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Multiple Treatment Effects in Panel-Heterogeneity and Aggregation

In: Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology

Author

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  • Cheng Hsiao
  • Yan Shen
  • Qiankun Zhou

Abstract

Panel data provide the possibilities of estimating individual treatment effects for multiple individuals. Two issues are considered: (1) differences in the estimated individual treatment effects are due to heterogeneity or a chance mechanism? (2) what is the best way to estimate the average treatment effects? Testing and aggregation methods are suggested. Monte Carlo simulations are also conducted to shed light on these two issues. An empirical analysis on the involvement of underground organization in China’s Peer-to-Peer (P2P) activities through the “anti-gang” campaign is also provided.

Suggested Citation

  • Cheng Hsiao & Yan Shen & Qiankun Zhou, 2022. "Multiple Treatment Effects in Panel-Heterogeneity and Aggregation," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology, volume 43, pages 81-101, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-90532021000043b005
    DOI: 10.1108/S0731-90532021000043B005
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    More about this item

    Keywords

    Treatment effects; panel data analysis; interactive effects; aggregation; eigenvalue; eigenvector; C01; C21; C31; I18;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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