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Estimating Quantile Treatment Effects for Panel Data

Author

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  • Zongwu Cai

    (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)

  • Ying Fang

    (The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China and Department of Statistics & Data Science, School of Economics, Xiamen University, Xiamen, Fujian 361005, China)

  • Ming Lin

    (The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China and Department of Statistics and Data Science, School of Economics, Xiamen University, Xiamen, Fujian 361005, China)

  • Mingfeng Zhan

    (The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China)

Abstract

Motivated by the paper by Hsiao, Ching and Wan (2012), which proposed a factor-based model to estimate the average treatment effect with panel data, this paper proposes a quantile treatment effect model for panel data to characterize the distributional effect of a treatment. We propose to estimate the counterfactual quantile for the treated unit by using the relationship between conditional and unconditional distributions. Also, the asymptotic properties for the proposed quantile treatment effect estimator are established, together with discussing the choice of control units and covariates. A simulation study is conducted to illustrate our method. Finally, the proposed method is applied to estimate the quantile treatment effects of introducing CSI 300 index futures trading on both the log-return and volatility of the stock market in China.

Suggested Citation

  • Zongwu Cai & Ying Fang & Ming Lin & Mingfeng Zhan, 2022. "Estimating Quantile Treatment Effects for Panel Data," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202205, University of Kansas, Department of Economics, revised Feb 2022.
  • Handle: RePEc:kan:wpaper:202205
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    More about this item

    Keywords

    LASSO method; Panel data; Nonparametric estimation; Quantile regression; Treatment effects.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling

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