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Half‐panel jackknife fixed‐effects estimation of linear panels with weakly exogenous regressors

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  • Alexander Chudik
  • M. Hashem Pesaran
  • Jui‐Chung Yang

Abstract

This paper considers estimation and inference in linear panel regression models with lagged dependent variables and/or other weakly exogenous regressors when N (the cross‐section dimension) is large relative to T (the time series dimension). It allows for fixed and time effects (FE‐TE) and derives a general formula for the bias of the FE‐TE estimator which generalizes the well‐known Nickell bias formula derived for the pure autoregressive dynamic panel data models. It shows that in the presence of weakly exogenous regressors inference based on the FE‐TE estimator will result in size distortions unless N/T is sufficiently small. To deal with the bias and size distortion of the FE‐TE estimator the use of a half‐panel jackknife FE‐TE estimator is considered and its asymptotic distribution is derived. It is shown that the bias of the half‐panel jackknife FE‐TE estimator is of order T−2, and for valid inference it is only required that N/T3→0, as N,T→∞ jointly. Extension to unbalanced panel data models is also provided. The theoretical results are illustrated with Monte Carlo evidence. It is shown that the FE‐TE estimator can suffer from large size distortions when N>T, with the half‐panel jackknife FE‐TE estimator showing little size distortions. The use of half‐panel jackknife FE‐TE estimator is illustrated with two empirical applications from the literature.

Suggested Citation

  • Alexander Chudik & M. Hashem Pesaran & Jui‐Chung Yang, 2018. "Half‐panel jackknife fixed‐effects estimation of linear panels with weakly exogenous regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(6), pages 816-836, September.
    • Handle: RePEc:wly:japmet:v:33:y:2018:i:6:p:816-836
    DOI: 10.1002/jae.2623
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    1. Alexander Chudik & M. Hashem Pesaran & Ron P. Smith, 2021. "Pooled Bewley Estimator of Long-Run Relationships in Dynamic Heterogenous Panels," Globalization Institute Working Papers 409, Federal Reserve Bank of Dallas, revised 08 Nov 2023.
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    11. Ziwei Mei & Liugang Sheng & Zhentao Shi, 2023. "Nickell Bias in Panel Local Projection: Financial Crises Are Worse Than You Think," Papers 2302.13455, arXiv.org, revised Oct 2023.
    12. Andrea Nocera & M. Hashem Pesaran, 2022. "Causal Effects of the Fed's Large-Scale Asset Purchases on Firms' Capital Structure," CESifo Working Paper Series 9695, CESifo.
    13. Chen, Weihao & Cizek, Pavel, 2023. "Bias-Corrected Instrumental Variable Estimation in Linear Dynamic Panel Data Models," Other publications TiSEM 9bf2c16c-522f-4223-8037-c, Tilburg University, School of Economics and Management.
    14. Higgins, Ayden & Jochmans, Koen, 2025. "Inference in Dynamic Models for Panel Data Using The Moving Block Bootstrap," TSE Working Papers 25-1620, Toulouse School of Economics (TSE).
    15. Rabe, Collin & Waddle, Andrea, 2020. "The evolution of purchasing power parity," Journal of International Money and Finance, Elsevier, vol. 109(C).
    16. Wojciech Charemza & Svetlana Makarova & Krzysztof Rybiński, 2023. "Anti-pandemic restrictions, uncertainty and sentiment in seven countries," Economic Change and Restructuring, Springer, vol. 56(1), pages 1-27, February.
    17. Hoskins, Stephen & Johnston, David W. & Kunz, Johannes S. & Shields, Michael A. & Staub, Kevin E., 2024. "Heterogeneity in the Persistence of Health: Evidence from a Monthly Micro Panel," IZA Discussion Papers 17023, Institute of Labor Economics (IZA).
    18. Chihwa Kao & Long Liu & Rui Sun, 2021. "A bias-corrected fixed effects estimator in the dynamic panel data model," Empirical Economics, Springer, vol. 60(1), pages 205-225, January.
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