Authors: | Preuß, Philip Sen, Kemal Dette, Holger |
Title: | Detecting long-range dependence in non-stationary time series |
Language (ISO): | en |
Abstract: | An 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. |
Subject Headings: | spectral density sieve method locally stationary process integrated periodogram empirical spectral measure goodness-of- fit tests non-stationary processes long-memory |
URI: | http://hdl.handle.net/2003/31550 http://dx.doi.org/10.17877/DE290R-13182 |
Issue Date: | 2013-12-18 |
Appears in Collections: | Sonderforschungsbereich (SFB) 823 |
Files in This Item:
File | Description | Size | Format | |
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DP_5013_SFB823_Preuß_Sen_Dette.pdf | DNB | 508.97 kB | Adobe PDF | View/Open |
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