Testing for Multiple Structural Breaks in Multivariate Long Memory Time Series

Philipp Sibbertsen, Kai Wenger and Simon Wingert

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

Abstract: 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.

Keywords: Multivariate Long Memory; Multiple Structural Breaks; Hypothesis Testing (search for similar items in EconPapers)
JEL-codes: C12 C22 C58 G15 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2020-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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