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Research Article

Estimation of the Local Conditional Tail Average Treatment Effect

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References

  • Abadie, A. (2003), “Semiparametric Instrumental Variable Estimation of Treatment Response Models,” Journal of Econometrics, 113, 231–263. DOI: 10.1016/S0304-4076(02)00201-4.
  • Abadie, A., Angrist, J., and Imbens, G. (2002), “Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings,” Econometrica, 70, 91–117. DOI: 10.1111/1468-0262.00270.
  • Angrist, J. D., Imbens, G. W., and Rubin, D. B. (1996), “Identification of Causal Effects Using Instrumental Variables,” Journal of the American Statistical Association, 91, 444–455. DOI: 10.1080/01621459.1996.10476902.
  • Belloni, A., Chernozhukov, V., Fernández-Val, I., and Hansen, C. (2017), “Program Evaluation and Causal Inference With High-Dimensional Data,” Econometrica, 85, 233–298. DOI: 10.3982/ECTA12723.
  • Chen, Y.-T., Hsu, Y.-C., and Wang, H.-J. (2020), “A Stochastic Frontier Model with Endogenous Treatment Status and Mediator,” Journal of Business & Economic Statistics, 38, 243–256. DOI: 10.1080/07350015.2018.1497504.
  • Chernozhukov, V. (2005), “Extremal Quantile Regression,” The Annals of Statistics, 33, 806–839. DOI: 10.1214/009053604000001165.
  • Chernozhukov, V., and Fernández-Val, I. (2011), “Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks,” The Review of Economic Studies, 78, 559–589. DOI: 10.1093/restud/rdq020.
  • Chernozhukov, V., Fernández-Val, I., and Melly, B. (2013), “Inference on Counterfactual Distributions,” Econometrica, 81, 2205–2268. DOI: 10.3982/ECTA10582.
  • Chernozhukov, V., and Hansen, C. (2004), “The Effects of 401(K) Participation on the Wealth Distribution: An Instrumental Quantile Regression Analysis,” The Review of Economics and Statistics, 86, 735–751. DOI: 10.1162/0034653041811734.
  • ———(2005), “An IV Model of Quantile Treatment Effects,” Econometrica, 73, 245–261. DOI: 10.1111/j.1468-0262.2005.00570.x.
  • ———(2006), “Instrumental Quantile Regression Inference for Structural and Treatment Effect Models,” Journal of Econometrics, 132, 491–525. DOI: 10.1016/j.jeconom.2005.02.009.
  • ———(2008), “Instrumental Variable Quantile Regression: A Robust Inference Approach,” Journal of Econometrics, 142, 379–398. DOI: 10.1016/j.jeconom.2007.06.005.
  • Chernozhukov, V., Hansen, C., and Jansson, M. (2007), “Inference Approaches for Instrumental Variable Quantile Regression,” Economics Letters, 95, 272–277. DOI: 10.1016/j.econlet.2006.10.016.
  • ———(2009), “Finite Sample Inference for Quantile Regression Models,” Journal of Econometrics, 152, 93–103. DOI: 10.1016/j.jeconom.2009.01.004.
  • Chou, R. Y., Yen, T.-J., and Yen, Y.-M. (2022), “Forecasting Expected Shortfall and Value-at-Risk with the FZ Loss and Realized Variance Measures,” Taiwan Economic Forecast and Policy, 52, 89–140.
  • Dimitriadis, T., and Bayer, S. (2019), “A Joint Quantile and Expected Shortfall Regression Framework,” Electronic Journal of Statistics, 13, 1823–1871. DOI: 10.1214/19-EJS1560.
  • Donald, S. G., Hsu, Y.-C., and Lieli, R. P. (2014), “Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT,” Journal of Business & Economic Statistics, 32, 395–415. DOI: 10.1080/07350015.2014.888290.
  • Fissler, T., and Ziegel, J. F. (2016), “Higher Order Elicitability and Osband’s Principle,” The Annals of Statistics, 44, 1680–1707. DOI: 10.1214/16-AOS1439.
  • Fricke, H., Frölich, M., Huber, M., and Lechner, M. (2020), “Endogeneity and Non-Response Bias in Treatment Evaluation–Nonparametric Identification of Causal Effects by Instruments,” Journal of Applied Econometrics, 35, 481–504. DOI: 10.1002/jae.2764.
  • Frölich, M., and Huber, M. (2017), “Direct and Indirect Treatment Effects–Causal Chains and Mediation Analysis with Instrumental Variables,” Journal of the Royal Statistical Society, Series B, 79, 1645–1666. DOI: 10.1111/rssb.12232.
