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含时变中介变量与滞后结果的双重差分模型AI 解读

We study difference-in-differences (DiD) designs in which a binary treatment changes an endogenous time-varying (continuous, discrete, or mixed) mediator that in turn affects an outcome. We allow for the inclusion of lagged outcomes in the model. Under our model assumptions, we show that the usual DiD estimand mixes the average direct effect on the treated, the average indirect effect, and a trend bias term. A two-way fixed effects (TWFE) regression that controls for the…

处理变量误分类时的局部平均处理效应与边际处理效应AI 解读

This paper studies identification of the local average and marginal treatment effects (LATE and MTE) with a misclassified binary treatment variable. We derive bounds on the (generalized) LATE and exploit its relationship with the MTE to further bound the MTE. Indeed, under some standard assumptions, the MTE is a limit of the ratio of the variation in the conditional expectation of the observed outcome given the instrument to the variation in the true propensity score, which…

rdid 与 rdidstag：稳健双重差分的 Stata 命令AI 解读

This article provides a Stata package for the implementation of the robust difference-in-differences (RDID) method developed in Ban and Kédagni (2023). It contains three main commands: $\texttt{rdid}$, $\texttt{rdid_dy}$, $\texttt{rdidstag}$, which we describe in the introduction and the main text. We illustrate these commands through simulations and empirical examples.

稳健双重差分模型AI 解读

The difference-in-differences (DID) method identifies the average treatment effects on the treated (ATT) under mainly the so-called parallel trends (PT) assumption. The most common and widely used approach to justify the PT assumption is the pre-treatment period examination. If a null hypothesis of the same trend in the outcome means for both treatment and control groups in the pre-treatment periods is rejected, researchers believe less in PT and the DID results. This paper…

不完美工具变量下处理效应的非参数界AI 解读

This paper extends the identification results in Nevo and Rosen (2012) to nonparametric models. We derive nonparametric bounds on the average treatment effect when an imperfect instrument is available. As in Nevo and Rosen (2012), we assume that the correlation between the imperfect instrument and the unobserved latent variables has the same sign as the correlation between the endogenous variable and the latent variables. We show that the monotone treatment selection and…