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Estimating conditional average treatment effects with heteroscedasticity by model averaging and matching

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  • Shi, Pengfei
  • Zhang, Xinyu
  • Zhong, Wei

Abstract

We propose a model averaging approach, combined with a partition and matching method to estimate the conditional average treatment effects under heteroskedastic error settings. The proposed approach has asymptotic optimality and consistency of weights and estimator. Numerical studies show that our method has good finite-sample performances.

Suggested Citation

  • Shi, Pengfei & Zhang, Xinyu & Zhong, Wei, 2024. "Estimating conditional average treatment effects with heteroscedasticity by model averaging and matching," Economics Letters, Elsevier, vol. 238(C).
    • Handle: RePEc:eee:ecolet:v:238:y:2024:i:c:s0165176524001629
    DOI: 10.1016/j.econlet.2024.111679
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    References listed on IDEAS

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    1. Kitagawa, Toru & Muris, Chris, 2016. "Model averaging in semiparametric estimation of treatment effects," Journal of Econometrics, Elsevier, vol. 193(1), pages 271-289.
    2. Lu, Xun & Su, Liangjun, 2015. "Jackknife model averaging for quantile regressions," Journal of Econometrics, Elsevier, vol. 188(1), pages 40-58.
    3. Zhang, Xinyu & Wan, Alan T.K. & Zou, Guohua, 2013. "Model averaging by jackknife criterion in models with dependent data," Journal of Econometrics, Elsevier, vol. 174(2), pages 82-94.
    4. Qingliang Fan & Yu-Chin Hsu & Robert P. Lieli & Yichong Zhang, 2022. "Estimation of Conditional Average Treatment Effects With High-Dimensional Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 313-327, January.
    5. Rolling, Craig A. & Yang, Yuhong & Velez, Dagmar, 2019. "Combining Estimates Of Conditional Treatment Effects," Econometric Theory, Cambridge University Press, vol. 35(6), pages 1089-1110, December.
    6. Xie, Tian, 2015. "Prediction model averaging estimator," Economics Letters, Elsevier, vol. 131(C), pages 5-8.
    7. Wan, Alan T.K. & Zhang, Xinyu & Zou, Guohua, 2010. "Least squares model averaging by Mallows criterion," Journal of Econometrics, Elsevier, vol. 156(2), pages 277-283, June.
    8. Craig A. Rolling & Yuhong Yang, 2014. "Model selection for estimating treatment effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(4), pages 749-769, September.
    9. Feng, Yang & Liu, Qingfeng & Okui, Ryo, 2020. "On the sparsity of Mallows model averaging estimator," Economics Letters, Elsevier, vol. 187(C).
    10. Liu, Chu-An, 2018. "Averaging estimators for kernel regressions," Economics Letters, Elsevier, vol. 171(C), pages 102-105.
    11. Bruce E. Hansen, 2007. "Least Squares Model Averaging," Econometrica, Econometric Society, vol. 75(4), pages 1175-1189, July.
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