Performance of Periodic Time Series Models in Forecasting
نویسنده
چکیده
Periodic time series models have become an appealing tool for the analysis of time series showing distinct seasonal patterns. Since these models condition the data{generating mechanism of a given time series on the season they are able to cope with periodic generalisations of common economic models introducing seasonal preferences, seasonal technologies etc. The paper examines for some macroeconomic time series the forecasting performance of univa-riate periodic models in comparison to some non{periodic (standard) speciications. Time series models assuming seasonal variation to be deterministic are employed as well as models involving stochastic seasonality and so{called periodic integration. The performance comparison concerns out{of{sample and in{sample forecasting precision. In addition, the forecasting accuracy for single seasons is investigated brieey. The analysed time series are consumption and income series from (Western{) Germany, United Kingdom, Japan and Sweden. The research of this paper was carried out within the Sonderforschungsbereich 373 at Humboldt University Berlin and was printed using funds made available by the Deutsche Forschungsgemeinschaft.
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