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Properties of the neural network sieve bootstrap

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  • F. Giordano
  • M. La Rocca
  • C. Perna

Abstract

In this paper, a sieve bootstrap scheme, the neural network sieve bootstrap, for nonlinear time series is proposed. The approach, which is nonparametric in its spirit, retains the conceptual simplicity of a classical residual bootstrap, and it has some advantages with respect to the blockwise schemes and kernel bootstrap techniques. The resampling scheme from the residuals of the feedforward neural networks is shown to be asymptotically justified. A Monte Carlo simulation study shows that the procedure performs similar to the autoregressive (AR)-sieve bootstrap for linear processes, while it outperforms the AR-sieve bootstrap, the moving block bootstrap and kernel bootstrap for nonlinear processes, both in terms of bias and variability.

Suggested Citation

  • F. Giordano & M. La Rocca & C. Perna, 2011. "Properties of the neural network sieve bootstrap," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(3), pages 803-817.
    • Handle: RePEc:taf:gnstxx:v:23:y:2011:i:3:p:803-817
    DOI: 10.1080/10485252.2011.561344
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    References listed on IDEAS

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    1. D. S. Poskitt, 2008. "Properties of the Sieve Bootstrap for Fractionally Integrated and Non‐Invertible Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(2), pages 224-250, March.
    2. Yoosoon Chang & Joon Y. Park, 2003. "A Sieve Bootstrap For The Test Of A Unit Root," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(4), pages 379-400, July.
    3. Buhlmann, Peter & Kunsch, Hans R., 1999. "Block length selection in the bootstrap for time series," Computational Statistics & Data Analysis, Elsevier, vol. 31(3), pages 295-310, September.
    4. J. Franke & J.‐P. Kreiss & E. Mammen & M. H. Neumann, 2002. "Properties of the nonparametric autoregressive bootstrap," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(5), pages 555-585, September.
    5. Psaradakis, Zacharias, 2003. "A sieve bootstrap test for stationarity," Statistics & Probability Letters, Elsevier, vol. 62(3), pages 263-274, April.
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