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Author/Editor:
Jean-Francois Dauphin ; Kamil Dybczak ; Morgan Maneely ; Marzie Taheri Sanjani ; . Nujin Suphaphiphat ; Yifei Wang ; Hanqi Zhang
Publication Date:
March 11, 2022
Electronic Access:
Free Download. Use the free Adobe Acrobat Reader to view this PDF file
Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
Summary:
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.
Series:
Working Paper No. 2022/052
Subject:
Business cycles COVID-19 Econometric analysis Economic forecasting Economic growth Factor models Health Machine learning Technology
Frequency:
regular
Publication Date:
March 11, 2022
ISBN/ISSN:
9798400204425/1018-5941
Stock No:
WPIEA2022052
Pages:
45
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