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Design-Adaptive Pointwise Nonparametric Regression Estimation For Recurrent Markov Time Series

  • Guerre

    (LSTA)

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    A general framework is proposed for (auto)regression nonparametric estimation of recurrent time series in a class of Hilbert Markov processes with a Lipschitz conditional mean. This includes various nonstationarities by relaxing usual dependence assumptions as mixing or ergodicity, which are replaced with recurrence. The cornerstone of design-adaptation is a data-driven bandwidth choice based on an empirical bias variance tradeoff, giving rise to a random consistency rate for a uniform kernel estimator. The estimator converges with this random rate, which is the optimal minimax random rate over the considered class of recurrent time series. Extensions to general kernel estimators are investigated. For weak dependent time-series, the order of the random rate coincides with the deterministic minimax rate previously derived. New deterministic estimation rates are obtained for modified Box-Cox transformations of Random Walks.

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    File URL: http://econwpa.repec.org/eps/em/papers/0411/0411007.pdf
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    Paper provided by EconWPA in its series Econometrics with number 0411007.

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    Length: 35 pages
    Date of creation: 10 Nov 2004
    Date of revision:
    Handle: RePEc:wpa:wuwpem:0411007
    Note: Type of Document - pdf; pages: 35
    Contact details of provider: Web page: http://econwpa.repec.org

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    1. repec:fth:inseep:9806 is not listed on IDEAS
    2. Peter C.B. Phillips & Joon Y. Park, 1998. "Nonstationary Density Estimation and Kernel Autoregression," Cowles Foundation Discussion Papers 1181, Cowles Foundation for Research in Economics, Yale University.
    3. Yakowitz, Sidney & Györfi, László & Kieffer, John & Morvai, Gusztáv, 1999. "Strongly Consistent Nonparametric Forecasting and Regression for Stationary Ergodic Sequences," Journal of Multivariate Analysis, Elsevier, vol. 71(1), pages 24-41, October.
    4. E. Guerre & J. Maës, 1998. "Optimal Rate for Nonparametric Estimation in Deterministic Dynamical Systems," Statistical Inference for Stochastic Processes, Springer, vol. 1(2), pages 157-173, May.
    5. Yakowitz, Sid, 1993. "Nearest neighbor regression estimation for null-recurrent Markov time series," Stochastic Processes and their Applications, Elsevier, vol. 48(2), pages 311-318, November.
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