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

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.