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Modelling time series with season-dependent autocorrelation structure

  • Yorghos Tripodis

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

  • Jeremy Penzer

    (Department of Statistics, LSE, London, UK)

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    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.

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    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 28 (2009)
    Issue (Month): 7 ()
    Pages: 559-574

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    Handle: RePEc:jof:jforec:v:28:y:2009:i:7:p:559-574
    DOI: 10.1002/for.1106
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