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Joint Inference for the Regression Discontinuity Effect and Its External Validity

回归不连续效应及其外部有效性的联合推断

ABSTRACT The external validity of regression discontinuity designs is crucial for informing policy but is rarely examined in applied work. To advance empirical practice, we propose a joint inference procedure for the treatment effect and its local external validity, captured by the treatment effect derivative (TED), within a robust bias correction framework. We further introduce a locally linear treatment effects assumption, which extends the scope of the TED and enables…

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Robustify and tighten the Lee bounds: a sample selection model under stochastic monotonicity and symmetry assumptions

使Lee界更稳健且更紧:随机单调性与对称性下的样本选择模型

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…

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