Real-time forecasting of GDP based on a large factor model with monthly and quarterly data
Christian Schumacher () and
Jörg Breitung ()
No 2006,33, Discussion Paper Series 1: Economic Studies from Deutsche Bundesbank
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.
Keywords: monthly GDP; EM algorithm; principal components; factor models (search for similar items in EconPapers)
JEL-codes: E37 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets, nep-for and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:bubdp1:5097
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