Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors
AbstractWe 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.
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Bibliographic InfoPaper provided by Institute of Economic Research, Hitotsubashi University in its series Global COE Hi-Stat Discussion Paper Series with number gd10-157.
Date of creation: Dec 2010
Date of revision:
conditional quantile; random design; check function; local linear regression; stable distribution; linear process; long-range dependence; martingale central limit theorem;
Other versions of this item:
- Toshio Honda, 2013. "Nonparametric quantile regression with heavy-tailed and strongly dependent errors," Annals of the Institute of Statistical Mathematics, Springer, vol. 65(1), pages 23-47, February.
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2005-13, Graduate School of Economics, Hitotsubashi University.
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