Performance of Periodic Time Series Models in Forecasting

Posted: 22 Jul 1999

See all articles by Helmut Herwartz

Helmut Herwartz

University of Kiel - Institute of Statistics and Econometrics

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.

JEL Classification: C22, C52, C53

Suggested Citation

Herwartz, Helmut, Performance of Periodic Time Series Models in Forecasting. Available at SSRN: https://ssrn.com/abstract=164834

Helmut Herwartz (Contact Author)

University of Kiel - Institute of Statistics and Econometrics ( email )

Olshausensrabe 40-60
D-24118 Kiel
Germany

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