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跨期处理效应的双重差分估计AI 解读

Abstract We study treatment-effect estimation using panel data. The treatment may be nonbinary, nonabsorbing, and the outcome may be affected by treatment lags. We make a parallel-trends assumption and propose event-study estimators of the effect of being exposed to a weakly higher treatment dose for ℓ periods. We also propose normalized estimators that estimate a weighted average of the effects of the current treatment and its lags. We also analyze commonly used two-way…

多种处理下的双向固定效应与双重差分估计AI 解读

We study two-way-fixed-effects regressions (TWFE) with several treatment variables. Under a parallel trends assumption, we show that the coefficient on each treatment identifies a weighted sum of that treatment’s effect, with possibly negative weights, plus a weighted sum of the effects of the other treatments. Thus, those estimators are not robust to heterogeneous effects and may be contaminated by other treatments’ effects. We further show that omitting a treatment from…

异质处理效应下的双向固定效应与双重差分：综述AI 解读

Summary Linear regressions with period and group fixed effects are widely used to estimate policie’s effects: 26 of the 100 most cited papers published by the American Economic Review from 2015 to 2019 estimate such regressions. It has recently been shown that those regressions may produce misleading estimates if the policy’s effect is heterogeneous between groups or over time, as is often the case. This survey reviews a fast-growing literature that documents this issue and…

容许逆从者？不依赖单调性的局部平均处理效应AI 解读

Instrumental variables (IVs) are commonly used to estimate the effects of some treatments. A valid IV should be as good as randomly assigned, it should not have a direct effect on the outcome, and it should not induce any unit to forgo treatment. This last condition, the so-called monotonicity condition, is often implausible. This paper starts by showing that actually, IVs are still valid under a weaker condition than monotonicity. It then derives conditions that are…