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Testing structural changes in panel data with small fixed panel size and bootstrap

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  • Barbora Peštová
  • Michal Pešta

Abstract

Panel data of our interest consist of a moderate or relatively large number of panels, while the panels contain a small number of observations. This paper establishes testing procedures to detect a possible common change in means of the panels. To this end, we consider a ratio type test statistic and derive its asymptotic distribution under the no change null hypothesis. Moreover, we prove the consistency of the test under the alternative. The main advantage of such an approach is that the variance of the observations neither has to be known nor estimated. On the other hand, the correlation structure is required to be calculated. To overcome this issue, a bootstrap technique is proposed in the way of a completely data driven approach without any tuning parameters. The validity of the bootstrap algorithm is shown. As a by-product of the developed tests, we introduce a common break point estimate and prove its consistency. The results are illustrated through a simulation study. An application of the procedure to actuarial data is presented. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Barbora Peštová & Michal Pešta, 2015. "Testing structural changes in panel data with small fixed panel size and bootstrap," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(6), pages 665-689, August.
    • Handle: RePEc:spr:metrik:v:78:y:2015:i:6:p:665-689
    DOI: 10.1007/s00184-014-0522-8
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    References listed on IDEAS

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    1. Bai, Jushan, 2010. "Common breaks in means and variances for panel data," Journal of Econometrics, Elsevier, vol. 157(1), pages 78-92, July.
    2. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    3. 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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