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

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:

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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

Frequency:

regular

English

Publication Date:

March 11, 2022

ISBN/ISSN:

9798400204425/1018-5941

Stock No:

WPIEA2022052

Pages:

45

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