Yihong Xu, Li Zheng
arXiv 1 Apr 2025 · Statistics — Methodology
arXiv:2504.00785 · PDF · DOI · OpenAlex · Extracted main text
We introduce novel estimators for quantile causal effects with high dimensional panel data (large $N$ and $T$), where only one or a few units are affected by the intervention or policy. Our method extends the generalized synthetic control method \citep{xu_2017} from average treatment effects on the treated to quantile treatment effects on the treated, allowing the underlying factor structure to change across the quantile of the interested outcome distribution. Our method involves estimating the quantile-dependent factors using the control group, followed by a quantile regression to estimate the quantile treatment effect using the treated units. We establish the asymptotic properties of our estimators and propose a bootstrap procedure for statistical inference, supported by simulation studies. An empirical application of the 2008 China Stimulus Program is provided.
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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 | Y. Xu (2016) Generalized synthetic control method: Causal inference with interactive fixed effects models | 1.000 | 8 | 5 | 100% |
| 2 | L. Chen, J. J. Dolado, and J. Gonzalo (2021) Quantile factor models | 0.941 | 24 | 7 | 83% |
| 3 | T. Ando and J. Bai (2020) Quantile co-movement in financial markets: A panel quantile model with unobserved heterogeneity | 0.928 | 5 | 3 | 80% |
| 4 | Z. Cai, Y. Fang, M. Lin, and M. Zhan (2022) Estimating quantile treatment effects for panel data | 0.928 | 4 | 4 | 100% |
| 5 | J. Bai (2003) Inferential theory for factor models of large dimensions | 0.928 | 4 | 3 | 100% |
| 6 | A. Abadie, A. Diamond, and J. Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of california’s tobacco control program | 0.874 | 6 | 4 | 67% |
| 7 | A. Abadie (2021) Using synthetic controls: Feasibility, data requirements, and methodological aspects | 0.843 | 3 | 3 | 100% |
| 8 | C. Hsiao, H. Steve Ching, and S. Ki Wan (2012) A panel data approach for program evaluation: measuring the benefits of political and economic integration of hong kong with mai… | 0.843 | 3 | 3 | 100% |
| 9 | M. Ouyang and Y. Peng (2015) The treatment-effect estimation: A case study of the 2008 economic stimulus package of china | 0.811 | 4 | 2 | 100% |
| 10 | K. B. Gregory, S. N. Lahiri, and D. J. Nordman (2018) A smooth block bootstrap for quantile regression with time series | 0.737 | 5 | 3 | 40% |
Showing the top 10 of 41 scored citations.