Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/19662
Authors: 
Schumacher, Christian
Breitung, Jörg
Year of Publication: 
2006
Series/Report no.: 
Discussion paper Series 1 / Volkswirtschaftliches Forschungszentrum der Deutschen Bundesbank 2006,33
Abstract: 
This paper discusses a factor model for estimating monthly GDP using a large number of monthly and quarterly time series in real-time. To take into account the different periodicities of the data and missing observations at the end of the sample, the factors are estimated by applying an EM algorithm combined with a principal components estimator. We discuss the in-sample properties of the estimator in real-time environments and methods for out-of-sample forecasting. As an empirical application, we estimate monthly German GDP in real-time, discuss the nowcast and forecast accuracy of the model and the role of revisions. Furthermore, we assess the contribution of timely monthly data to the forecast performance.
Subjects: 
monthly GDP
EM algorithm
principal components
factor models
JEL: 
E37
C53
Document Type: 
Working Paper

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