Nonparametric Estimation of Conditional Medians for Linear and Related Processes
AbstractWe consider nonparametric estimation of conditional medians for time series data. The time series data are generated from two mutually independent linear processes. The linear processes may show long-range dependence.The estimator of the conditional medians is based on minimizing the locally weighted sum of absolute deviations for local linear regression. We present the asymptotic distribution of the estimator. The rate of convergence is independent of regressors in our setting. The result of a simulation study is also given.
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Bibliographic InfoPaper provided by Graduate School of Economics, Hitotsubashi University in its series Discussion Papers with number 2005-04.
Length: 31,  p.
Date of creation: Oct 2007
Date of revision:
Local linear estimator; least absolute deviation regression; conditional quantiles; linear processes; short-range dependence; long-range dependence; random design; martingale CLT; simulation study;
Other versions of this item:
- Toshio Honda, 2010. "Nonparametric estimation of conditional medians for linear and related processes," Annals of the Institute of Statistical Mathematics, Springer, vol. 62(6), pages 995-1021, December.
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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2005-13, Graduate School of Economics, Hitotsubashi University.
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Global COE Hi-Stat Discussion Paper Series
gd10-157, Institute of Economic Research, Hitotsubashi University.
- 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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