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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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    1. De Wachter, Stefan & Tzavalis, Elias, 2012. "Detection of structural breaks in linear dynamic panel data models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3020-3034.
    2. Bai, Jushan, 2010. "Common breaks in means and variances for panel data," Journal of Econometrics, Elsevier, vol. 157(1), pages 78-92, July.
    3. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    4. Annika Betken, 2016. "Testing for Change-Points in Long-Range Dependent Time Series by Means of a Self-Normalized Wilcoxon Test," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 785-809, November.
    5. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2016. "Estimation of heterogeneous panels with structural breaks," Journal of Econometrics, Elsevier, vol. 191(1), pages 176-195.
    6. Shao, Xiaofeng & Zhang, Xianyang, 2010. "Testing for Change Points in Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1228-1240.
    7. 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.
    8. Andrews, Donald W K, 1993. "Tests for Parameter Instability and Structural Change with Unknown Change Point," Econometrica, Econometric Society, vol. 61(4), pages 821-856, July.
    9. Dimitris Politis & Halbert White, 2004. "Automatic Block-Length Selection for the Dependent Bootstrap," Econometric Reviews, Taylor & Francis Journals, vol. 23(1), pages 53-70.
    10. Xiaofeng Shao, 2011. "A simple test of changes in mean in the possible presence of long‐range dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(6), pages 598-606, November.
    11. 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.
    12. Marie Hušková & Claudia Kirch, 2012. "Bootstrapping sequential change-point tests for linear regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(5), pages 673-708, July.
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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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