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To Pool or not to Pool: Revisited

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Abstract

This paper provides a new comparative analysis of pooled least squares and fixed effects estimators of the slope coefficients in the case of panel data models when the time dimension (T) is fixed while the cross section dimension (N) is allowed to increase without bounds. The individual effects are allowed to be correlated with the regressors, and the comparison is carried out in terms of an exponent coefficient, delta, which measures the degree of pervasiveness of the fixed effects in the panel. The use of delta allows us to distinguish between poolability of small N dimensional panels with large T from large N dimensional panels with small T. It is shown that the pooled estimator remains consistent so long as delta

Suggested Citation

  • M. Hashem Pesaran & Qiankun Zhou, 2017. "To Pool or not to Pool: Revisited," Departmental Working Papers 2017-13, Department of Economics, Louisiana State University.
    • Handle: RePEc:lsu:lsuwpp:2017-13
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    File URL: https://www.lsu.edu/business/economics/files/workingpapers/pap17_13.pdf
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    1. Kurter, Zeynep O. & Bhatti, Balaaj, 2025. "The Effect of AI Investment Announcements on Adopting Companies Abnormal Returns : A Critical Analysis of the UK Market," The Warwick Economics Research Paper Series (TWERPS) 1581, University of Warwick, Department of Economics.
    2. Akgun, Oguzhan & Pirotte, Alain & Urga, Giovanni, 2020. "Forecasting using heterogeneous panels with cross-sectional dependence," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1211-1227.
    3. Siddik, Abu Bakkar & Forid, Md. Shak & Yong, Li & Du, Anna Min & Goodell, John W., 2025. "Artificial intelligence as a catalyst for sustainable tourism growth and economic cycles," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    4. Zhan Gao & M. Hashem Pesaran, 2024. "Identification and estimation of categorical random coefficient models," Advanced Studies in Theoretical and Applied Econometrics, in: Subal C. Kumbhakar & Robin C. Sickles & Hung-Jen Wang (ed.), Advances in Applied Econometrics, pages 79-124, Springer.
    5. David Schröder & Andrew Yim, 2018. "Industry Effects in Firm and Segment Profitability Forecasting," Contemporary Accounting Research, John Wiley & Sons, vol. 35(4), pages 2106-2130, December.
    6. Dong, Hao & Millimet, Daniel L., 2023. "Embrace the Noise: It Is OK to Ignore Measurement Error in a Covariate, Sometimes," IZA Discussion Papers 16508, Institute of Labor Economics (IZA).

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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