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Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors

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  • Toshio Honda

Abstract

We consider nonparametric estimation of the conditional qth quantile for stationary time series. We deal with stationary time series with strong time dependence and heavy tails under the setting of random design. We estimate the conditional qth quantile by local linear regression and investigate the asymptotic properties. It is shown that the asymptotic properties are affected by both the time dependence and the tail index of the errors. The results of a small simulation study are also given.

Suggested Citation

  • Toshio Honda, 2010. "Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors," Global COE Hi-Stat Discussion Paper Series gd10-157, Institute of Economic Research, Hitotsubashi University.
  • Handle: RePEc:hst:ghsdps:gd10-157
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    References listed on IDEAS

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    2. Toshio Honda, 2010. "Nonparametric estimation of conditional medians for linear and related processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(6), pages 995-1021, December.
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