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

Laurent Ferrara, Dominique Guégan and Patrick Rakotomarolahy
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Dominique Guégan: Paris School of Economics, CES-MSE, Université Paris 1 Panthéon-Sorbonne, Banque de France, Paris, France, Postal: 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, Postal: CES-MSE, University of Paris 1 Panthéon-Sorbonne, Paris, France

Journal of Forecasting, 2010, vol. 29, issue 1-2, 186-199

Abstract: 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.

Date: 2010
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Citations: View citations in EconPapers (18)

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Related works:
Working Paper: GDP nowcasting with ragged-edge data: a semi-parametric modeling (2010)
Working Paper: GDP nowcasting with ragged-edge data: a semi-parametric modeling (2010)
Working Paper: GDP nowcasting with ragged-edge data: A semi-parametric modelling (2009)
Working Paper: GDP nowcasting with ragged-edge data: A semi-parametric modelling (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:29:y:2010:i:1-2:p:186-199

DOI: 10.1002/for.1159

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