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Performance of periodic time series models in forecasting

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  • Helmut Herwartz

    ()
    (Institut fØr Statistik und ãkonometrie, Humboldt UniversitÄt zu Berlin, Spandauer Str. 1, D-10178 Berlin, Germany)

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    Abstract

    The paper provides a comparison of alternative univariate time series models that are advocated for the analysis of seasonal data. Consumption and income series from (West-) Germany, United Kingdom, Japan and Sweden are investigated. The performance of competing models in forecasting is used to assess the adequacy of a specific model. To account for nonstationarity first and annual differences of the series are investigated. In addition, time series models assuming periodic integration are evaluated. To describe the stationary dynamics (standard) time invariant parametrizations are compared with periodic time series models conditioning the data generating process on the season. Periodic models improve the in-sample fit considerably but in most cases under study this model class involves a loss in ex-ante forecasting relative to nonperiodic models. Inference on unit-roots indicates that the nonstationary characteristics of consumption and income data may differ. For German and Swedish data forecasting exercises yield a unique recommendation of unit roots in consumption and income data which is an important (initial) result for multivariate analysis. Time series models assuming periodic integration are parsimonious to specify but often involve correlated one-step-ahead forecast errors.

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    Bibliographic Info

    Article provided by Springer in its journal Empirical Economics.

    Volume (Year): 24 (1999)
    Issue (Month): 2 ()
    Pages: 271-301

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    Handle: RePEc:spr:empeco:v:24:y:1999:i:2:p:271-301

    Note: received: April 1996/final version received: January 1998
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    Related research

    Keywords: Forecasting · periodic models · seasonality · unit roots;

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    Cited by:
    1. Franses, Philip Hans & van Dijk, Dick, 2005. "The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production," International Journal of Forecasting, Elsevier, vol. 21(1), pages 87-102.
    2. Franses, Ph.H.B.F. & Paap, R., 1999. "Forecasting with periodic autoregressive time series models," Econometric Institute Research Papers EI 9927-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Franses, Ph.H.B.F. & Paap, R., 2005. "Random-Coefficient periodic autoregression," Econometric Institute Research Papers EI 2005-34, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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