arXiv 15 Feb 2024 · Econometrics · 9 citations (OpenAlex)
arXiv:2402.09928 · PDF · DOI · OpenAlex · Extracted main text
The conventional Two-Way Fixed-Effects (TWFE) estimator has come under scrutiny lately. Recent literature has revealed potential shortcomings of TWFE when the treatment effects are heterogeneous. Scholars have developed new advanced dynamic Difference-in-Differences (DiD) estimators to tackle these potential shortcomings. However, confusion remains in applied research as to when the conventional TWFE is biased and what issues the novel estimators can and cannot address. In this study, we first provide an intuitive explanation of the problems of TWFE and elucidate the key features of the novel alternative DiD estimators. We then systematically demonstrate the conditions under which the conventional TWFE is inconsistent. We employ Monte Carlo simulations to assess the performance of dynamic DiD estimators under violations of key assumptions, which likely happens in applied cases. While the new dynamic DiD estimators offer notable advantages in capturing heterogeneous treatment effects, we show that the conventional TWFE performs generally well if the model specifies an event-time function. All estimators are equally sensitive to violations of the parallel trends assumption, anticipation effects or violations of time-varying exogeneity. Despite their advantages, the new dynamic DiD estimators tackle a very specific problem and they do not serve as a universal remedy for violations of the most critical assumptions. We finally derive, based on our simulations, recommendations for how and when to use TWFE and the new DiD estimators in applied research.
appendix boundary found by appendix_command · 92% of the source is main text. Read the extracted text to check this.
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 | Callaway, B. and Sant'Anna, P. H (2021) Difference-in-Differences with multiple time periods | 1.000 | 14 | 5 | 100% |
| 2 | Wooldridge, J. M (2021) Two-Way Fixed Effects, the Two-Way Mundlak Regression, and Difference-in-Differences Estimators | 1.000 | 12 | 5 | 100% |
| 3 | Borusyak, K., Jaravel, X., and Spiess, J (2024) Revisiting Event-Study Designs: Robust and Efficient Estimation | 1.000 | 9 | 5 | 100% |
| 4 | Athey, S., Bayati, M., Doudchenko, N., Imbens, G., and Khosravi, K (2021) Matrix completion methods for causal panel data models | 1.000 | 9 | 3 | 100% |
| 5 | Sun, L. and Abraham, S (2021) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 1.000 | 7 | 3 | 100% |
| 6 | Roth, J., Sant'Anna, P. H., Bilinski, A., and Poe, J (2023) What's trending in difference-in-differences? A synthesis of the recent econometrics literature | 1.000 | 5 | 4 | 100% |
| 7 | Goodman-Bacon, A (2021) Difference-in-differences with variation in treatment timing | 0.965 | 10 | 5 | 90% |
| 8 | Chiu, A., Lan, X., Liu, Z., and Xu, Y (2023) What To Do (and Not to Do) with Causal Panel Analysis under Parallel Trends: Lessons from A Large Reanalysis Study | 0.928 | 4 | 4 | 100% |
| 9 | De Chaisemartin, C. and D'Haultfuille, X (2020) Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects | 0.843 | 3 | 3 | 100% |
| 10 | Wooldridge, J. M (2010) Econometric Analysis of Cross Section and Panel Data | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 37 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Refining the Notion of No Anticipation in Difference-in-Differences Studies | 0.405 | 1 | 1 |