A sieve bootstrap test for stationarity
AbstractThis paper proposes a bootstrap test for testing the null hypothesis that a time series is stationary against the alternative hypothesis that it is integrated of order one. Our approach makes use of a sieve bootstrap scheme based on residual resampling from autoregressive approximations the order of which increases with the sample size at a suitable rate. The first-order asymptotic correctness of the sieve bootstrap for testing the stationarity hypothesis is established for a subclass of linear processes. The small-sample properties of the method are also investigated by means of Monte Carlo experiments.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 62 (2003)
Issue (Month): 3 (April)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Psaradakis, Zacharias, 2006. "Blockwise bootstrap testing for stationarity," Statistics & Probability Letters, Elsevier, vol. 76(6), pages 562-570, March.
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