Overcoming Data Sparsity : A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa /
The COVID-19 crisis has had a tremendous economic impact for all countries. Yet, assessing the full impact of the crisis has been frequently hampered by the delayed publication of official GDP statistics in several emerging market and developing economies. This paper outlines a machine-learning fram...
| প্রধান লেখক: | |
|---|---|
| অন্যান্য লেখক: | , , |
| বিন্যাস: | পত্রিকা |
| ভাষা: | English |
| প্রকাশিত: |
Washington, D.C. :
International Monetary Fund,
2022.
|
| মালা: | IMF Working Papers; Working Paper ;
No. 2022/088 |
| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | Full text available on IMF Full text available on IMF |
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| 022 | |a 1018-5941 | ||
| 040 | |a BD-DhAAL |c BD-DhAAL | ||
| 100 | 1 | |a Barhoumi, Karim. | |
| 245 | 1 | 0 | |a Overcoming Data Sparsity : |b A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa / |c Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li. |
| 264 | 1 | |a Washington, D.C. : |b International Monetary Fund, |c 2022. | |
| 300 | |a 1 online resource (23 pages) | ||
| 490 | 1 | |a IMF Working Papers | |
| 500 | |a <strong>Off-Campus Access:</strong> No User ID or Password Required | ||
| 500 | |a <strong>On-Campus Access:</strong> No User ID or Password Required | ||
| 506 | |a Electronic access restricted to authorized BRAC University faculty, staff and students | ||
| 520 | 3 | |a The COVID-19 crisis has had a tremendous economic impact for all countries. Yet, assessing the full impact of the crisis has been frequently hampered by the delayed publication of official GDP statistics in several emerging market and developing economies. This paper outlines a machine-learning framework that helps track economic activity in real time for these economies. As illustrative examples, the framework is applied to selected sub-Saharan African economies. The framework is able to provide timely information on economic activity more swiftly than official statistics. | |
| 538 | |a Mode of access: Internet | ||
| 650 | 7 | |a Forecasting and Other Model Applications |2 imf | |
| 650 | 7 | |a Large Data Sets |2 imf | |
| 650 | 7 | |a Macroeconomic Analyses of Economic Development |2 imf | |
| 650 | 7 | |a Prices, Business Fluctuations, and Cycles |2 imf | |
| 650 | 7 | |a Trade |2 imf | |
| 700 | 1 | |a Iyer, Tara. | |
| 700 | 1 | |a Li, Jiakun. | |
| 700 | 1 | |a Mo Choi, Seung. | |
| 830 | 0 | |a IMF Working Papers; Working Paper ; |v No. 2022/088 | |
| 856 | 4 | 0 | |z Full text available on IMF |u https://elibrary.imf.org/openurl?genre=journal&issn=1018-5941&volume=2022&issue=088 |z IMF e-Library |
| 856 | 4 | 0 | |z Full text available on IMF |u https://elibrary.imf.org/openurl?genre=journal&issn=1018-5941&volume=2022&issue=097 |z IMF e-Library |