arXiv 18 Sep 2021 · Statistics — Applications · publishedJournal of Business and Economic Statistics (2024) · 1 citations (OpenAlex)
arXiv:2109.08793 · PDF · DOI · OpenAlex · Extracted main text
The conditional tail average treatment effect (CTATE) is defined as a difference between the conditional tail expectations of potential outcomes, which can capture heterogeneity and deliver aggregated local information on treatment effects over different quantile levels and is closely related to the notion of second-order stochastic dominance and the Lorenz curve. These properties render it a valuable tool for policy evaluation. In this paper, we study estimation of the CTATE locally for a group of compliers (local CTATE or LCTATE) under the two-sided noncompliance framework. We consider a semiparametric treatment effect framework under endogeneity for the LCTATE estimation using a newly introduced class of consistent loss functions jointly for the conditional tail expectation and quantile. We establish the asymptotic theory of our proposed LCTATE estimator and provide an efficient algorithm for its implementation. We then apply the method to evaluate the effects of participating in programs under the Job Training Partnership Act in the US.
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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 | Abadie, A (2003) Semiparametric instrumental variable estimation of treatment response models | 0.874 | 6 | 2 | 100% |
| 2 | Fissler, T. and J. F. Ziegel (2016) Higher order elicitability and Osband’s principle | 0.843 | 15 | 4 | 60% |
| 3 | Patton, A. J., J. F. Ziegel, and R. Chen (2019) Dynamic semiparametric models for expected shortfall (and Value-at-Risk) | 0.754 | 7 | 3 | 43% |
| 4 | Abadie, A., J. Angrist, and G. Imbens (2002) Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings | 0.751 | 33 | 5 | 42% |
| 5 | Chernozhukov, V., I. Fernández-Val, and B. Melly (2013) Inference on Counterfactual Distributions | 0.737 | 4 | 3 | 50% |
| 6 | Imbens, G. W. and D. B. Rubin (2015) Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction | 0.737 | 3 | 2 | 100% |
| 7 | Newey, W. K (1997) Convergence rates and asymptotic normality for series estimators | 0.644 | 4 | 2 | 50% |
| 8 | Chernozhukov, V. and C. Hansen (2008) Instrumental variable quantile regression: A robust inference approach | 0.644 | 3 | 2 | 67% |
| 9 | Angrist, J. D., G. W. Imbens, and D. B. Rubin (1996) Identification of Causal Effects Using Instrumental Variables | 0.644 | 2 | 2 | 100% |
| 10 | Imbens, G. and J. Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 48 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Self-Normalized Inference in (Quantile, Expected Shortfall) Regressions for Time Series | 0.405 | 1 | 1 |
| 2 | Rectified Linear Unit Regression | 0.405 | 1 | 1 |