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Kernel regression smoothing of time series

Author

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  • HÄRDLE, Wolfgang
  • VIEU, Philippe

Abstract

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Suggested Citation

  • HÄRDLE, Wolfgang & VIEU, Philippe, 1992. "Kernel regression smoothing of time series," LIDAM Reprints CORE 981, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:981
    DOI: 10.1111/j.1467-9892.1992.tb00103.x
    Note: In : Journal of Time Series Anlysis, 13(3), 209-232, 1992
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    Cited by:

    1. David I. Harvey & Stephen J. Leybourne & Yang Zu, 2024. "Tests for equal forecast accuracy under heteroskedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 850-869, August.
    2. Jozef Barunik & Lukas Vacha, 2023. "The Dynamic Persistence of Economic Shocks," Papers 2306.01511, arXiv.org.
    3. Ayse Yilmaz & Ufuk Yolcu, 2022. "Dendritic neuron model neural network trained by modified particle swarm optimization for time‐series forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 793-809, July.
    4. Kim, Namhyun & W. Saart, Patrick, 2021. "Estimation in partially linear semiparametric models with parametric and/or nonparametric endogeneity," Cardiff Economics Working Papers E2021/9, Cardiff University, Cardiff Business School, Economics Section.
    5. Federico M Bandi & Valentina Corradi & Daniel Wilhelm, 2016. "Possibly Nonstationary Cross-Validation," CeMMAP working papers 11/16, Institute for Fiscal Studies.
    6. Francesco Audrino, 2005. "Local Likelihood for non‐parametric ARCH(1) models," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(2), pages 251-278, March.
    7. Zhenyu Jiang & Nengxiang Ling & Zudi Lu & Dag Tj⊘stheim & Qiang Zhang, 2020. "On bandwidth choice for spatial data density estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 817-840, July.

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