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

Economics Bulletin, 2020, vol. 40, issue 3, 2431-2439

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.

Keywords: GDP forecasting; shrinkage methods; general-to-specific approach; bridge models. (search for similar items in EconPapers)
JEL-codes: C2 O4 (search for similar items in EconPapers)
Date: 2020-09-24
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Citations: View citations in EconPapers (1)

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Working Paper: Nowcasting GDP growth using data reduction methods: Evidence for the French economy (2020)
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