Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects
by
de Chaisemartin, Clément
, D’Haultfœuille, Xavier
in
Analysis
/ Causal inference
/ Economics
/ Fixed-effects regression models
/ Mediating effects (Research)
/ Methods
/ Observations
2020
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects
by
de Chaisemartin, Clément
, D’Haultfœuille, Xavier
in
Analysis
/ Causal inference
/ Economics
/ Fixed-effects regression models
/ Mediating effects (Research)
/ Methods
/ Observations
2020
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects
Journal Article
Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects
2020
Request Book From Autostore
and Choose the Collection Method
Overview
Linear regressions with period and group fixed effects are widely used to estimate treatment effects. We show that they estimate weighted sums of the average treatment effects (ATE) in each group and period, with weights that may be negative. Due to the negative weights, the linear regression coefficient may for instance be negative while all the ATEs are positive. We propose another estimator that solves this issue. In the two applications we revisit, it is significantly different from the linear regression estimator.
Publisher
American Economic Association
This website uses cookies to ensure you get the best experience on our website.