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Abstract Details
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Activity Number:
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411
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #300623 |
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Title:
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Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors
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Author(s):
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Toshio Honda*+
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Companies:
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Hitotsubashi University
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Address:
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Graduate School of Economics, Hitotsubashi Univ., Kunitachi, Tokyo, 186-8601, JAPAN
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Keywords:
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conditional quantile ;
random design ;
long-rande dependence ;
stable distribution ;
martingale CLT ;
linear process
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Abstract:
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We consider nonparametric estimation of the conditional q-th 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 q-th 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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Authors who are presenting talks have a * after their name.
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