Forecasting Dutch GDP using Large Scale Factor Models

Abstract

This paper applies large scale factor models to Dutch quarterly data in order to generate forecasts of GDP growth rates for an horizon up to 8 quarters ahead. The data set consists of the series underlying the cen- tral bank´s macroeconomic structural model for the Netherlands sup- plemented with leading indicator variables. In a pseudo out-of-sample forecasting context, we select optimal models in the time dimension and the optimal size of the ordered data set in the cross-sectional dimension. The main empirical ?ndings of this paper are that the cross-sectional opti- mization substantially improves the forecasting performance of the factor models. However, only the dynamic factor model systematically outper- forms and encompasses the autoregressive benchmark model with an op- timal subset of the data of around 110 series. The forecasting gains in terms of mean squared errors range from 10% to 30% for forecast horizons up to 6 quarters ahead.

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