  • Frölich, M., and B. Melly (2013), “Unconditional Quantile Treatment Effects Under Endogeneity,” Journal of Business and Economic Statistics, 31, 346–357. DOI: 10.1080/07350015.2013.803869.
  • Hahn, J., and Liao, Z. (2021), “Bootstrap Standard Error Estimates and Inference,” Econometrica, 89, 1963–1977. DOI: 10.3982/ECTA17912.
  • Heckman, J. J., Smith, J., and Clements, N. (1997), “Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts,” The Review of Economic Studies, 64, 487–535. DOI: 10.2307/2971729.
  • Hill, J. B. (2015), “Expected Shortfall Estimation and Gaussian Inference for Infinite Variance Time Series,” Journal of Financial Econometrics, 13, 1–44. DOI: 10.1093/jjfinec/nbt020.
  • Hsu, Y.-C., Lai, T.-C., and Lieli, R. P. (2022), “Estimation and Inference for Distribution and Quantile Functions in Endogenous Treatment Effect Models,” Econometric Reviews, 41, 22–50. DOI: 10.1080/07474938.2020.1847479.
  • Imbens, G., and Angrist, J. (1994), “Identification and Estimation of Local Average Treatment Effects,” Econometrica, 62, 467–75. DOI: 10.2307/2951620.
  • Imbens, G. W., and Rubin, D. B. (2015), Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction, Cambridge: Cambridge University Press.
  • Koenker, R., and Bassett, G. (1978), “Regression Quantiles,” Econometrica, 46, 33–50. DOI: 10.2307/1913643.
  • Lei, L., Sahoo, R., and Wager, S. (2023), “Policy Learning under Biased Sample Selection,” arXiv working paper, 2304.11735.
  • Levy, H. (2016), Stochastic Dominance-Investment Decision Making under Uncertainty (3rd ed.), Cham: Springer.
  • Linton, O., and Xiao, Z. (2013), “Estimation of and Inference About the Expected Shortfall for Time Series with Infinite Variance,” Econometric Theory, 29, 771–807. DOI: 10.1017/S0266466612000692.
  • Luo, Y., and Wan, Y. (2018), “Integrated-Quantile-Based Estimation for First-Price Auction Models,” Journal of Business & Economic Statistics, 36, 173–180. DOI: 10.1080/07350015.2016.1166119.
  • Melly, B., and Wüthrich, K. (2017), “Local Quantile Treatment Effects,” in Handbook of Quantile Regression, eds. R. Koenker, V. Chernozhukov, X. He, and L. Peng, pp. 145–164, New York: Chapman and Hall/CRC.
  • Meng, X., and Taylor, J. W. (2020), “Estimating Value-at-Risk and Expected Shortfall Using the Intraday Low and Range Data,” European Journal of Operational Research, 280, 191–202. DOI: 10.1016/j.ejor.2019.07.011.
  • Newey, W. K. (1991), “Uniform Convergence in Probability and Stochastic Equicontinuity,” Econometrica, 59, 1161–1167. DOI: 10.2307/2938179.
  • ———(1997), “Convergence Rates and Asymptotic Normality for Series Estimators,” Journal of Econometrics, 79, 147–168. DOI: 10.1016/S0304-4076(97)00011-0.
  • Newey, W. K., and McFadden, D. (1994), “Large Sample Estimation and Hypothesis Testing,” in Handbook of Econometrics (Vol. 4), eds. R. F. Engle and D, L. McFadden, pp. 2111–2245, Amsterdam: Elsevier.
  • Patton, A. J., Ziegel, J. F., and Chen, R. (2019), “Dynamic Semiparametric Models for Expected Shortfall (and Value-at-Risk),” Journal of Econometrics, 211, 388–413. DOI: 10.1016/j.jeconom.2018.10.008.
  • Taylor, J. W. (2019), “Forecasting Value at Risk and Expected Shortfall Using a Semiparametric Approach Based on the Asymmetric Laplace Distribution,” Journal of Business & Economic Statistics, 37, 121–133. DOI: 10.1080/07350015.2017.1281815.
  • Van der Vaart, A. W. (1998), Asymptotic Statistics, Cambridge: Cambridge University Press.
  • Wei, B., Peng, L., Zhang, M.-J., and Fine, J. P. (2021), “Estimation of Causal Quantile Effects with a Binary Instrumental Variable and Censored Data,” Journal of the Royal Statistical Society, Series B, 83, 559–578. DOI: 10.1111/rssb.12431.
  • Wei, B., Tan, K. M., and He, X. (2024), “Estimation of Complier Expected Shortfall Treatment Effects with a Binary Instrumental Variable,” Journal of Econometrics, 238, 105572. DOI: 10.1016/j.jeconom.2023.105572.
  • Wüthrich, K. (2020), “A Comparison of Two Quantile Models With Endogeneity,” Journal of Business & Economic Statistics, 38, 443–456. DOI: 10.1080/07350015.2018.1514307.

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