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Olivier Darne and Amelie Charles
''Nowcasting GDP growth using data reduction methods: Evidence for the French economy''
( 2020, Vol. 40 No.3 )
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.
JEL: C2 - Single Equation Models; Single Variables: General
O4 - Economic Growth and Aggregate Productivity: General
Manuscript Received : Jul 07 2020 Manuscript Accepted : Sep 24 2020

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