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Panel treatment effects measurement: Factor or linear projection modelling?

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  • Cheng Hsiao
  • Qiankun Zhou

Abstract

We discuss methods of measuring the treatment effects of a unit through the use of other units in panel data by either the factor‐based (FB) approach or the linear projection (LP) approach under different sample configurations of cross‐sectional dimension N and time series dimension T. We show that the LP approach in general yields smaller mean square prediction error than the FB approach when either both N and T are large or N fixed and T→∞ or T fixed and N large. The Monte Carlo simulation and empirical example are also conducted to consider their finite sample performances.

Suggested Citation

  • Cheng Hsiao & Qiankun Zhou, 2024. "Panel treatment effects measurement: Factor or linear projection modelling?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1332-1358, November.
    • Handle: RePEc:wly:japmet:v:39:y:2024:i:7:p:1332-1358
    DOI: 10.1002/jae.3081
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    1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    2. Xiao Ke & Cheng Hsiao, 2023. "Data Subject To Multiple Treatment Effects €” Disentangle The Impacts Of Global Pandemic And A Specific Disease Control Policy," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 68(05), pages 1507-1527, September.
    3. Jushan Bai & Kunpeng Li & Lina Lu, 2016. "Estimation and Inference of FAVAR Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 620-641, October.
    4. Bruno Ferman & Cristine Pinto & Vitor Possebom, 2020. "Cherry Picking with Synthetic Controls," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 39(2), pages 510-532, March.
    5. Jushan Bai & Serena Ng, 2021. "Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1746-1763, October.
    6. Cheng Hsiao & Zhentao Shi & Qiankun Zhou, 2022. "Transformed Estimation for Panel Interactive Effects Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1831-1848, October.
    7. Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2021. "Matrix Completion Methods for Causal Panel Data Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1716-1730, October.
    8. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    9. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    10. Xu, Yiqing, 2017. "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models," Political Analysis, Cambridge University Press, vol. 25(1), pages 57-76, January.
    11. Connor, Gregory & Korajczyk, Robert A., 1986. "Performance measurement with the arbitrage pricing theory : A new framework for analysis," Journal of Financial Economics, Elsevier, vol. 15(3), pages 373-394, March.
    12. Imbens, Guido W. & Lemieux, Thomas, 2008. "Regression discontinuity designs: A guide to practice," Journal of Econometrics, Elsevier, vol. 142(2), pages 615-635, February.
    13. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    14. Li, Xingyu & Shen, Yan & Zhou, Qiankun, 2024. "Confidence intervals of treatment effects in panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 240(1).
    15. Li, Kathleen T. & Bell, David R., 2017. "Estimation of average treatment effects with panel data: Asymptotic theory and implementation," Journal of Econometrics, Elsevier, vol. 197(1), pages 65-75.
    16. Jiang, Bin & Yang, Yanrong & Gao, Jiti & Hsiao, Cheng, 2021. "Recursive estimation in large panel data models: Theory and practice," Journal of Econometrics, Elsevier, vol. 224(2), pages 439-465.
    17. Wan, Shui-Ki & Xie, Yimeng & Hsiao, Cheng, 2018. "Panel data approach vs synthetic control method," Economics Letters, Elsevier, vol. 164(C), pages 121-123.
    18. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    19. Cheng Hsiao & Yimeng Xie & Qiankun Zhou, 2021. "Factor dimension determination for panel interactive effects models: an orthogonal projection approach," Computational Statistics, Springer, vol. 36(2), pages 1481-1497, June.
    20. Cheng Hsiao & Qiankun Zhou, 2019. "Panel parametric, semiparametric, and nonparametric construction of counterfactuals," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(4), pages 463-481, June.
    21. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    22. Alberto Abadie & Alexis Diamond & Jens Hainmueller, 2015. "Comparative Politics and the Synthetic Control Method," American Journal of Political Science, John Wiley & Sons, vol. 59(2), pages 495-510, February.
    23. Cheng Hsiao & H. Steve Ching & Shui Ki Wan, 2012. "A Panel Data Approach For Program Evaluation: Measuring The Benefits Of Political And Economic Integration Of Hong Kong With Mainland China," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 705-740, August.
    24. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    25. Bai, Jushan & Ng, Serena, 2013. "Principal components estimation and identification of static factors," Journal of Econometrics, Elsevier, vol. 176(1), pages 18-29.
    26. Vinod, Hrishikesh D, 1978. "A Survey of Ridge Regression and Related Techniques for Improvements over Ordinary Least Squares," The Review of Economics and Statistics, MIT Press, vol. 60(1), pages 121-131, February.
    27. Jiafeng Chen, 2023. "Synthetic Control as Online Linear Regression," Econometrica, Econometric Society, vol. 91(2), pages 465-491, March.
    28. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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