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基于双层优化问题的匹配估计量AI 解读

This paper proposes a novel matching estimator where neighbors used and weights are endogenously determined by optimizing a covariate balancing criterion. The estimator is based on finding, for each unit that needs to be matched, sets of observations such that a convex combination of them has the same covariate values as the unit needing matching or with minimized distance between them. We implement the proposed estimator with data from the National Supported Work…

通过导数约束建模内生解释变量与异质性结构方程AI 解读

In this paper, I present a general modeling framework for nonparametric models with endogenous regressors and heterogeneity. I show that many existing models in the literature can be derived from a structural equation with unobserved heterogeneity by imposing constancy assumptions on the first and second derivatives. I consider a less restrictive model that imposes constancy assumptions on the second partial derivative of the structural equation. Assuming the existence of…