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A new structural break test for panels with common factors
[Panel data models with multiple time-varying individual effects]

Author

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  • Huanjun Zhu
  • Vasilis Sarafidis
  • Mervyn J Silvapulle

Abstract

SummaryThis paper develops new tests against a structural break in panel data models with common factors when T is fixed, where T denotes the number of observations over time. For this class of models, the available tests against a structural break are valid only under the assumption that T is ‘large’. However, this may be a stringent requirement—more commonly so in datasets with annual time frequency, in which case the sample may cover a relatively long period even if T is not large. The proposed approach builds upon existing generalized method of moments methodology and develops Distance-type and Lagrange Multiplier-type tests for detecting a structural break, both when the break point is known and when it is unknown. The proposed methodology permits weak exogeneity and/or endogeneity of the regressors. In a simulation study, the method performed well, in terms of size and power, as well as in terms of successfully locating the time of the structural break. The method is illustrated by testing the so-called ‘Gibrat’s Law’, using a dataset from 4,128 financial institutions, each one observed for the period 2002–2014.

Suggested Citation

  • Huanjun Zhu & Vasilis Sarafidis & Mervyn J Silvapulle, 2020. "A new structural break test for panels with common factors [Panel data models with multiple time-varying individual effects]," The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 137-155.
    • Handle: RePEc:oup:emjrnl:v:23:y:2020:i:1:p:137-155.
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    File URL: http://hdl.handle.net/10.1093/ectj/utz018
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    Citations

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    Cited by:

    1. 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.
    2. Artūras Juodis & Yiannis Karavias & Vasilis Sarafidis, 2021. "A homogeneous approach to testing for Granger non-causality in heterogeneous panels," Empirical Economics, Springer, vol. 60(1), pages 93-112, January.
    3. Guowei Cui & Vasilis Sarafidis & Takashi Yamagata, 2020. "IV Estimation of Spatial Dynamic Panels with Interactive Effects: Large Sample Theory and an Application on Bank Attitude," Monash Econometrics and Business Statistics Working Papers 11/20, Monash University, Department of Econometrics and Business Statistics.
    4. De Vos, Ignace & Everaert, Gerdie & Sarafidis, Vasilis, 2021. "A method for evaluating the rank condition for CCE estimators," MPRA Paper 112305, University Library of Munich, Germany, revised 09 Mar 2022.
    5. Guowei Cui & Vasilis Sarafidis & Takashi Yamagata, 2023. "IV estimation of spatial dynamic panels with interactive effects: large sample theory and an application on bank attitude towards risk," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 124-146.

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