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Found 7 papers, 1 papers with code
Two-way Fixed Effects and Differences-in-Differences Estimators in Heterogeneous Adoption Designs
If there are stayers with a period-two treatment equal to 0, the robust estimator is a difference-in-differences (DID) estimator using stayers as the control group.
Difference-in-Differences Estimators with Continuous Treatments and no Stayers
However, such estimators are not robust to heterogeneous treatment effects in general; they also rely on the linearity of treatment effects.
Estimating the Gains (and Losses) of Revenue Management
Despite the wide adoption of revenue management in many industries such as airline, railway, and hospitality, there is still scarce empirical evidence on the gains or losses of such strategies compared to uniform pricing or fully flexible strategies.
A Robust Permutation Test for Subvector Inference in Linear Regressions
We develop a new permutation test for inference on a subvector of coefficients in linear models.
Partially Linear Models under Data Combination
We study partially linear models when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked.
Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey
Linear regressions with period and group fixed effects are widely used to estimate policies' effects: 26 of the 100 most cited papers published by the American Economic Review from 2015 to 2019 estimate such regressions.
Two-way Fixed Effects and Differences-in-Differences Estimators with Several Treatments
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.
