Preuß, PhilipSen, KemalDette, Holger2013-12-182013-12-182013-12-18http://hdl.handle.net/2003/3155010.17877/DE290R-13182An important problem in time series analysis is the discrimination between non-stationarity and longrange dependence. Most of the literature considers the problem of testing specificc parametric hypotheses of non-stationarity (such as a change in the mean) against long-range dependent stationary alternatives. In this paper we suggest a simple nonparametric approach, which can be used to test the null-hypothesis of a general non-stationary short-memory against the alternative of a non-stationary long-memory process. This test is working in the spectral domain and uses a sieve of approximating tvFARIMA models to estimate the time varying long-range dependence parameter nonparametrically. We prove uniform consistency of this estimate and asymptotic normality of an averaged version. These results yield a simple test (based on the quantiles of the standard normal distribution), and it is demonstrated in a simulation study that - despite of its nonparametric nature - the new test outperforms the currently available methods, which are constructed to discriminate between speci fic parametric hypotheses of non-stationarity short- and stationarity long-range dependence.enDiscussion Paper / SFB 823;50/2013spectral densitysieve methodlocally stationary processintegrated periodogramempirical spectral measuregoodness-of- fit testsnon-stationary processeslong-memory310330620working paper