Koki Fusejima, Takuya Ishihara
arXiv 27 Mar 2025 · Econometrics
arXiv:2503.21763 · PDF · DOI · OpenAlex · Extracted main text
This paper provides a new approach for identifying and estimating the Average Treatment Effect on the Treated under a linear factor model that allows for multiple time-varying unobservables. Unlike the majority of the literature on treatment effects in linear factor models, our approach does not require the number of pre-treatment periods to go to infinity to obtain a valid estimator. Our identification approach employs a certain nonlinear transformations of the time invariant observed covariates that are sufficiently correlated with the unobserved variables. This relevance condition can be checked with the available data on pre-treatment periods by validating the correlation of the transformed covariates and the pre-treatment outcomes. Based on our identification approach, we provide an asymptotically unbiased estimator of the effect of participating in the treatment when there is only one treated unit and the number of control units is large.
appendix boundary found by appendix_command · 54% 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 | Imbens, G., N. Kallus, and X. Mao (2021) Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Models | 1.000 | 6 | 4 | 100% |
| 2 | Brown, N. and K. Butts (2023) Dynamic Treatment Effect Estimation with Interactive Fixed Effects and Short Panels, Tech | 0.874 | 7 | 2 | 100% |
| 3 | Callaway, B. and S. Karami (2022) Treatment effects in interactive fixed effects models with a small number of time periods | 0.811 | 4 | 2 | 100% |
| 4 | Ahn, S. C., Y. H. Lee, and P. Schmidt (2013) Panel data models with multiple time-varying individual effects | 0.585 | 3 | 1 | 100% |
| 5 | Ben-Israel, A. and T. N. Greville (2003) Generalized inverses: theory and applications | 0.511 | 2 | 1 | 100% |
| 6 | Abadie, A. and J. Gardeazabal (2003) The economic costs of conflict: A case study of the Basque Country | 0.405 | 1 | 1 | 100% |
| 7 | Abadie, A., A. Diamond, and J. Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program | 0.405 | 1 | 1 | 100% |
| 8 | Abadie, A., A. Diamond, and J. Hainmueller (2015) Comparative politics and the synthetic control method | 0.405 | 1 | 1 | 100% |
| 9 | Abadie, A (2021) Using synthetic controls: Feasibility, data requirements, and methodological aspects | 0.405 | 1 | 1 | 100% |
| 10 | Callaway, B (2023) Difference-in-differences for policy evaluation | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 16 scored citations.