arXiv 6 Sep 2024 · Econometrics · publishedJournal of Business and Economic Statistics (2025)
arXiv:2409.03979 · PDF · DOI · OpenAlex · Extracted main text
We introduce a novel method for estimating and conducting inference about extreme quantile treatment effects (QTEs) in the presence of endogeneity. Our approach is applicable to a broad range of empirical research designs, including instrumental variables design and regression discontinuity design, among others. By leveraging regular variation and subsampling, the method ensures robust performance even in extreme tails, where data may be sparse or entirely absent. Simulation studies confirm the theoretical robustness of our approach. Applying our method to assess the impact of job training provided by the Job Training Partnership Act (JTPA), we find significantly negative QTEs for the lowest quantiles (i.e., the most disadvantaged individuals), contrasting with previous literature that emphasizes positive QTEs for intermediate quantiles.
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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 | Frandsen, B. R., M. Frölich, and B. Melly (2012) Quantile treatment effects in the regression discontinuity design | 0.811 | 4 | 2 | 100% |
| 2 | de Haan, L. and A. Ferreira (2006) Extreme Value Theory: An Introduction | 0.737 | 3 | 3 | 67% |
| 3 | Sasaki, Y. and Y. Wang (2024) Extreme Changes in Changes self | 0.737 | 3 | 2 | 100% |
| 4 | Abadie, A., J. Angrist, and G. Imbens (2002) Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings | 0.644 | 2 | 2 | 100% |
| 5 | Hill, B. M (1975) A simple general approach to inference about the tail of a distribution | 0.511 | 2 | 1 | 100% |
| 6 | Zhang, Y (2018) Extremal Quantile Treatment Effects | 0.511 | 2 | 1 | 100% |
| 7 | Heckman, J., H. Ichimura, J. Smith, and P. Todd (1998) Characterizing Selection Bias Using Experimental Data | 0.405 | 1 | 1 | 100% |
| 8 | Hill, J. B (2010) On Tail Index Estimation for Dependent, Heterogeneous Data | 0.405 | 1 | 1 | 100% |
| 9 | Hill, J. B (2015) Tail Index Estimation for a Filtered Dependent Time Series | 0.405 | 1 | 1 | 100% |
| 10 | Abadie, A (2003) Semiparametric instrumental variable estimation of treatment response models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 20 scored citations.