Nowcasting GDP : A Scalable Approach Using DFM, Machine Learning and Novel Data, Applied to European Economies /
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...
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| Other Authors: | , , |
| Format: | Journal |
| Language: | English |
| Published: |
Washington, D.C. :
International Monetary Fund,
2022.
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| Series: | IMF Working Papers; Working Paper ;
No. 2022/052 |
| Subjects: | |
| Online Access: | Full text available on IMF |
| 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. |
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| Item Description: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| Physical Description: | 1 online resource (45 pages) |
| Format: | Mode of access: Internet |
| ISSN: | 1018-5941 |
| Access: | Electronic access restricted to authorized BRAC University faculty, staff and students |