Arbeitspapier

Testing for multiple structural breaks in multivariate long memory time series

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This paper considers estimation and testing of multiple breaks that occur at unknown dates in multivariate long-memory time series. We propose a likelihood ratio based approach for estimating breaks in the mean and the covariance of a system of long-memory time series. The limiting distribution of these estimates as well as consistency of the estimators is derived. A testing procedure to determine the unknown number of break points is given based on iterative testing on the regression residuals. A Monte Carlo exercise shows the finite sample performance of our method. An empirical application to inflation series illustrates the usefulness of our procedures.

Sprache
Englisch

Erschienen in
Series: Hannover Economic Papers (HEP) ; No. 676

Klassifikation
Wirtschaft
Hypothesis Testing: General
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Financial Econometrics
International Financial Markets
Thema
Multivariate Long Memory
Multiple Structural Breaks
Hypothesis Testing

Ereignis
Geistige Schöpfung
(wer)
Sibbertsen, Philipp
Wenger, Kai
Wingert, Simon
Ereignis
Veröffentlichung
(wer)
Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät
(wo)
Hannover
(wann)
2020

Handle
Letzte Aktualisierung
10.03.2025, 11:45 MEZ

Datenpartner

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Objekttyp

  • Arbeitspapier

Beteiligte

  • Sibbertsen, Philipp
  • Wenger, Kai
  • Wingert, Simon
  • Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät

Entstanden

  • 2020

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