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The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production

Listed author(s):
  • Franses, Ph.H.B.F.
  • van Dijk, D.J.C.

Seasonality often accounts for the major part of quarterly or monthly movements in detrended macro-economic time series. In addition, business cycle nonlinearity is a prominent feature of many such series too. A forecaster can nowadays consider a wide variety of time series models which describe seasonal variation and regime-switching behaviour. In this paper we examine the forecasting performance of various models for seasonality and nonlinearity using quarterly industrial production series for 17 OECD countries. We find that forecasting performance varies widely across series, across forecast horizons and across seasons. However, in general, linear models with fairly simple descriptions of seasonality outperform at short forecast horizons, whereas nonlinear models with more elaborate seasonal components dominate at longer horizons.

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File URL: http://repub.eur.nl/pub/1678/feweco20010426095757.pdf
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Paper provided by Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute in its series Econometric Institute Research Papers with number EI 2001-14.

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Date of creation: 26 Apr 2001
Handle: RePEc:ems:eureir:1678
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