Brantly Callaway, Sonia Karami
arXiv 29 Jun 2020 · Econometrics · publishedJournal of Econometrics (2022) · 31 citations (OpenAlex)
arXiv:2006.15780 · PDF · DOI · OpenAlex · Extracted main text
This paper considers identifying and estimating the Average Treatment Effect on the Treated (ATT) when untreated potential outcomes are generated by an interactive fixed effects model. That is, in addition to time-period and individual fixed effects, we consider the case where there is an unobserved time invariant variable whose effect on untreated potential outcomes may change over time and which can therefore cause outcomes (in the absence of participating in the treatment) to follow different paths for the treated group relative to the untreated group. The models that we consider in this paper generalize many commonly used models in the treatment effects literature including difference in differences and individual-specific linear trend models. Unlike the majority of the literature on interactive fixed effects models, we do not require the number of time periods to go to infinity to consistently estimate the ATT. Our main identification result relies on having the effect of some time invariant covariate (e.g., race or sex) not vary over time. Using our approach, we show that the ATT can be identified with as few as three time periods and with panel or repeated cross sections data.
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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 | Callaway, Brantly, Sant'Anna, Pedro HC (2021) Difference-in-differences with multiple time periods self | 1.000 | 6 | 3 | 100% |
| 2 | Ahn, Seung C, Lee, Young H, Schmidt, Peter (2013) Panel data models with multiple time-varying individual effects | 0.928 | 4 | 3 | 100% |
| 3 | Abadie, Alberto (2005) Semiparametric difference-in-differences estimators | 0.737 | 3 | 2 | 100% |
| 4 | Arkhangelsky, Dmitry, Athey, Susan, Hirshberg, David A, Imbens, Guid… (2021) Synthetic difference-in-differences | 0.644 | 2 | 2 | 100% |
| 5 | Gardner, John (2020) Identification and estimation of average causal effects when treatment status is ignorable within unobserved strata | 0.644 | 2 | 2 | 100% |
| 6 | Gobillon, Laurent, Magnac, Thierry (2016) Regional policy evaluation: Interactive fixed effects and synthetic controls | 0.644 | 2 | 2 | 100% |
| 7 | Goodman-Bacon, Andrew (2021) Difference-in-differences with variation in treatment timing | 0.644 | 2 | 2 | 100% |
| 8 | Heckman, James, Hotz, V Joseph (1989) Choosing among alternative nonexperimental methods for estimating the impact of social programs: The case of manpower training | 0.644 | 2 | 2 | 100% |
| 9 | Holtz-Eakin, Douglas, Newey, Whitney, Rosen, Harvey S (1988) Estimating vector autoregressions with panel data | 0.644 | 2 | 2 | 100% |
| 10 | Juodis, Vasilis (2020) A linear estimator for factor-augmented fixed-T panels with endogenous regressors | 0.644 | 2 | 2 | 100% |
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