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Non‐parametric vs parametric forecasting in time series: A computational point of view

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  • M. Carbon
  • M. Delecroix

Abstract

The finite sample behaviour of non‐parametric predictors is considered for time series. Among other results, it is shown by simulation arguments that such predictors compare favourably with predictors based on parametric models in the spirit of the usual Box‐Jenkins approach.

Suggested Citation

  • M. Carbon & M. Delecroix, 1993. "Non‐parametric vs parametric forecasting in time series: A computational point of view," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 9(3), pages 215-229, September.
    • Handle: RePEc:wly:apsmda:v:9:y:1993:i:3:p:215-229
    DOI: 10.1002/asm.3150090303
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    Cited by:

    1. 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.
    2. Thomakos, Dimitrios D. & Guerard, John Jr., 2004. "Naive, ARIMA, nonparametric, transfer function and VAR models: A comparison of forecasting performance," International Journal of Forecasting, Elsevier, vol. 20(1), pages 53-67.
    3. Norberto Rodríguez & Patricia Siado, 2003. "Un Pronóstico No Paramétrico De La Inflación Colombiana," Borradores de Economia 3691, Banco de la Republica.
    4. Norberto Rodríguez N. & Patricia Siado C., 2003. "Un Pronóstico no Paramétrico de la Inflación Colombiana," Borradores de Economia 248, Banco de la Republica de Colombia.

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