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

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  • Guerre

    (LSTA)

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    Abstract

    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://128.118.178.162/eps/em/papers/0411/0411007.pdf
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    Bibliographic Info

    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
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    Web page: http://128.118.178.162

    Related research

    Keywords: Nonparametric regression estimation; Recurrent time series; Design-adaptation; Optimalrandom estimation rate.;

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. repec:fth:inseep:9806 is not listed on IDEAS
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    Cited by:
    1. Qiying Wang & Peter C.B. Phillips, 2006. "Asymptotic Theory for Local Time Density Estimation and Nonparametric Cointegrating Regression," Cowles Foundation Discussion Papers 1594, Cowles Foundation for Research in Economics, Yale University.
    2. Peter C.B.Phillips & Ioannis Kasparis, 2009. "Dynamic Misspecification in Nonparametric Cointegrating Regression," Working Papers CoFie-01-2009, Sim Kee Boon Institute for Financial Economics.
    3. Peter C.B. Phillips & Donggyu Sul, 2005. "Economic Transition and Growth," Cowles Foundation Discussion Papers 1514, Cowles Foundation for Research in Economics, Yale University.
    4. Peter C. B. Phillips & Donggyu Sul, 2007. "Transition Modeling and Econometric Convergence Tests," Econometrica, Econometric Society, vol. 75(6), pages 1771-1855, November.
    5. Delattre, Sylvain & Gaïffas, Stéphane, 2011. "Nonparametric regression with martingale increment errors," Stochastic Processes and their Applications, Elsevier, vol. 121(12), pages 2899-2924.
    6. Peter C.B. Phillips, 2008. "Local Limit Theory and Spurious Nonparametric Regression," Cowles Foundation Discussion Papers 1654, Cowles Foundation for Research in Economics, Yale University.

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