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Estimation of heterogeneous panels with structural breaks

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  • Baltagi, Badi H.
  • Feng, Qu
  • Kao, Chihwa

Abstract

This paper extends Pesaran’s (2006) work on common correlated effects (CCE) estimators for large heterogeneous panels with a general multifactor error structure by allowing for unknown common structural breaks. Structural breaks due to new policy implementation or major technological shocks, are more likely to occur over a longer time span. Consequently, ignoring structural breaks may lead to inconsistent estimation and invalid inference. We propose a general framework that includes heterogeneous panel data models and structural break models as special cases. The least squares method proposed by Bai (1997a, 2010) is applied to estimate the common change points, and the consistency of the estimated change points is established. We find that the CCE estimator have the same asymptotic distribution as if the true change points were known. Additionally, Monte Carlo simulations are used to verify the main results of this paper.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:econom:v:191:y:2016:i:1:p:176-195
    DOI: 10.1016/j.jeconom.2015.03.048
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    Cited by:

    1. Boneva, L. & Linton, O., 2017. "A Discrete Choice Model For Large Heterogeneous Panels with Interactive Fixed Effects with an Application to the Determinants of Corporate Bond Issuance," Cambridge Working Papers in Economics 1703, Faculty of Economics, University of Cambridge.
    2. Wang, Shaoping & Cui, Guowei & Li, Kunpeng, 2015. "Factor-augmented regression models with structural change," Economics Letters, Elsevier, vol. 130(C), pages 124-127.
    3. Timothy Neal, 2016. "Multidimensional Parameter Heterogeneity in Panel Data Models," Discussion Papers 2016-15, School of Economics, The University of New South Wales.
    4. repec:eee:eejocm:v:28:y:2018:i:c:p:108-123 is not listed on IDEAS
    5. Oka, Tatsushi & Perron, Pierre, 2018. "Testing for common breaks in a multiple equations system," Journal of Econometrics, Elsevier, vol. 204(1), pages 66-85.
    6. Qian, Junhui & Su, Liangjun, 2016. "Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso," Journal of Econometrics, Elsevier, vol. 191(1), pages 86-109.
    7. Ryo Okui & Wendun Wang, 2018. "Heterogeneous structural breaks in panel data models," Papers 1801.04672, arXiv.org, revised Nov 2018.
    8. repec:eee:ecolet:v:161:y:2017:i:c:p:141-145 is not listed on IDEAS
    9. repec:taf:jnlasa:v:111:y:2016:i:516:p:1804-1819 is not listed on IDEAS
    10. Timothy Neal, 2018. "Multidimensional Parameter Heterogeneity in Panel Data Models," Discussion Papers 2016-15A, School of Economics, The University of New South Wales.
    11. Otilia Boldea & Bettina Drepper & Zhuojiong Gan, 2018. "Change Point Estimation in Panel Data with Time-Varying Individual Effects," Papers 1808.03109, arXiv.org.
    12. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Papers 1805.03807, arXiv.org.
    13. Barbora Peštová & Michal Pešta, 2017. "Change Point Estimation in Panel Data without Boundary Issue," Risks, MDPI, Open Access Journal, vol. 5(1), pages 1-22, January.
    14. Huanjun Zhu & Vasilis Sarafidis & Mervyn Silvapulle & Jiti Gao, 2015. "Testing for a Structural Break in Dynamic Panel Data Models with Common Factors," Monash Econometrics and Business Statistics Working Papers 20/15, Monash University, Department of Econometrics and Business Statistics.

    More about this item

    Keywords

    Heterogeneous panels; Cross-sectional dependence; Structural breaks; Common correlated effects;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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