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Forecasting Dutch GDP using Large Scale Factor Models

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  • Ard H.J. den Reijer

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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Bibliographic Info

Paper provided by Netherlands Central Bank, Research Department in its series DNB Working Papers with number 028.

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Date of creation: Feb 2005
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Handle: RePEc:dnb:dnbwpp:028

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Web page: http://www.dnb.nl/en/
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Keywords: Factor models; Forecasting; Leading Indicators.;

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