者使用协变量作为控制变量处理内生性，并从含误差的观测恢复真实解释变量分布，再估计条件及加权平均边际效应。
- 材料陈述的贡献
- 摘要称获得多类估计量的收敛率和渐近正态性；儿童成绩应用提示收入效应较既有认识更大，且随收入和父母能力变化。
- 阅读关注点
- 应核对控制变量所需的条件独立性、真实变量分布恢复的识别条件，以及不同加权效应对应的收敛率和应用解释。
- 阅读边界
- 仅据所给OpenAlex版本摘要，测量误差结构、识别条件细节和收入效应数值均未说明；不推断其他版本变化。
原文摘要
We study the identification and estimation of covariate-conditioned average marginal effects of endogenous regressors in nonseparable structural systems when the regressors are mismeasured. We control for the endogeneity by making use of covariates as control variables; this ensures conditional independence between the endogenous causes of interest and other unobservable drivers of the dependent variable. Moreover, we recover distributions of the underlying true causes from their error-laden measurements to deliver consistent estimators. We obtain uniform convergence rates and asymptotic normality for estimators of covariate-conditioned average marginal effects, faster convergence rates for estimators of their weighted averages over instruments, and root-n consistency and asymptotic normality for estimators of their weighted averages over control variables and regressors. We investigate their finite-sample behavior using Monte Carlo simulation and apply new methods to study the impact of family income on child achievement measured by math and reading scores, using a matched mother–child subsample of the National Longitudinal Survey of Youth. Our findings suggest that these effects are considerably larger than previously recognized, and depend on parental abilities and family income. This underscores the importance of measurement errors, endogeneity of family income, nonlinearity of income effects, and interactions between causes of child achievement.