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Properties of the nonparametric autoregressive bootstrap

Author

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  • J. FRANKE
  • J.‐P. KREISS
  • E. MAMMEN
  • M. H. NEUMANN

Abstract

For nonparametric autoregression, we investigate a model based bootstrap procedure (`autoregressive bootstrap') that mimics the complete dependence structure of the original time series. We give consistency results for uniform bootstrap confidence bands of the autoregression function based on kernel estimates of the autoregression function. This result is achieved by global strong approximations of the kernel estimates for the resample and for the original sample. Furthermore, it is obtained that the autoregressive bootstrap also yields asymptotically correct approximations for distributions of parametric statistics, for which regression‐type bootstrap‐techniques like the wild bootstrap do not work. For this purpose, we prove geometric ergodicity and absolute regularity of the nonparametric autoregressive bootstrap process. We propose some particular estimators of the autoregression function and of the density of the innovations such that the mixing coefficients of the autoregressive bootstrap process can be bounded uniformly by some exponentially decaying sequence. This is achieved by using well‐established coupling techniques. Moreover, by using some `decoupling' argument, we show that the stationary density of the bootstrap process converges to that of the original process. The paper may serve as a template for proving similar consistency results for other bootstrap techniques such as the Markov bootstrap.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jtsera:v:23:y:2002:i:5:p:555-585
    DOI: 10.1111/1467-9892.00278
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    Cited by:

    1. Chang, Christopher C. & Politis, Dimitris N., 2011. "Bootstrap with larger resample size for root-n consistent density estimation with time series data," Statistics & Probability Letters, Elsevier, vol. 81(6), pages 652-661, June.
    2. Wolfgang Hardle & Torsten Kleinow & Alexander Korostelev & Camille Logeay & Eckhard Platen, 2008. "Semiparametric diffusion estimation and application to a stock market index," Quantitative Finance, Taylor & Francis Journals, vol. 8(1), pages 81-92.
    3. Wang, Bin & Zheng, Xu, 2022. "Testing for the presence of jump components in jump diffusion models," Journal of Econometrics, Elsevier, vol. 230(2), pages 483-509.
    4. Giordano, F. & Parrella, M.L., 2008. "Neural networks for bandwidth selection in local linear regression of time series," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2435-2450, January.
    5. Maria Mohr & Leonie Selk, 2020. "Estimating change points in nonparametric time series regression models," Statistical Papers, Springer, vol. 61(4), pages 1437-1463, August.
    6. Franke, Jurgen & Neumann, Michael H. & Stockis, Jean-Pierre, 2004. "Bootstrapping nonparametric estimators of the volatility function," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 189-218.
    7. Wolfgang Härdle & Joel Horowitz & Jens‐Peter Kreiss, 2003. "Bootstrap Methods for Time Series," International Statistical Review, International Statistical Institute, vol. 71(2), pages 435-459, August.
    8. 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.

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