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Nowcasting GDP - A Scalable Approach Using DFM, Machine Learning and Novel Data, Applied to European Economies

Jean-Francois Dauphin, Kamil Dybczak, Morgan Maneely, Marzie Taheri Sanjani, Nujin Suphaphiphat, Yifei Wang and Hanqi Zhang

No 2022/052, IMF Working Papers from International Monetary Fund

Abstract: 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.

Keywords: Nowcasting; Factor Model; Machine Learning; Large Data Sets; machine learning algorithm; novel data; approach Using DFM; support vector Machine; data availability; Machine learning; COVID-19; Business cycles; Factor models; Global; Caribbean; Europe (search for similar items in EconPapers)
Pages: 45
Date: 2022-03-11
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-fdg and nep-for
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Citations: View citations in EconPapers (3)

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