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Structural breaks in panel data: Large number of panels and short length time series

Author

Listed:
  • Jaromír Antoch
  • Jan Hanousek
  • Lajos Horváth
  • Marie Hušková
  • Shixuan Wang

Abstract

The detection of (structural) breaks or the so called change point problem has drawn increasing attention from the theoretical, applied economic and financial fields. Much of the existing research concentrates on the detection of change points and asymptotic properties of their estimators in panels when N, the number of panels, as well as T, the number of observations in each panel are large. In this paper we pursue a different approach, i.e., we consider the asymptotic properties when N→∞ while keeping T fixed. This situation is typically related to large (firm-level) data containing financial information about an immense number of firms/stocks across a limited number of years/quarters/months. We propose a general approach for testing for break(s) in this setup. In particular, we obtain the asymptotic behavior of test statistics. We also propose a wild bootstrap procedure that could be used to generate the critical values of the test statistics. The theoretical approach is supplemented by numerous simulations and by an empirical illustration. We demonstrate that the testing procedure works well in the framework of the four factors CAPM model. In particular, we estimate the breaks in the monthly returns of US mutual funds during the period January 2006 to February 2010 which covers the subprime crises.

Suggested Citation

  • Jaromír Antoch & Jan Hanousek & Lajos Horváth & Marie Hušková & Shixuan Wang, 2019. "Structural breaks in panel data: Large number of panels and short length time series," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 828-855, August.
    • Handle: RePEc:taf:emetrv:v:38:y:2019:i:7:p:828-855
    DOI: 10.1080/07474938.2018.1454378
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    Cited by:

    1. Jan Ditzen & Yiannis Karavias & Joakim Westerlund, 2022. "Multiple Structural Breaks in Interactive Effects Panel Data and the Impact of Quantitative Easing on Bank Lending," Papers 2211.06707, arXiv.org, revised Jan 2023.
    2. Jiang, Peiyun & Kurozumi, Eiji, 2026. "A new test for common breaks in heterogeneous panel data models," Econometrics and Statistics, Elsevier, vol. 37(C), pages 87-125.
    3. Adu-Darko, Eunice & You, Kefei, 2025. "Structural breaks, institutional quality and productivity growth in Sub-Saharan Africa," Structural Change and Economic Dynamics, Elsevier, vol. 74(C), pages 40-60.
    4. Rogneda Vasilyeva & Anton Skrobotov & Aleksei Tsarev, 2025. "Structural breaks in panel data: COVID-19 pandemic in Russian regions," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 80, pages 117-142.
    5. Shugang Li & Fang Liu & Yuqi Zhang & Zhaoxu Yu, 2025. "Lean persuasive design of electronic word-of-mouth (e-WOM) indexes for e-commerce stores based on fogg behavior model," Electronic Commerce Research, Springer, vol. 25(4), pages 2463-2502, August.
    6. Yiannis Karavias & Paresh Kumar Narayan & Joakim Westerlund, 2023. "Structural Breaks in Interactive Effects Panels and the Stock Market Reaction to COVID-19," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(3), pages 653-666, July.
    7. Kaddoura, Yousef, 2025. "Estimating coefficient-by-coefficient breaks in panel data models," Journal of Econometrics, Elsevier, vol. 249(PB).
    8. Klaudia Jarno & Hanna Kołodziejczyk, 2021. "Does the Design of Stablecoins Impact Their Volatility?," JRFM, MDPI, vol. 14(2), pages 1-14, January.
    9. Kraft, Kornelius & Lammers, Alexander, 2021. "The Effects of Reforming a Federal Employment Agency on Labor Demand," IZA Discussion Papers 14629, IZA Network @ LISER.
    10. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Papers 1805.03807, arXiv.org.
    11. Kraft, Kornelius & Lammers, Alexander, 2021. "Bargaining Power and the Labor Share - a Structural Break Approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242342, Verein für Socialpolitik / German Economic Association.
    12. Maran Marimuthu & Hanana Khan & Romana Bangash, 2021. "Reverse Causality between Fiscal and Current Account Deficits in ASEAN: Evidence from Panel Econometric Analysis," Mathematics, MDPI, vol. 9(10), pages 1-18, May.
    13. Jaromír Antoch & Jan Hanousek & Marie Hušková & Jiří Trešl, 2019. "Detekce změn v panelových datech: Změna parametrů Fama-French modelu u vybraných evropských akcií v období finanční krize [Detection of Changes in Panel Data: Change in Fama-French Model Parameters for Selected European Stocks During the Financial," Politická ekonomie, Prague University of Economics and Business, vol. 2019(1), pages 3-19.
    14. Matúš Maciak & Michal Pešta & Barbora Peštová, 2020. "Changepoint in dependent and non-stationary panels," Statistical Papers, Springer, vol. 61(4), pages 1385-1407, August.
    15. Karamti, Chiraz & Jeribi, Ahmed, 2023. "Stock markets from COVID-19 to the Russia–Ukraine crisis: Structural breaks in interactive effects panels," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    16. Christos Argyropoulos & Bertrand Candelon & Jean‐Baptiste Hasse & Ekaterini Panopoulou, 2024. "Towards a macroprudential regulatory framework for mutual funds?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3063-3082, July.
    17. Phong B. Dao, 2021. "A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    18. Jiang, Peiyun & Kurozumi, Eiji, 2021. "A new test for common breaks in heterogeneous panel data models," Discussion paper series HIAS-E-107, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    19. Daniel Ventosa‐Santaulària & Luis G. Hernández‐Román & Alejandro Villagómez Amezcua, 2021. "Recessions and potential GDP: The case of Mexico," Bulletin of Economic Research, Wiley Blackwell, vol. 73(2), pages 179-195, April.

    More about this item

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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