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Rank-based test for slope homogeneity in high-dimensional panel data models

Author

Listed:
  • Yanling Ding

    (Changchun Institute of Technology)

  • Binghui Liu

    (Northeast Normal University)

  • Ping Zhao

    (LPMC and KLMDASR, Nankai University)

  • Long Feng

    (LPMC and KLMDASR, Nankai University)

Abstract

A large number of existing high-dimensional panel data analyses are established based on normal or nearly normal distribution assumptions, which may be not robust to severe departures of normality. Since the observed data may not follow the normal distribution in some specific applications, it is necessary to design robust tests to departures of normality. On this ground, we propose a rank-based score test for testing slope homogeneity in high-dimensional panel data regressions, where robust tests to departures of normality are still rare. Both theoretical and numerical results demonstrate the advantage of the proposed test in robustness to departures of normality.

Suggested Citation

  • Yanling Ding & Binghui Liu & Ping Zhao & Long Feng, 2022. "Rank-based test for slope homogeneity in high-dimensional panel data models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(5), pages 605-626, July.
    • Handle: RePEc:spr:metrik:v:85:y:2022:i:5:d:10.1007_s00184-021-00845-y
    DOI: 10.1007/s00184-021-00845-y
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    References listed on IDEAS

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    1. Swamy, P A V B, 1970. "Efficient Inference in a Random Coefficient Regression Model," Econometrica, Econometric Society, vol. 38(2), pages 311-323, March.
    2. Ted Juhl & Oleksandr Lugovskyy, 2014. "A Test for Slope Heterogeneity in Fixed Effects Models," Econometric Reviews, Taylor & Francis Journals, vol. 33(8), pages 906-935, November.
    3. Hashem Pesaran, M. & Yamagata, Takashi, 2008. "Testing slope homogeneity in large panels," Journal of Econometrics, Elsevier, vol. 142(1), pages 50-93, January.
    4. Peter C. B. Phillips & Donggyu Sul, 2003. "Dynamic panel estimation and homogeneity testing under cross section dependence *," Econometrics Journal, Royal Economic Society, vol. 6(1), pages 217-259, June.
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