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CCE estimation of factor‐augmented regression models with more factors than observables

Author

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  • Hande Karabiyik
  • Jean‐Pierre Urbain
  • Joakim Westerlund

Abstract

This paper considers estimation of factor‐augmented panel data regression models. One of the most popular approaches towards this end is the common correlated effects (CCE) estimator of Pesaran (Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 2006, 74, 967–1012, 2006). For the pooled version of this estimator to be consistent, either the number of observables must be larger than the number of unobserved common factors, or the factor loadings must be distributed independently of each other. This is a problem in the typical application involving only a small number of regressors and/or correlated loadings. The current paper proposes a simple extension to the CCE procedure by which both requirements can be relaxed. The CCE approach is based on taking the cross‐section average of the observables as an estimator of the common factors. The idea put forth in the current paper is to consider not only the average but also other cross‐section combinations. Asymptotic properties of the resulting combination‐augmented CCE (C3E) estimator are provided and tested in small samples using both simulated and real data.

Suggested Citation

  • Hande Karabiyik & Jean‐Pierre Urbain & Joakim Westerlund, 2019. "CCE estimation of factor‐augmented regression models with more factors than observables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 268-284, March.
    • Handle: RePEc:wly:japmet:v:34:y:2019:i:2:p:268-284
    DOI: 10.1002/jae.2661
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    Cited by:

    1. Jörg Breitung & Philipp Hansen, 2021. "Correction to: Alternative estimation approaches for the factor augmented panel data model with small T," Empirical Economics, Springer, vol. 61(6), pages 3557-3558, December.
    2. Ovidijus Stauskas & Ignace De Vos, 2025. "Handling Distinct Correlated Effects with CCE," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 87(2), pages 448-475, April.
    3. Artūras Juodis, 2022. "A regularization approach to common correlated effects estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 788-810, June.
    4. Tingting Cheng & Jiachen Cong & Fei Liu & Xuanbin Yang, 2025. "Binary Response Forecasting under a Factor-Augmented Framework," Papers 2507.16462, arXiv.org.
    5. Luca Margaritella & Joakim Westerlund, 2023. "Using information criteria to select averages in CCE," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 405-421.
    6. Ignace De Vos & Gerdie Everaert, 2016. "Bias-Corrected Common Correlated Effects Pooled Estimation In Homogeneous Dynamic Panels," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 16/920, Ghent University, Faculty of Economics and Business Administration.
    7. De Vos, Ignace & Everaert, Gerdie & Sarafidis, Vasilis, 2021. "A method for evaluating the rank condition for CCE estimators," MPRA Paper 112305, University Library of Munich, Germany, revised 09 Mar 2022.
    8. Juodis, Arturas & Sarafidis, Vasilis, 2015. "A Simple Estimator for Short Panels with Common Factors," MPRA Paper 68164, University Library of Munich, Germany.
    9. Al Mamun, Md & Boubaker, Sabri & Hossain, Md Zakir & Manita, Riadh, 2024. "Female political empowerment and green finance," Energy Economics, Elsevier, vol. 131(C).
    10. Recep Ulucak & Danish & Yacouba Kassouri, 2020. "An assessment of the environmental sustainability corridor: Investigating the non‐linear effects of environmental taxation on CO2 emissions," Sustainable Development, John Wiley & Sons, Ltd., vol. 28(4), pages 1010-1018, July.
    11. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    12. Yan Sun & Wei Huang, 2022. "Quasi-maximum likelihood estimation of short panel data models with time-varying individual effects," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(1), pages 93-114, January.
    13. Marco Avarucci & Paolo Zaffaroni, 2019. "Robust Nearly-Efficient Estimation of Large Panels with Factor Structures," Papers 1902.11181, arXiv.org.
    14. Alharbi, Samar S. & Al Mamun, Md & Boubaker, Sabri & Rizvi, Syed Kumail Abbas, 2023. "Green finance and renewable energy: A worldwide evidence," Energy Economics, Elsevier, vol. 118(C).
    15. De Vos, Ignace & Stauskas, Ovidijus, 2024. "Cross-section bootstrap for CCE regressions," Journal of Econometrics, Elsevier, vol. 240(1).
    16. Dai, Siqi & Hong, Yongmiao & Li, Haiqi & Zheng, Chaowen, 2025. "Shrinkage estimation of spatial panel data models with multiple structural breaks and a multifactor error structure," Journal of Econometrics, Elsevier, vol. 251(C).

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