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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| अन्य लेखक: | , , |
| स्वरूप: | पत्रिका |
| भाषा: | English |
| प्रकाशित: |
Washington, D.C. :
International Monetary Fund,
2022.
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| श्रृंखला: | IMF Working Papers; Working Paper ;
No. 2022/052 |
| विषय: | |
| ऑनलाइन पहुंच: | Full text available on IMF |
| सारांश: | 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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| वस्तु वर्णन: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| भौतिक वर्णन: | 1 online resource (45 pages) |
| स्वरूप: | Mode of access: Internet |
| आईएसएसएन: | 1018-5941 |
| अभिगमन: | Electronic access restricted to authorized BRAC University faculty, staff and students |