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02019cas a2200325 a 4500 |
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|c 5.00 USD
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|z 9781557758873
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|a 1018-5941
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|a BD-DhAAL
|c BD-DhAAL
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|a Redl, Chris.
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|a Forecasting Social Unrest :
|b A Machine Learning Approach /
|c Chris Redl, Sandile Hlatshwayo.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2021.
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| 300 |
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|a 1 online resource (29 pages)
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|a IMF Working Papers
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|a <strong>Off-Campus Access:</strong> No User ID or Password Required
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|a <strong>On-Campus Access:</strong> No User ID or Password Required
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|a Electronic access restricted to authorized BRAC University faculty, staff and students
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|a We produce a social unrest risk index for 125 countries covering a period of 1996 to 2020. The risk of social unrest is based on the probability of unrest in the following year derived from a machine learning model drawing on over 340 indicators covering a wide range of macro-financial, socioeconomic, development and political variables. The prediction model correctly forecasts unrest in the following year approximately two-thirds of the time. Shapley values indicate that the key drivers of the predictions include high levels of unrest, food price inflation and mobile phone penetration, which accord with previous findings in the literature.
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|a Mode of access: Internet
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|a Forecasting and Other Model Applications
|2 imf
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| 650 |
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|a Machine Learning Model
|2 imf
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|a Neural Networks and Related Topics
|2 imf
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|a Political Economy
|2 imf
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| 650 |
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|a Risk Index
|2 imf
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| 650 |
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|a Social Unrest, Machine Learning
|2 imf
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| 700 |
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|a Hlatshwayo, Sandile.
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|a IMF Working Papers; Working Paper ;
|v No. 2021/263
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| 856 |
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|z Full text available on IMF
|u http://elibrary.imf.org/view/journals/001/2021/263/001.2021.issue-263-en.xml
|z IMF e-Library
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