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

Olivier Darné and Amelie Charles
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Amelie Charles: Audencia Business School

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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.

Date: 2020-09
New Economics Papers: this item is included in nep-big, nep-eec and nep-for
Note: View the original document on HAL open archive server: https://audencia.hal.science/hal-02948802
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Published in Economics Bulletin, 2020

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Journal Article: Nowcasting GDP growth using data reduction methods: Evidence for the French economy (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02948802

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