Real-time forecasting of GDP based on a large factor model with monthly and quarterly data

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/10419/19662

Title:	Real-time forecasting of GDP based on a large factor model with monthly and quarterly data
Keywords:	E37 C53 ddc:330 monthly GDP EM algorithm principal components factor models Konjunkturprognose Prognoseverfahren Zeitreihenanalyse Faktorenanalyse Schätzung Theorie Deutschland
Issue Date:	16-Oct-2013
Description:	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.
URI:	http://koha.mediu.edu.my:8181/xmlui/handle/10419/19662
Other Identifiers:	http://hdl.handle.net/10419/19662 ppn:519430387 RePEc:zbw:bubdp1:5097
Appears in Collections:	EconStor

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