Nowcasting GDP : A Scalable Approach Using DFM, Machine Learning and Novel Data, Applied to European Economies - Dauphin, Jean-Francois.; Dybczak, Kamil; Maneely, Morgan; Taheri Sanjani, Marzie (Washington, D.C. : International Monetary Fund, 2022)
This paper describes recent work to strengthen nowcasting capacity at the IMF's European department. It motivates and compiles datasets of standard and nontraditional variables, such as Google search and air quality. It applies standard dynamic factor models (DFMs) and several machine learning (ML) algorithms to nowcast GDP growth across a heterogenous group of European economies during normal and crisis times. Most of our methods significantly outperform the AR(1) benchmark model. Our DFMs tend to perform better during normal times while many of the ML methods we used performed strongly at identifying turning points. Our approach is easily applicable to other countries, subject to data availability.
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Keywords: Forecasting and Other Model Applications; Machine Learning Algorithm; Monetary Policy; Multiple or Simultaneous Equation Models; Nowcasting, Factor Model, Machine Learning and Large Data Sets