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. Copyright The Institute of Statistical Mathematics, Tokyo 2013
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Bibliographic InfoArticle provided by Springer in its journal Annals of the Institute of Statistical Mathematics.
Volume (Year): 65 (2013)
Issue (Month): 1 (February)
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Web page: http://www.springerlink.com/link.asp?id=102845
Other versions of this item:
- 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.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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Cambridge University Press, number 9780521845731, 9.
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2005-13, Graduate School of Economics, Hitotsubashi University.
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