Revisiting Event Study Designs

By Kirill Borusyak, Xavier Jaravel. 2017

446 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9ee7a062d5640ac1004f24ec4c3079459f38d2b5

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Citations in the corpus

Difference-in-Differences with multiple time periods (2018, background): “For instance, one can combine our proposal with Callaway and Li (2017) in order to consider group-time quantile treatment effects.”
How Much Should We Trust Staggered Difference-In-Differences Estimates? (2021, background): “…where a treatment occurs across units at different points in time, 5The body of work examining these issues include: Athey and Imbens (2018), Borusyak and Jaravel (2017),Callaway and Sant’Anna (2020), Goodman-Bacon (2019), Imai and Kim (2020), Strezhnev (2018), and Sun and Abraham (2020).”
Pre-Event Trends in the Panel Event-Study Design (2019, background): “Although a static model does not capture the post-treatment dynamics of the outcomes, it is a common way to summarize the effect size (Borusyak and Jaravel 2017).”
The Promise and Pitfalls of Differences-in-Differences: Reflections on 16 and Pregnant and Other Applications (2018, unknown): “Borusyak and Jaravel (2017) discussed in depth the challenges of using DiD in cases with group-specific time trends and time-specific treatment effects.”
Gender gaps in labor informality: The motherhood effect (2019, unknown): “See Borusyak and Jaravel (2018).”
Synthetic Difference In Differences (2018, unknown): “…the time weights to both DID, where all pretreatment periods are given equal weight, and to event studies where typically the last pretreatment period is used as a comparison and so implicitly gets all the weight ( e.g., Borusyak and Jaravel 2016; Freyaldenhoven, Hansen, and Shapiro 2019 .”
What’s trending in difference-in-differences? A synthesis of the recent econometrics literature (2022, unknown): “can potentially improve efficiency since it averages over more periods, and indeed Borusyak et al. (2021) show that this is necessarily the case in a model for Yitp8q with homoskedastic errors. On the other hand, the validity of the estimator depends on a stronger parallel trends assumption, which i”
Difference-in-Differences with a Continuous Treatment (2021, unknown): “[7] Kirill Borusyak and Xavier Jaravel.”
Efficient Estimation for Staggered Rollout Designs (2021, background): “…TWFE models to recover a sensible causal estimand under treatment effect heterogeneity and have proposed alternative estimators with better properties (Borusyak and Jaravel, 2018; Goodman-Bacon, 2021; de Chaisemartin and D’Haultfoeuille, 2020; Callaway and Sant’Anna, 2021; Sun and Abraham, 2021).”
Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey (2021, background): “9Borusyak and Jaravel (2017) have also coined the “forbidden comparisons” expression we borrow here.”
Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects (2018, unknown): “More recently, Borusyak and Jaravel ( 2017 ) , Abraham and Sun ( 2018 ) , Athey and Imbens ( 2018 ) , Callaway and Sant’Anna ( 2018 ) , and Goodman-Bacon ( 2018 ) study the special case of staggered adoption designs, where the treatment of a group is weakly increasing over time.”