A New Ratio Statistic to Test Mean Change Point in Long Memory Time Series
In this paper, we propose a new ratio statistic to test mean change point in long memory time series. We derive the test statistic converges to a non-degenerate distribution under the null hypothesis and that it diverges to infinity under the alternative of a change-point with constant height. Furthermore, we estimate the long memory parameters and approximate the critical values of the statistic by the Sieve Bootstrap method. We show the size and power properties of our test through numerical simulations and the performance is inspiring. Finally, we illustrate the effectiveness of this method through a set of actual data.