arXiv 4 Apr 2018 · Econometrics · 8 citations (OpenAlex)
arXiv:1804.01208 · PDF · DOI · OpenAlex · Extracted main text
The common practice in difference-in-difference (DiD) designs is to check for parallel trends prior to treatment assignment, yet typical estimation and inference does not account for the fact that this test has occurred. I analyze the properties of the traditional DiD estimator conditional on having passed (i.e. not rejected) the test for parallel pre-trends. When the DiD design is valid and the test for pre-trends confirms it, the typical DiD estimator is unbiased, but traditional standard errors are overly conservative. Additionally, there exists an alternative unbiased estimator that is more efficient than the traditional DiD estimator under parallel trends. However, when in population there is a non-zero pre-trend but we fail to reject the hypothesis of parallel pre-trends, the DiD estimator is generally biased relative to the population DiD coefficient. Moreover, if the trend is monotone, then under reasonable assumptions the bias from conditioning exacerbates the bias relative to the true treatment effect. I propose new estimation and inference procedures that account for the test for parallel trends, and compare their performance to that of the traditional estimator in a Monte Carlo simulation.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Andrews, I. and Kasy, M (2017) Identification of and Correction for Publication Bias | 0.843 | 4 | 3 | 75% |
| 2 | Lee, J. D., Sun, D. L., Sun, Y., and Taylor, J. E (2016) Exact post-selection inference, with application to the lasso | 0.843 | 5 | 3 | 60% |
| 3 | Borusyak, K. and Jaravel, X (2016) Revisiting Event Study Designs | 0.511 | 2 | 2 | 50% |
| 4 | Camerer, C. F., Dreber, A., Forsell, E., Ho, T.-H., Huber, J., Johan… (2016) Evaluating replicability of laboratory experiments in economics | 0.405 | 1 | 1 | 100% |
| 5 | Abadie, A (2005) Semiparametric Difference-in-Differences Estimators | 0.405 | 1 | 1 | 100% |
| 6 | Athey, S. and Imbens, G. W (2006) Identification and Inference in Nonlinear Difference-in-Differences Models | 0.405 | 1 | 1 | 100% |
| 7 | Bertrand, M., Duflo, E., and Mullainathan, S (2004) How Much Should We Trust Differences-In-Differences Estimates? | 0.405 | 1 | 1 | 100% |
| 8 | Brodeur, A., Lé, M., Sangnier, M., and Zylberberg, Y (2016) Star Wars: The Empirics Strike Back | 0.405 | 1 | 1 | 100% |
| 9 | Christensen, G. S. and Miguel, E (2016) Transparency, Reproducibility, and the Credibility of Economics Research | 0.405 | 1 | 1 | 100% |
| 10 | Donald, S. G. and Lang, K (2007) Inference with Difference-in-Differences and Other Panel Data | 0.405 | 1 | 1 | 100% |
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