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

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  • Hanousek, Jan
  • Antoch, Jaromir
  • Huskova, Marie
  • Horvath, Lajos
  • Wang, Shixuan

Abstract

The detection of the (structural) break or so called change point problem has drawn increasing attention from both theoretical and applied economic and financial research over the last decade. A large part of the existing research concentrates on the detection and asymptotic properties of the change point problem for panels with a large time dimension T. In this article we study a different approach, i.e., we consider the asymptotic properties with respect to N (number of panel members) while keeping T fixed. This situation (N → ∞ but T being fixed and rather small) is typically related to large (firm-level) data containing financial information about an immerse number of firms/stocks across a limited number of years/quarters/months. We propose a general approach for testing for the break(s) in this setup, which also allows their detection. In particular, we show the asymptotic behavior of the test statistics, along with an alternative 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 extended by an empirical illustration. In the practical application we demonstrate the testing procedure in the framework of the four factors CAPM model. In particular, we estimate breaks in monthly returns of the US mutual funds during the period January 2006 to February 2010 which covers the subprime crises.

Suggested Citation

  • Hanousek, Jan & Antoch, Jaromir & Huskova, Marie & Horvath, Lajos & Wang, Shixuan, 2017. "Structural breaks in panel data: Large number of panels and short length time series," CEPR Discussion Papers 11891, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:11891
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    1. 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.
    2. 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.
    3. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Papers 1805.03807, arXiv.org.
    4. 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.
    5. 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.
    6. 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," Politická ekonomie, Prague University of Economics and Business, vol. 2019(1), pages 3-19.
    7. Matúš Maciak & Michal Pešta & Barbora Peštová, 2020. "Changepoint in dependent and non-stationary panels," Statistical Papers, Springer, vol. 61(4), pages 1385-1407, August.
    8. 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).
    9. 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.
    10. 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.
    11. 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.
    12. Klaudia Jarno & Hanna Kołodziejczyk, 2021. "Does the Design of Stablecoins Impact Their Volatility?," JRFM, MDPI, vol. 14(2), pages 1-14, January.
    13. 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.
    14. Kraft, Kornelius & Lammers, Alexander, 2021. "The Effects of Reforming a Federal Employment Agency on Labor Demand," IZA Discussion Papers 14629, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    Change point problem; Stationarity; Four factor capm model; Us mutual funds; Panel data; Bootstrap;
    All these keywords.

    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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