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GDP nowcasting with ragged-edge data: a semi-parametric modeling

  • Laurent Ferrara

    (Banque de France, Business Conditions and Macroeconomic Forecasting Directorate, Paris, France)

  • Dominique Guégan

    (Paris School of Economics, CES-MSE, Université Paris 1 Panthéon-Sorbonne, Banque de France, Paris, France)

  • Patrick Rakotomarolahy

    (CES-MSE, University of Paris 1 Panthéon-Sorbonne, Paris, France)

This paper formalizes the process of forecasting unbalanced monthly datasets in order to obtain robust nowcasts and forecasts of quarterly gross domestic product (GDP) growth rate through a semi-parametric modeling. This innovative approach lies in the use of non-parametric methods, based on nearest neighbors and on radial basis function approaches, to forecast the monthly variables involved in the parametric modeling of GDP using bridge equations. A real-time experience is carried out on euro area vintage data in order to anticipate, with an advance ranging from 6 to 1 months, the GDP flash estimate for the whole zone. Copyright © 2009 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.1159
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 29 (2010)
Issue (Month): 1-2 ()
Pages: 186-199

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Handle: RePEc:jof:jforec:v:29:y:2010:i:1-2:p:186-199
DOI: 10.1002/for.1159
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  9. Marcellino, Massimiliano & Schumacher, Christian, 2007. "Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP," Discussion Paper Series 1: Economic Studies 2007,34, Deutsche Bundesbank, Research Centre.
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  15. D'Agostino, Antonello & Domenico, Giannone & Surico, Paolo, 2006. "(Un)Predictability and Macroeconomic Stability," Research Technical Papers 5/RT/06, Central Bank of Ireland.
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  17. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
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