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A sieve bootstrap test for stationarity

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  • Psaradakis, Zacharias

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.

Suggested Citation

  • Psaradakis, Zacharias, 2003. "A sieve bootstrap test for stationarity," Statistics & Probability Letters, Elsevier, vol. 62(3), pages 263-274, April.
  • Handle: RePEc:eee:stapro:v:62:y:2003:i:3:p:263-274
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    References listed on IDEAS

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    1. Lee, Junsoo, 1996. "On the power of stationarity tests using optimal bandwidth estimates," Economics Letters, Elsevier, vol. 51(2), pages 131-137, May.
    2. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    3. Park, Joon Y., 2002. "An Invariance Principle For Sieve Bootstrap In Time Series," Econometric Theory, Cambridge University Press, vol. 18(02), pages 469-490, April.
    4. Edwin Choi & Peter Hall, 2000. "Bootstrap confidence regions computed from autoregressions of arbitrary order," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 461-477.
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    Cited by:

    1. Fuertes, Ana-Maria, 2008. "Sieve bootstrap t-tests on long-run average parameters," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3354-3370, March.
    2. Psaradakis, Zacharias, 2006. "Blockwise bootstrap testing for stationarity," Statistics & Probability Letters, Elsevier, vol. 76(6), pages 562-570, March.

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