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CCE in fixed‐T panels

Author

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  • Joakim Westerlund
  • Yana Petrova
  • Milda Norkute

Abstract

The presence of unobserved heterogeneity and its likely detrimental effect on inference has recently motivated the use of factor‐augmented panel regression models. The workhorse of this literature is based on first estimating the unknown factors using the cross‐section averages of the observables, and then applying ordinary least squares conditional on the first‐step factor estimates. This is the common correlated effects (CCE) approach, the existing asymptotic theory for which is based on the requirement that both the number of time series observations, T, and the number of cross‐section units, N, tend to infinity. The obvious implication of this theory for empirical work is that both N and T should be large, which means that CCE is impossible for the typical micro panel where only N is large. In the current paper, we put the existing CCE theory and its implications to a test. This is done by developing a new theory that enables T to be fixed. The results show that many of the previously derived large‐T results hold even if T is fixed. In particular, the pooled CCE estimator is still consistent and asymptotically normal, which means that CCE is more applicable than previously thought. In fact, not only do we allow T to be fixed, but the conditions placed on the time series properties of the factors and idiosyncratic errors are also much more general than those considered previously.

Suggested Citation

  • Joakim Westerlund & Yana Petrova & Milda Norkute, 2019. "CCE in fixed‐T panels," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 746-761, August.
  • Handle: RePEc:wly:japmet:v:34:y:2019:i:5:p:746-761
    DOI: 10.1002/jae.2707
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    Citations

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    Cited by:

    1. Jörg Breitung & Philipp Hansen, 2021. "Alternative estimation approaches for the factor augmented panel data model with small T," Empirical Economics, Springer, vol. 60(1), pages 327-351, January.
    2. Nicholas Brown & Kyle Butts & Joakim Westerlund, 2023. "Simple Difference-in-Differences Estimation in Fixed-T Panels," Papers 2301.11358, arXiv.org, revised Jun 2023.
    3. Brown, Nicholas & Westerlund, Joakim, 2023. "Testing factors in CCE," Economics Letters, Elsevier, vol. 230(C).
    4. Hugo Freeman & Martin Weidner, 2021. "Linear Panel Regressions with Two-Way Unobserved Heterogeneity," Papers 2109.11911, arXiv.org, revised Aug 2022.
    5. Juodis, Artūras & Sarafidis, Vasilis, 2022. "An incidental parameters free inference approach for panels with common shocks," Journal of Econometrics, Elsevier, vol. 229(1), pages 19-54.
    6. Stauskas, Ovidijus & De Vos, Ignace, 2024. "Handling Distinct Correlated Effects with CCE," MPRA Paper 120194, University Library of Munich, Germany.
    7. Vogt, M. & Walsh, C. & Linton, O., 2022. "CCE Estimation of High-Dimensional Panel Data Models with Interactive Fixed Effects," Cambridge Working Papers in Economics 2242, Faculty of Economics, University of Cambridge.
    8. 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.
    9. De Pascale, Gianluigi & Fiore, Mariantonietta & Contò, Francesco, 2021. "Short and long run environmental tax buoyancy in EU-28: a panel study," International Economics, Elsevier, vol. 168(C), pages 1-9.
    10. Floros Flouros & Victoria Pistikou & Vasilios Plakandaras, 2022. "Geopolitical Risk as a Determinant of Renewable Energy Investments," Energies, MDPI, vol. 15(4), pages 1-21, February.
    11. Amendolagine, Vito & De Pascale, Gianluigi & Faccilongo, Nicola, 2021. "International capital mobility and corporate tax revenues: How do controlled foreign company rules and innovation shape this relationship?," Economic Modelling, Elsevier, vol. 101(C).
    12. Freeman, Hugo & Weidner, Martin, 2023. "Linear panel regressions with two-way unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 237(1).
    13. Vogt, M. & Walsh, C. & Linton, O., 2022. "CCE Estimation of High-Dimensional Panel Data Models with Interactive Fixed Effects," Janeway Institute Working Papers 2218, Faculty of Economics, University of Cambridge.
    14. Kabaya, Kei, 2021. "Empirical analysis of associations between health expenditure and forest environments: A case of Japan," Ecological Economics, Elsevier, vol. 181(C).
    15. Karamti, Chiraz & Jeribi, Ahmed, 2023. "Stock markets from COVID-19 to the Russia–Ukraine crisis: Structural breaks in interactive effects panels," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    16. Michael Vogt & Christopher Walsh & Oliver Linton, 2022. "CCE Estimation of High-Dimensional Panel Data Models with Interactive Fixed Effects," Papers 2206.12152, arXiv.org.
    17. 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.
    18. Wu, Jianhong, 2020. "A joint test for serial correlation and heteroscedasticity in fixed-T panel regression models with interactive effects," Economics Letters, Elsevier, vol. 197(C).
    19. Hong, Shengjie & Su, Liangjun & Jiang, Tao, 2023. "Profile GMM estimation of panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 235(2), pages 927-948.
    20. S. M. Woahid Murad, 2022. "The role of domestic and foreign economic uncertainties in determining the foreign exchange rates: an extended monetary approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(4), pages 666-677, October.

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