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Semiparametrically Efficient R-Estimation for Dynamic Location-Scale Models

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  • Marc Hallin
  • Davide La Vecchia

Abstract

We define rank-based estimators (R-estimators) for semiparametric time series models in whichthe conditional location and scale depend on a Euclidean parameter, while the innovation density isan infinite-dimensional nuisance. Applications include linear and nonlinear models, featuring eitherhomo- or heteroskedasticity (e.g. AR-ARCH and discretely observed diffusions with jumps). We showhow to construct easy-to-implement R-estimators, which achieve semiparametric efficiency at somepredetermined reference density while preserving root-n consistency, irrespective of the actual density.Numerical examples illustrate the good performances of the proposed estimators. An empirical analysisof the log-return and log-transformed two-scale realized volatility concludes the paper.

Suggested Citation

  • Marc Hallin & Davide La Vecchia, 2014. "Semiparametrically Efficient R-Estimation for Dynamic Location-Scale Models," Working Papers ECARES ECARES 2014-45, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:eca:wpaper:2013/176572
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    References listed on IDEAS

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    Cited by:

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    Keywords

    conditional heteroskedasticity; distribution-freeness; forecasting; Lévy processes; one-step R-Estimators;
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