Arbeitspapier
Inference on average treatment effects in aggregate panel data settings
This paper studies inference on treatment effects in aggregate panel data settings with a single treated unit and many control units. We propose new methods for making inference on average treatment effects in settings where both the number of pre-treatment and the number of post-treatment periods are large. We use linear models to approximate the counterfactual mean outcomes in the absence of the treatment. The counterfactuals are estimated using constrained Lasso, an essentially tuning free regression approach that nests difference-in-differences and synthetic control as special cases. We propose a K-fold cross-fitting procedure to remove the bias induced by regularization. To avoid the estimation of the long run variance, we construct a self-normalized t-statistic. The test statistic has an asymptotically pivotal distribution (a student t-distribution with K - 1 degrees of freedom), which makes our procedure very easy to implement. Our approach has several theoretical advantages. First, it does not rely on any sparsity assumptions. Second, it is fully robust against misspecification of the linear model. Third, it is more efficient than difference-in-means and difference-in-differences estimators. The proposed method demonstrates an excellent performance in simulation experiments, and is taken to a data application, where we re-evaluate the economic consequences of terrorism.
- Language
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Englisch
- Bibliographic citation
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Series: cemmap working paper ; No. CWP32/19
- Classification
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Wirtschaft
- Event
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Geistige Schöpfung
- (who)
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Chernozhukov, Victor
Wüthrich, Kaspar
Zhu, Yinchu
- Event
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Veröffentlichung
- (who)
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Centre for Microdata Methods and Practice (cemmap)
- (where)
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London
- (when)
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2019
- DOI
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doi:10.1920/wp.cem.2019.3219
- Handle
- Last update
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10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Chernozhukov, Victor
- Wüthrich, Kaspar
- Zhu, Yinchu
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2019
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Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Valid simultaneous inference in high-dimensional settings (with the HDM package for R)
Posterior inference in curved exponential families under increasing dimensions
Posterior inference in curved exponential families under increasing dimensions
Inference for extremal conditional quantile models, with an application to market and birthweight risks
Inference on counterfactual distributions
Inference on counterfactual distributions
Inference on counterfactual distributions
Intersection bounds: Estimation and inference
Intersection bounds: Estimation and inference
Intersection bounds: Estimation and inference
Inference on sets in finance
Simultaneous inference for best linear predictor of the conditional average treatment effect and other structural functions
Valid simultaneous inference in high-dimensional settings (with the HDM package for R)
Posterior inference in curved exponential families under increasing dimensions
Posterior inference in curved exponential families under increasing dimensions
Inference for extremal conditional quantile models, with an application to market and birthweight risks
Inference on counterfactual distributions
Inference on counterfactual distributions
Inference on counterfactual distributions
Intersection bounds: Estimation and inference
Intersection bounds: Estimation and inference
Intersection bounds: Estimation and inference