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Nonparametric forecasting with one-sided kernel adopting pseudo one-step ahead data

Author

Listed:
  • Jungwoo Kim

    (Yonsei University)

  • Joocheol Kim

    (Yonsei University)

Abstract

A new nonparametric forecasting using one-sided kernel is proposed via adopting pseudo one-step ahead data. Adopting pseudo one-step data is inspired from the difference between training error and test error, which motivates us to reduce test error minimization problem to training error minimization problem. The theoretical basis and the numerical justification of the new approach are presented.

Suggested Citation

  • Jungwoo Kim & Joocheol Kim, 2017. "Nonparametric forecasting with one-sided kernel adopting pseudo one-step ahead data," Working papers 2017rwp-102, Yonsei University, Yonsei Economics Research Institute.
    • Handle: RePEc:yon:wpaper:2017rwp-102
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    References listed on IDEAS

    as
    1. Azhong Ye & Rob J Hyndman & Zinai Li, 2006. "Local Linear Multivariate Regression with Variable Bandwidth in the Presence of Heteroscedasticity," Monash Econometrics and Business Statistics Working Papers 8/06, Monash University, Department of Econometrics and Business Statistics.
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    3. I. Gijbels & A. Pope & M. P. Wand, 1999. "Understanding exponential smoothing via kernel regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 39-50.
    4. Lejeune, Michel & Sarda, Pascal, 1992. "Smooth estimators of distribution and density functions," Computational Statistics & Data Analysis, Elsevier, vol. 14(4), pages 457-471, November.
    5. Hansen, Bruce E, 1995. "Regression with Nonstationary Volatility," Econometrica, Econometric Society, vol. 63(5), pages 1113-1132, September.
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    More about this item

    Keywords

    Nonparametric methods; Time series; One-sided kernel; Local regression; Exponential smoothing;
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