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A look into the factor model black box: publication lags and the role of hard and soft data in forecasting GDP

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  • Banbura, Marta
  • Rünstler, Gerhard

Abstract

We derive forecast weights and uncertainty measures for assessing the role of individual series in a dynamic factor model (DFM) to forecast euro area GDP from monthly indicators. The use of the Kalman filter allows us to deal with publication lags when calculating the above measures. We find that surveys and financial data contain important information beyond the monthly real activity measures for the GDP forecasts. However, this is discovered only, if their more timely publication is properly taken into account. Differences in publication lags play a very important role and should be considered in forecast evaluation. JEL Classification: E37, C53

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Bibliographic Info

Paper provided by European Central Bank in its series Working Paper Series with number 0751.

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Date of creation: May 2007
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Handle: RePEc:ecb:ecbwps:20070751

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Keywords: Dynamic Factor Models; filter weights; forecasting;

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