摘要: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.
关键词:Factor models, Forecasting, Leading Indicators. JEL Code: C43, C51, E32