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

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

March 11, 2022

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Jean-Francois Dauphin, Kamil Dybczak, Morgan Maneely, Marzie Taheri Sanjani, Nujin Suphaphiphat, Yifei Wang, and Hanqi Zhang. Nowcasting GDP - A Scalable Approach Using DFM, Machine Learning and Novel Data, Applied to European Economies, (USA: International Monetary Fund, 2022) accessed December 22, 2024

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.

Subject: Business cycles, COVID-19, Econometric analysis, Economic forecasting, Economic growth, Factor models, Health, Machine learning, Technology

Keywords: Approach Using DFM, Business cycles, Caribbean, COVID-19, Data availability, Europe, Factor Model, Factor models, Global, Large Data Sets, Machine learning, Machine Learning, Machine learning algorithm, Novel data, Nowcasting, Support vector Machine

Publication Details

Pages:

45
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2022/052
Stock No:

WPIEA2022052
ISBN:

9798400204425
ISSN:

1018-5941

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