Testing for multiple structural breaks in multivariate long memory time series - Philipp Sibbertsen; Kai Wenger; Simon Wingert (Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, 2020)
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: C12; C22; C58; G15; 330; Multivariate Long Memory; Multiple Structural Breaks; Hypothesis Testing