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A Weighted Regression Approach to Break-Point Detection in Panel Data

In: Asymptotic and Methodological Statistics

Author

Listed:
  • Charl Pretorius

    (North-West University, Centre for Business Mathematics and Informatics)

  • Heinrich Roodt

    (North-West University, Pure and Applied Analytics)

Abstract

New procedures for detecting a change in the cross-sectional mean of panel data are proposed. The procedures rely on estimating nuisance parameters using certain cross-sectional means across panels using a weighted least squares regression. In the case of weak cross-sectional dependence between panels, we show how test statistics can be constructed to have a limit null distribution not depending on any choice of bandwidths typically needed to estimate the long-run variances of the panel errors. The theoretical assertions are derived for general choices of the regression weights, and it is shown that consistent test procedures can be obtained from the proposed process. The theoretical results are extended to the case where strong cross-sectional dependence exist between panels. The paper concludes with a numerical study illustrating the behavior of several special cases of the test procedure in finite samples.

Suggested Citation

  • Charl Pretorius & Heinrich Roodt, 2026. "A Weighted Regression Approach to Break-Point Detection in Panel Data," Springer Books, in: Daniel Hlubinka & Šárka Hudecová & Matúš Maciak & Michal Pešta (ed.), Asymptotic and Methodological Statistics, chapter 0, pages 217-239, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-07178-1_11
    DOI: 10.1007/978-3-032-07178-1_11
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