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Interpretation and identification of within-unit and cross-sectional variation in panel data models

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  • Jonathan Kropko
  • Robert Kubinec

Abstract

While fixed effects (FE) models are often employed to address potential omitted variables, we argue that these models’ real utility is in isolating a particular dimension of variance from panel data for analysis. In addition, we show through novel mathematical decomposition and simulation that only one-way FE models cleanly capture either the over-time or cross-sectional dimensions in panel data, while the two-way FE model unhelpfully combines within-unit and cross-sectional variation in a way that produces un-interpretable answers. In fact, as we show in this paper, if we begin with the interpretation that many researchers wrongly assign to the two-way FE model—that it represents a single estimate of X on Y while accounting for unit-level heterogeneity and time shocks—the two-way FE specification is statistically unidentified, a fact that statistical software packages like R and Stata obscure through internal matrix processing.

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  • Jonathan Kropko & Robert Kubinec, 2020. "Interpretation and identification of within-unit and cross-sectional variation in panel data models," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-22, April.
    • Handle: RePEc:plo:pone00:0231349
    DOI: 10.1371/journal.pone.0231349
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    References listed on IDEAS

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    Cited by:

    1. Nour Mohamad Fayad, 2024. "The Causality Between Corruption and Economic Growth in MENA Countries: A Dynamic Panel-Data Analysis," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 14(1), pages 28-49.
    2. Luis Costa & Vivek F. Farias & Patricio Foncea & Jingyuan (Donna) Gan & Ayush Garg & Ivo Rosa Montenegro & Kumarjit Pathak & Tianyi Peng & Dusan Popovic, 2023. "Generalized Synthetic Control for TestOps at ABI: Models, Algorithms, and Infrastructure," Interfaces, INFORMS, vol. 53(5), pages 336-349, September.
    3. Maria Elena Bontempi & Jan Ditzen, 2023. "GMM-lev estimation and individual heterogeneity: Monte Carlo evidence and empirical applications," Papers 2312.00399, arXiv.org, revised Dec 2023.

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