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Hybrid wild bootstrap for nonparametric trend estimation in locally stationary time series

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  • Krampe, J.
  • Kreiss, J.-P.
  • Paparoditis, E.

Abstract

Based on consistency and asymptotic normality of a nonparametric kernel trend estimation in the context of locally stationary processes, validity of a hybrid wild bootstrap approach for estimating the distribution of the nonparametric estimator is established. Simulations are presented.

Suggested Citation

  • Krampe, J. & Kreiss, J.-P. & Paparoditis, E., 2015. "Hybrid wild bootstrap for nonparametric trend estimation in locally stationary time series," Statistics & Probability Letters, Elsevier, vol. 101(C), pages 54-63.
    • Handle: RePEc:eee:stapro:v:101:y:2015:i:c:p:54-63
    DOI: 10.1016/j.spl.2015.03.003
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    References listed on IDEAS

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    1. Rainer Von Sachs & Brenda Macgibbon, 2000. "Non‐parametric Curve Estimation by Wavelet Thresholding with Locally Stationary Errors," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(3), pages 475-499, September.
    2. Jens-Peter Kreiss & Efstathios Paparoditis, 2015. "Bootstrapping locally stationary processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(1), pages 267-290, January.
    3. Marios Sergides & Efstathios Paparoditis, 2008. "Bootstrapping the Local Periodogram of Locally Stationary Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(2), pages 264-299, March.
    4. Dahlhaus, R., 1996. "On the Kullback-Leibler information divergence of locally stationary processes," Stochastic Processes and their Applications, Elsevier, vol. 62(1), pages 139-168, March.
    5. Michael Vogt, 2012. "Nonparametric regression for locally stationary time series," CeMMAP working papers CWP22/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Paparoditis, Efstathios, 2010. "Validating Stationarity Assumptions in Time Series Analysis by Rolling Local Periodograms," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 839-851.
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