A sieve bootstrap test for stationarity

Zacharias Psaradakis

Statistics & Probability Letters, 2003, vol. 62, issue 3, 263-274

Abstract: This 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.

Keywords: Autoregressive; approximation; Linear; process; Sieve; bootstrap; Stationarity; Time; series (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (6)

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