A Look into the Factor Model Black Box: Publication Lags and the Role of Hard and Soft Data in Forecasting GDP
36 Pages Posted: 31 May 2007
Date Written: May 2007
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.
Keywords: dynamic factor models, forecasting, filter weights
JEL Classification: E37, C53
Suggested Citation: Suggested Citation