Data and code for: Pre-test with Caution: Event-study Estimates After Testing for Parallel Trends
Principal Investigator(s): View help for Principal Investigator(s) Jonathan Roth, Brown University
Version: View help for Version V1
| Name | File Type | Size | Last Modified |
|---|---|---|---|
| EventStudyInference_Replication_Archive | 03/15/2022 09:59:AM |
Project Citation:
Roth, Jonathan. Data and code for: Pre-test with Caution: Event-study Estimates After Testing for Parallel Trends. Nashville, TN: American Economic Association [publisher], 2022. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2022-08-26. https://doi.org/10.3886/E151982V1
Project Description
Summary:
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Replication code and data for ''Pre-test with Caution: Event-study Estimates After Testing for Parallel Trends'' by Jonathan Roth, published in AER: Insights.
The abstract of the paper is as follows:
This paper discusses two important limitations of the common practice of testing for pre-existing differences in trends (''pre-trends'') when using difference-in-differences and related methods. First, conventional pre-trends tests may have low power. Second, conditioning the analysis on the result of a pre-test can distort estimation and inference, potentially exacerbating the bias of point estimates and undercoverage of confidence intervals. I analyze these issues both in theory and in simulations calibrated to a survey of recent papers in leading economics journals, which suggest that these limitations are important in practice. I conclude with practical recommendations for mitigating these issues.
The abstract of the paper is as follows:
This paper discusses two important limitations of the common practice of testing for pre-existing differences in trends (''pre-trends'') when using difference-in-differences and related methods. First, conventional pre-trends tests may have low power. Second, conditioning the analysis on the result of a pre-test can distort estimation and inference, potentially exacerbating the bias of point estimates and undercoverage of confidence intervals. I analyze these issues both in theory and in simulations calibrated to a survey of recent papers in leading economics journals, which suggest that these limitations are important in practice. I conclude with practical recommendations for mitigating these issues.
Funding Sources:
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NSF Graduate Research Fellowship (DGE1144152)
Scope of Project
JEL Classification:
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C10 Econometric and Statistical Methods and Methodology: General
C10 Econometric and Statistical Methods and Methodology: General
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