Title: Testing for multiple structural breaks in multivariate long memory time series
Authors: Philipp Sibbertsen - University of Hannover (Germany) [presenting]
Vivien Less - Leibniz Universitaet Hannover (Germany)
Abstract: Estimation and testing of multiple breaks that occur at unknown dates are considered 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 the consistency of the estimators are 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.