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Alternative estimation approaches for the factor augmented panel data model with small T

Author

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  • Jörg Breitung

    (University of Cologne)

  • Philipp Hansen

    (University of Cologne)

Abstract

In this paper, we compare alternative estimation approaches for factor augmented panel data models. Our focus lies on panel data sets where the number of panel groups (N) is large relative to the number of time periods (T). The principal component (PC) and common correlated effects (CCE) estimators were originally developed for panel data with large N and T, whereas the GMM approaches of Ahn et al. (J Econ 728 174:1–14, 2013) and Robertson and Sarafidis (J Econ 185(2):526–541, 2015) assume that T is small (that is T is fixed in the asymptotic analysis). Our comparison of existing methods addresses three different issues. First, we analyze the possibility of an inappropriate normalization of the factor space (the so-called normalization failure). In particular we propose a variant of the CCE estimator that avoids the normalization failure by adapting a weighting scheme inspired by the analysis of Mundlak (Econometrica 46(1):69–85, 1978). Second, we analyze the effects of estimating versus fixing the number of factors in advance. Third, we demonstrate how the design of the Monte Carlo simulations favors some estimators, which explains the conflicting findings from existing Monte Carlo experiments.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:empeco:v:60:y:2021:i:1:d:10.1007_s00181-020-01948-7
    DOI: 10.1007/s00181-020-01948-7
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Nicholas Brown & Kyle Butts & Joakim Westerlund, 2023. "Simple Difference-in-Differences Estimation in Fixed-T Panels," Papers 2301.11358, arXiv.org, revised Jun 2023.
    2. 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.
    3. Qi, Songqiao & Sun, Tianmin, 2024. "Resource curse in OPEC with varied levels of financial regulations and constraints: The role of oil price shocks and digital finance," Resources Policy, Elsevier, vol. 91(C).
    4. Qi Li & Vasilis Sarafidis & Joakim Westerlund, 2021. "Essays in honor of Professor Badi H Baltagi," Empirical Economics, Springer, vol. 60(1), pages 1-11, January.
    5. Li, Qi & Sarafidis, Vasilis & Westerlund, Joakim, 2020. "Essays in Honor of Professor Badi H Baltagi: Editorial," MPRA Paper 104751, University Library of Munich, Germany.
    6. 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.
    7. Masud Alam, 2024. "Output, employment, and price effects of U.S. narrative tax changes: a factor-augmented vector autoregression approach," Empirical Economics, Springer, vol. 67(4), pages 1421-1471, October.

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    More about this item

    Keywords

    Panel data; Interactive fixed effects; CCE estimator; GMM;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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