Performance of periodic time series models in forecasting |
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Authors: | Helmut Herwartz |
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Institution: | Institut für Statistik und ?konometrie, Humboldt Universit?t zu Berlin, Spandauer Str. 1, D-10178 Berlin, Germany (e-mail: helmut@wiwi.hu-berlin.de), DE
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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.
First version received: April 1996/final version received: January 1998 |
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Keywords: | : Forecasting periodic models seasonality unit roots |
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