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

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HAL-SHS : économie et finance

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info:eu-repo/semantics/altIdentifier/doi/10.1002/for.1159

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Centre pour la communication scientifique directe

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info:eu-repo/semantics/OpenAccess

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euro area GDP • real-time nowcasting • forecasting • non-parametric methods C - Mathematical and Quantitative Methods/C.C2 - Single Equation Models • Single Variables/C.C2.C22 - Time-Series Models • Dynamic Quantile Regressions • Dynamic Treatment Effect Models • Diffusion Processes C - Mathematical and Quantitative Methods/C.C5 - Econometric Modeling/C.C5.C53 - Forecasting and Prediction Methods • Simulation Methods E - Macroeconomics and Monetary Economics/E.E3 - Prices, Business Fluctuations, and Cycles/E.E3.E32 - Business Fluctuations • Cycles

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GDP Domestic product, Gross

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Laurent Ferrara et al., « GDP nowcasting with ragged-edge data: a semi-parametric modeling », HAL-SHS : économie et finance, ID : 10.1002/for.1159

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Résumé En Fr

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.

Ce papier propose une approche innovante, basée sur des méthodes non-paramétriques, pour prédire en temps réel le produit intérieur brut.

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