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Sieve Extremum Estimates for Weakly Dependent Data

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  • Xiaohong Chen
  • Xiaotong Shen
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    Abstract

    Many non/semiparametric time series estimates may be regarded as different forms of sieve extremum estimates. For stationary absolute regular mixing observations, the authors obtain convergence rates of sieve extremurn estimates and root-n asymptotic normality of 'plug-in' sieve extremum estimates of smooth functionals. As applications to time series models, they give convergence rates for nonparametric ARX(p,q) regression via neural networks, splines, wavelets; root-n asymptotic normality for partial linear additive AR(p) models, and monotone transformation AR(1) models.

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    Bibliographic Info

    Article provided by Econometric Society in its journal Econometrica.

    Volume (Year): 66 (1998)
    Issue (Month): 2 (March)
    Pages: 289-314

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    Handle: RePEc:ecm:emetrp:v:66:y:1998:i:2:p:289-314

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