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- ISIDORE Id: 10670/1.27b928...
- hal: hal-02948802
info:eu-repo/semantics/OpenAccess
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-fin]/Statistical Finance [q-fin.ST]Sujets proches
Prediction Forecasts FuturologyCiter ce document
Olivier Darné et al., « Nowcasting GDP growth using data reduction methods: Evidence for the French economy », HAL SHS (Sciences de l’Homme et de la Société), ID : 10670/1.27b928...
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Résumé
In this paper, we propose bridge models to nowcast French gross domestic product (GDP) quarterly growth rate. The bridge models, allowing economic interpretations, are specified by using a machine learning approach via Lasso-based regressions and by an econometric approach based on an automatic general-to-specific procedure. These approaches allow to select explanatory variables among a large data set of soft data. A recursive forecast study is carried out to assess the forecasting performance. It turns out that the bridge models constructed using the both variable-selection approaches outperform benchmark models and give similar performance in the out-of-sample forecasting exercise. Finally, the combined forecasts of these both approaches display interesting forecasting performance.
