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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

Gerhard Rünstler () and Marta Banbura ()

No 751, Working Paper Series from European Central Bank

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

Keywords: dynamic factor models; filter weights; forecasting (search for similar items in EconPapers)
Date: 2007-05
Note: 339116
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Journal Article: A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP (2011) Downloads
Journal Article: A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP (2011) Downloads
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