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Inference in Dynamic Models for Panel Data Using The Moving Block Bootstrap

Author

Listed:
  • Ayden Higgins

    (University of Exeter)

  • Koen Jochmans

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

Inference in linear panel data models is complicated by the presence of fixed effects when (some of) the regressors are not strictly exogenous. Under asymptotics where the number of cross-sectional observations and time periods grow at the same rate, the within-group estimator is consistent but its limit distribution features a bias term. In this paper we show that a panel version of the moving block bootstrap, where blocks of adjacent cross-sections are resampled with replacement, replicates the limit distribution of the within-group estimator. Confidence ellipsoids and hypothesis tests based on the reverse-percentile bootstrap are thus asymptotically valid without the need to take the presence of bias into account.

Suggested Citation

  • Ayden Higgins & Koen Jochmans, 2025. "Inference in Dynamic Models for Panel Data Using The Moving Block Bootstrap," Working Papers hal-04947761, HAL.
    • Handle: RePEc:hal:wpaper:hal-04947761
    Note: View the original document on HAL open archive server: https://hal.science/hal-04947761v1
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    Keywords

    Bootstrap; Dynamic model; Fixed effects; Inference; Asymptotic bias;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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