{did2s}: Two-Stage Difference-in-Differences
Recent work has highlighted the difficulties of estimating difference-in-differences models when treatment timing occurs at different times for different units. This article introduces the R package did2s which implements the estimator introduced in Gardner (2021). The article provides an approachable review of the underlying econometric theory and introduces the syntax for the function did2s. Further, the package introduces a function, event_study, that provides a common syntax for all the modern event-study estimators and plot_event_study to plot the results of each estimator.
Code (1)
Tasks
Vocal Bursts Valence PredictionSimilar Papers 제목 키워드 기반
Two-stage differences in differences
A recent literature has shown that when adoption of a treatment is staggered and average treatment effects vary across groups and over time, difference-in-differences regression does not identify an easily interpretable …
regressionVocal Bursts Valence PredictionA Two-Stage Weighting Framework for Multi-Source Domain Adaptation
Discriminative learning when training and test data belong to different distributions is a challenging and complex task. Often times we have very few or no labeled data from the test or target distribution but may have p…
Domain AdaptationVocal Bursts Valence PredictionDifference-in-Differences with Multiple Events
This paper studies staggered Difference-in-Differences (DiD) design when there is a second event confounding the target event. When the events are correlated, the treatment and the control group are unevenly exposed to t…
Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding
This study considers various semiparametric difference-in-differences models under different assumptions on the relation between the treatment group identifier, time and covariates for cross-sectional and panel data. The…
Vocal Bursts Intensity PredictionA Concept for Efficient Scalability of Automated Driving Allowing for Technical, Legal, Cultural, and Ethical Differences
Efficient scalability of automated driving (AD) is key to reducing costs, enhancing safety, conserving resources, and maximizing impact. However, research focuses on specific vehicles and context, while broad deployment …
Transfer Learning