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Nowcasting GDP growth using data reduction methods: Evidence for the French economy

Abstract : 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.
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Submitted on : Friday, September 25, 2020 - 9:36:58 AM
Last modification on : Thursday, April 28, 2022 - 4:03:05 PM
Long-term archiving on: : Thursday, December 3, 2020 - 5:32:20 PM

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  • HAL Id : hal-02948802, version 1

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Olivier Darne, Amelie Charles. Nowcasting GDP growth using data reduction methods: Evidence for the French economy. Economics Bulletin, Economics Bulletin, 2020. ⟨hal-02948802⟩

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