Seasonal unit roots and forecasts of two-digit European industrial production

Abstract

Monthly industrial production in important sectors of the German, French and UK economies are shown to exhibit very strong seasonality, such that typically 80% or more of the variation in monthly growth can be attributed to seasonal effects. Seasonal unit root test results imply that most of these series should be modelled using conventional first differences with the inclusion of monthly dummy variables, rather than as specifications involving other levels of differencing. However, when the post-sample forecast accuracy is compared for various models, annual difference specifications often produce the most accurate forecasts at horizons of up to a year. First difference models appear to be most accurate at short forecast horizons for series where seasonality is particularly marked. The study also examines the impact of updating coefficient estimates and model respecification within the forecast period.