Stationary bootstrapping for common mean change detection in cross-sectionally dependent panels

Eunju Hwang, Dong Wan Shin

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Stationary bootstrapping is applied to a CUSUM test for common mean break detection in cross-sectionally correlated panel data. Asymptotic null distribution of the bootstrapped test is derived, which is the same as that of the original CUSUM test depending on cross-sectional correlation parameter. A bootstrap test using the CUSUM test with bootstrap critical values is proposed and its asymptotic validity is proved. Finite sample Monte-Carlo simulation shows that the proposed test has reasonable size while other existing tests have severe size distortion under cross-section correlation. The simulation also shows good power performance of the proposed test against non-cancelling mean changes. The simulation also shows that the theoretically justified stationary bootstrapping CUSUM test has comparable size and power relative to other, theoretically unjustified, moving block or tapered block bootstrapping CUSUM tests.

Original languageEnglish
Pages (from-to)767-787
Number of pages21
Issue number6-8
StatePublished - 1 Nov 2017

Bibliographical note

Funding Information:
Acknowledgements The authors are very grateful for the constructive comments of a referee which lead us to a substantially improved paper. This study was supported by grants from the National Research Foundation of Korea (NRF-2016R1A2B4008780, NRF-2015-1006133).

Publisher Copyright:
© 2017, Springer-Verlag GmbH Germany.


  • Bootstrap test
  • Common panel mean change
  • Cross-section correlation
  • Size distortion
  • Stationary bootstrapping


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