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Changepoint in dependent and non-stationary panels

Author

Listed:
  • Matúš Maciak

    (Charles University)

  • Michal Pešta

    (Charles University)

  • Barbora Peštová

    (The Czech Academy of Sciences)

Abstract

Detection procedures for a change in means of panel data are proposed. Unlike classical inference tools used for the changepoint analysis in the panel data framework, we allow for mutually dependent and generally non-stationary panels with an extremely short follow-up period. Two competitive self-normalized test statistics are employed and their asymptotic properties are derived for a large number of available panels. The bootstrap extensions are introduced in order to handle such a universal setup. The novel changepoint methods are able to detect a common break point even when the change occurs immediately after the first time point or just before the last observation period. The developed tests are proved to be consistent. Their empirical properties are investigated through a simulation study. The invented techniques are applied to option pricing and non-life insurance.

Suggested Citation

  • Matúš Maciak & Michal Pešta & Barbora Peštová, 2020. "Changepoint in dependent and non-stationary panels," Statistical Papers, Springer, vol. 61(4), pages 1385-1407, August.
    • Handle: RePEc:spr:stpapr:v:61:y:2020:i:4:d:10.1007_s00362-020-01180-6
    DOI: 10.1007/s00362-020-01180-6
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    References listed on IDEAS

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    9. Michal Pešta & Martin Wendler, 2020. "Nuisance-parameter-free changepoint detection in non-stationary series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 379-408, June.
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    Cited by:

    1. Martin Hrba & Matúš Maciak & Barbora Peštová & Michal Pešta, 2022. "Bootstrapping Not Independent and Not Identically Distributed Data," Mathematics, MDPI, vol. 10(24), pages 1-26, December.
    2. Zdeněk Drábek & Miloš Kopa & Matúš Maciak & Michal Pešta & Sebastiano Vitali, 2023. "Investment disputes and their explicit role in option market uncertainty and overall risk instability," Computational Management Science, Springer, vol. 20(1), pages 1-25, December.
    3. Rodi Lykou & George Tsaklidis, 2021. "Particle Filtering: A Priori Estimation of Observational Errors of a State-Space Model with Linear Observation Equation," Mathematics, MDPI, vol. 9(12), pages 1-16, June.
    4. Maciak, Matúš & Okhrin, Ostap & Pešta, Michal, 2021. "Infinitely stochastic micro reserving," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 30-58.
    5. Michal Pešta, 2021. "Changepoint in Error-Prone Relations," Mathematics, MDPI, vol. 9(1), pages 1-25, January.
    6. Shashi Bhushan & Anoop Kumar & Usman Shahzad & Amer Ibrahim Al-Omari & Ibrahim Mufrah Almanjahie, 2022. "On Some Improved Class of Estimators by Using Stratified Ranked Set Sampling," Mathematics, MDPI, vol. 10(18), pages 1-32, September.

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