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Nonparametric Estimation of Conditional Medians for Linear and Related Processes

Author

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  • Honda, Toshio
  • 本田, 敏雄

Abstract

We 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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Suggested Citation

  • Honda, Toshio & 本田, 敏雄, 2007. "Nonparametric Estimation of Conditional Medians for Linear and Related Processes," Discussion Papers 2005-04, Graduate School of Economics, Hitotsubashi University.
    • Handle: RePEc:hit:econdp:2005-04
    Note: September 2005; October 2007 (revised)
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    File URL: https://hit-u.repo.nii.ac.jp/record/2052366/files/070econDP05-04.pdf
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    Cited by:

    1. is not listed on IDEAS
    2. Toshio Honda, 2013. "Nonparametric quantile regression with heavy-tailed and strongly dependent errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(1), pages 23-47, February.
    3. Honda, Toshio, 2013. "Nonparametric LAD cointegrating regression," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 150-162.

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