GDP nowcasting with ragged-edge data : A semi-parametric modelling

Abstract : This papier formalizes the process of forecasting unbalanced monthly data sets in order to obtain robust nowcasts and forecasts of quarterly GDP growth rate through a semi-parametric modelling. This innovative approach lies on the use on non-parametric methods, based on nearest neighbors and on radial basis function approaches, ti forecast the monthly variables involved in the parametric modelling 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 six to one months, the GDP flash estimate for the whole zone.
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https://halshs.archives-ouvertes.fr/halshs-00344839
Contributor : Lucie Label <>
Submitted on : Friday, December 5, 2008 - 6:53:41 PM
Last modification on : Thursday, October 4, 2018 - 6:28:03 PM
Long-term archiving on : Monday, June 7, 2010 - 11:49:25 PM

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  • HAL Id : halshs-00344839, version 1

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Laurent Ferrara, Dominique Guegan, Patrick Rakotomarolahy. GDP nowcasting with ragged-edge data : A semi-parametric modelling. 2008. ⟨halshs-00344839v1⟩

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