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On nonparametric regression for bivariate circular long-memory time series

Author

Listed:
  • Jan Beran

    (University of Konstanz)

  • Britta Steffens

    (University of Konstanz)

  • Sucharita Ghosh

    (Swiss Federal Research Institute WSL)

Abstract

We consider nonparametric regression for bivariate circular time series with long-range dependence. Asymptotic results for circular Nadaraya–Watson estimators are derived. Due to long-range dependence, a range of asymptotically optimal bandwidths can be found where the asymptotic rate of convergence does not depend on the bandwidth. The result can be used for obtaining simple confidence bands for the regression function. The method is illustrated by an application to wind direction data.

Suggested Citation

  • Jan Beran & Britta Steffens & Sucharita Ghosh, 2022. "On nonparametric regression for bivariate circular long-memory time series," Statistical Papers, Springer, vol. 63(1), pages 29-52, February.
    • Handle: RePEc:spr:stpapr:v:63:y:2022:i:1:d:10.1007_s00362-021-01228-1
    DOI: 10.1007/s00362-021-01228-1
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    References listed on IDEAS

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