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

  • Emmanuel Guerre

    (Crest)

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

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    Paper provided by Centre de Recherche en Economie et Statistique in its series Working Papers with number 2004-22.

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    Date of creation: 2004
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    Handle: RePEc:crs:wpaper:2004-22
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    1. repec:fth:inseep:9806 is not listed on IDEAS
    2. Emmanuel Guerre & Jules Maes, 1998. "Optimal Rate for Nonparametric Estimation in Deterministic Dynamical Systems," Working Papers 98-06, Centre de Recherche en Economie et Statistique.
    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. 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.
    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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