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Modelling time series with season-dependent autocorrelation structure

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  • Yorghos Tripodis

    (Department of Biostatistics, Boston University, Boston, Massachusetts, USA)

  • Jeremy Penzer

    (Department of Statistics, LSE, London, UK)

Abstract

Time series with season-dependent autocorrelation structure are commonly modelled using periodic autoregressive moving average (PARMA) processes. In most applications, the moving average terms are excluded for ease of estimation. We propose a new class of periodic unobserved component models (PUCM). Parameter estimates for PUCM are readily interpreted; the estimated coefficients correspond to variances of the measurement noise and of the error terms in unobserved components. We show that PUCM have correlation structure equivalent to that of a periodic integrated moving average (PIMA) process. Results from practical applications indicate that our models provide a natural framework for series with periodic autocorrelation structure both in terms of interpretability and forecasting accuracy. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Yorghos Tripodis & Jeremy Penzer, 2009. "Modelling time series with season-dependent autocorrelation structure," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 559-574.
    • Handle: RePEc:jof:jforec:v:28:y:2009:i:7:p:559-574
    DOI: 10.1002/for.1106
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    References listed on IDEAS

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