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.
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