Ratio Testing for Changes in the Long Memory Indexes in Presence of Breaks in Mean

In this paper we consider the problem of detecting for breaks in the long memory indexes in presence of breaks in mean. The limiting distribution is derived under the null hypothesis and the alternative hypothesis, the ratio tests also diverge to infinity as the sample size grows. These results show that the reject rate seriously depends on the magnitude of change points. Finally, the Monte Carlo study presents that our test has reasonably good size and power properties.