Abstract
In this paper it is investigated whether multivariate time series models can improve the forecasting performance of Holt-Winters models and univariate ‘naive’ ARIMA-models, which are easier to construct, but do not take into account lead-lag relationships. This research deals with the Dutch truck market performance in the light of overall economic developments. A 5-variate model is built, containing two truck sales series and three economic indicators. It is found that a substantial reduction in residual variance can be found by using a multivariate model. In the case of one truck series (rigids) this leads to uniformly better forecasts. For the other output series (artics), no improvement in forecasting accuracy was found. Holt-Winters models do not seem to improve any of the sophisticated models.
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