Format:
1 Online-Ressource (circa 45 Seiten)
,
Illustrationen
ISBN:
9798400204425
Series Statement:
Working paper / International Monetary Fund WP/22, 52
Content:
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
Additional Edition:
Erscheint auch als Druck-Ausgabe Dauphin, Jean-Francois Nowcasting GDP - A Scalable Approach Using DFM, Machine Learning and Novel Data, Applied to European Economies Washington, D.C. : International Monetary Fund, 2022 ISBN 9798400204425
Language:
English
Keywords:
Graue Literatur
DOI:
10.5089/9798400204425.001
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