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教育回报的分样本工具变量估计AI 解读

Two-stage least squares is biased in the same direction as ordinary least squares even in very large samples. The authors propose a split-sample instrumental variables estimator that is not biased toward ordinary least squares. Split-sample instrumental variables uses one-half of a sample to estimate parameters of the first-stage equation. Estimated first-stage parameters are then used to construct fitted values and second-stage parameter estimates in the other half sample.…

入学年龄对教育程度的影响：结合两样本矩的工具变量应用AI 解读

We present a model in which compulsory school attendance laws, which typically require school attendance until a specified birthday, induce a relationship between years of schooling and age at school entry. Variation in school starting age created by children's dates of birth provides a natural experiment for estimating the effect of age at school entry. Because no large data set contains information on both age at school entry and educational attainment, we use an…