Robustify and tighten the Lee bounds: a sample selection model under stochastic monotonicity and symmetry assumptions
提出随机单调性和非参数分布形状约束，以增强Lee边界的稳健性并收紧其范围，该方法不依赖排除限制且可一致估计，适用于实验数据分析。
Summary In the presence of sample selection, Lee’s (2009, Review of Economic Studies 76, 1071–102) non-parametric bounds are a popular tool for estimating a treatment effect. However, the Lee bounds rely on the monotonicity assumption, the empirical validity of which is sometimes unclear. Furthermore, the bounds are often regarded to be wide and less informative even under monotonicity. To address these issues, this study introduces a stochastic version of the monotonicity assumption alongside a non-parametric distributional shape constraint. The former enhances the robustness of the Lee bounds with respect to monotonicity, while the latter helps tighten these bounds. The obtained bounds do not rely on the exclusion restriction and can be root-n consistently estimable, making them practically viable. The potential usefulness of the proposed methods is illustrated by their application to experimental data from an after-school instruction programme.