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

Olivier Darné (1) , Amelie Charles (2)

1 LEMNA - Laboratoire d'économie et de management de Nantes Atlantique
2 Audencia Business School

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.

Domaines: Econométrie de la finance [q-fin.ST]; Sciences de l'Homme et Société

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