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Comparing Seasonal Forecasts of Industrial Production

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  • Pedro M.D.C.B. Gouveia
  • Denise R. Osborn
  • Paulo M.M. Rodrigues

Abstract

Forecast combination methodologies exploit complementary relations between different types of econometric models and often deliver more accurate forecasts than the individual models on which they are based. This paper examines forecasts of seasonally unadjusted monthly industrial production data for 17 countries and the Euro Area, comparing individual model forecasts and forecast combination methods in order to examine whether the latter are able to take advantage of the properties of different seasonal specifications. In addition to linear models (with deterministic seasonality and with nonstationary stochastic seasonality), more complex models that capture nonlinearity or seasonally varying coefficients (periodic models) are also examined. Although parsimonous periodic models perform well for some countries, forecast combinations provide the best overall performance at short horizons, implying that utilizing the characteristics captured by different models can contribute to improved forecast accuracy.

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File URL: http://www.socialsciences.manchester.ac.uk/medialibrary/cgbcr/discussionpapers/dpcgbcr102.pdf
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Paper provided by Economics, The Univeristy of Manchester in its series Centre for Growth and Business Cycle Research Discussion Paper Series with number 102.

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Length: 24 pages
Date of creation: 2008
Date of revision:
Handle: RePEc:man:cgbcrp:102

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