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On estimation of the change points in multivariate regression models with structural changes

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  • Fuqi Chen
  • Sévérien Nkurunziza

Abstract

In this article, we consider the estimation of possibly multiple change points in multivariate regression models with structural changes. A salient feature of the methods is that the dependence structure of the error terms and the regressors can be as weak as that of L2$\mathcal {L}^{2}$-Mixingale arrays of size − 1/2. Further, we also provide some numerical simulations and a real data application to illustrate the efficiency of the proposed methods.

Suggested Citation

  • Fuqi Chen & Sévérien Nkurunziza, 2017. "On estimation of the change points in multivariate regression models with structural changes," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(14), pages 7157-7173, July.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:14:p:7157-7173
    DOI: 10.1080/03610926.2016.1143510
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    Cited by:

    1. Sévérien Nkurunziza, 2023. "On efficiency of some restricted estimators in a multivariate regression model," Statistical Papers, Springer, vol. 64(2), pages 617-642, April.
    2. Qing Yang & Yu-Ning Li & Yi Zhang, 2020. "Change point detection for nonparametric regression under strongly mixing process," Statistical Papers, Springer, vol. 61(4), pages 1465-1506, August.

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