Testing for Multiple Structural Breaks in Multivariate Long Memory Regression Models

Vivien Less, Paulo M. M. Rodrigues and Philipp Sibbertsen

Hannover Economic Papers (HEP) from Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät

Abstract: This paper focuses on the estimation and testing of multiple breaks that occur at unknown dates in multivariate long memory time series regression models, allowing for fractional cointegration. A likelihood-ratio based approach for estimating the breaks in the parameters and in the covariance of a system of long memory time series regressions is proposed. The limiting distributions as well as the consistency of the estimators are derived. Furthermore, a testing procedure to determine the unknown number of breaks is introduced which is based on iterative testing on the regression residuals. A Monte Carlo exercise shows the good finite sample properties of our novel approach, and empirical applications on inflation series of France and Germany and on benchmark government bonds of eight EMU countries illustrate the usefulness of the proposed procedures.

Keywords: Multivariate Long Memory; Fractional Cointegration; Multiple Structural Breaks; Hypothesis Testing; Inflation; Government Bonds (search for similar items in EconPapers)
JEL-codes: C12 C22 C58 G15 (search for similar items in EconPapers)
Pages: 72 pages
Date: 2025-02
References: Add references at CitEc
Citations:

Downloads: (external link)
https://diskussionspapiere.wiwi.uni-hannover.de/pdf_bib/dp-735.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:han:dpaper:dp-735

Access Statistics for this paper

More papers in Hannover Economic Papers (HEP) from Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät Contact information at EDIRC.
Bibliographic data for series maintained by Heidrich, Christian ().