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A KALMAN FILTER APPROACH TO THE FORECASTING OF MONTHLY TIME SERIES AFFECTED BY Morris Festivals

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  • N. D. Morris
  • D. Pfeffermann

Abstract

. Many economic time series are affected by the moving dates of festivals. In this paper a moving festival effect is defined and incorporated into a dynamic linear model which specifies how the parameters of several unobservable components of a time series evolve stochastically in time. The merits of this approach in comparison to other approaches are discussed and demonstrated using empirical data of three selected time series.

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  • N. D. Morris & D. Pfeffermann, 1984. "A KALMAN FILTER APPROACH TO THE FORECASTING OF MONTHLY TIME SERIES AFFECTED BY Morris Festivals," Journal of Time Series Analysis, Wiley Blackwell, vol. 5(4), pages 255-268, July.
    • Handle: RePEc:bla:jtsera:v:5:y:1984:i:4:p:255-268
    DOI: 10.1111/j.1467-9892.1984.tb00391.x
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    Cited by:

    1. Jin-Lung Lin & Tian- Syh Liu, 2003. "Modeling lunar calendar effects in taiwan," Econometrics 0306005, University Library of Munich, Germany.
    2. Danny Pfeffermann, 2022. "Time series modelling of repeated survey data for estimation of finite population parameters," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1757-1777, October.

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