Systematic risk profiling: A novel approach with applications to Kenya, Rwanda, and Malawi
This paper uses machine learning, simulation, and data mining methods to develop Systematic Risk Profiles of three developing economies: Kenya, Rwanda, and Malawi. We focus on three exogenous shocks with implications for economic performance: world market prices, capital flows, and climate-driven se...
| Autores principales: | , , , , |
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| Formato: | Artículo preliminar |
| Lenguaje: | Inglés |
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International Food Policy Research Institute
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/158180 |
| _version_ | 1855538503093321728 |
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| author | Mukashov, Askar Robinson, Sherman Thurlow, James Arndt, Channing Thomas, Timothy S. |
| author_browse | Arndt, Channing Mukashov, Askar Robinson, Sherman Thomas, Timothy S. Thurlow, James |
| author_facet | Mukashov, Askar Robinson, Sherman Thurlow, James Arndt, Channing Thomas, Timothy S. |
| author_sort | Mukashov, Askar |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This paper uses machine learning, simulation, and data mining methods to develop Systematic Risk Profiles of three developing economies: Kenya, Rwanda, and Malawi. We focus on three exogenous shocks with implications for economic performance: world market prices, capital flows, and climate-driven sectoral productivity. In these and other developing countries, recent decades have been characterized by increased risks associated with all these factors, and there is a demand for instruments that can help to disentangle them. For each country, we utilize historical data to develop multi-variate distributions of shocks. We then sample from these distributions to obtain a series of shock vectors, which we label economic uncertainty scenarios. These scenarios are then entered into economywide computable general equilibrium (CGE) simulation models for the three countries, which allow us to quantify the impact of increased uncertainty on major economic indicators. Finally, we utilize importance metrics from the random forest machine learning algorithm and relative importance metrics from multiple linear regression models to quantify the importance of country-specific risk factors for country performance. We find that Malawi and Rwanda are more vulnerable to sectoral productivity shocks, and Kenya is more exposed to external risks. These findings suggest that a country’s level of development and integration into the global economy are key driving forces defining their risk profiles. The methodology of Systematic Risk Profiling can be applied to many other countries, delineating country-specific risks and vulnerabilities. |
| format | Artículo preliminar |
| id | CGSpace158180 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1581802025-11-06T07:21:11Z Systematic risk profiling: A novel approach with applications to Kenya, Rwanda, and Malawi Mukashov, Askar Robinson, Sherman Thurlow, James Arndt, Channing Thomas, Timothy S. climate computable general equilibrium models machine learning risk uncertainty This paper uses machine learning, simulation, and data mining methods to develop Systematic Risk Profiles of three developing economies: Kenya, Rwanda, and Malawi. We focus on three exogenous shocks with implications for economic performance: world market prices, capital flows, and climate-driven sectoral productivity. In these and other developing countries, recent decades have been characterized by increased risks associated with all these factors, and there is a demand for instruments that can help to disentangle them. For each country, we utilize historical data to develop multi-variate distributions of shocks. We then sample from these distributions to obtain a series of shock vectors, which we label economic uncertainty scenarios. These scenarios are then entered into economywide computable general equilibrium (CGE) simulation models for the three countries, which allow us to quantify the impact of increased uncertainty on major economic indicators. Finally, we utilize importance metrics from the random forest machine learning algorithm and relative importance metrics from multiple linear regression models to quantify the importance of country-specific risk factors for country performance. We find that Malawi and Rwanda are more vulnerable to sectoral productivity shocks, and Kenya is more exposed to external risks. These findings suggest that a country’s level of development and integration into the global economy are key driving forces defining their risk profiles. The methodology of Systematic Risk Profiling can be applied to many other countries, delineating country-specific risks and vulnerabilities. 2024-10-25 2024-10-25T21:24:33Z 2024-10-25T21:24:33Z Working Paper https://hdl.handle.net/10568/158180 en Open Access application/pdf International Food Policy Research Institute Mukashov, Askar; Robinson, Sherman; Thurlow, James; Arndt, Channing; and Thomas, Timothy S. 2024. Systematic risk profiling: A novel approach with applications to Kenya, Rwanda, and Malawi. IFPRI Discussion Paper 2286. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/158180 |
| spellingShingle | climate computable general equilibrium models machine learning risk uncertainty Mukashov, Askar Robinson, Sherman Thurlow, James Arndt, Channing Thomas, Timothy S. Systematic risk profiling: A novel approach with applications to Kenya, Rwanda, and Malawi |
| title | Systematic risk profiling: A novel approach with applications to Kenya, Rwanda, and Malawi |
| title_full | Systematic risk profiling: A novel approach with applications to Kenya, Rwanda, and Malawi |
| title_fullStr | Systematic risk profiling: A novel approach with applications to Kenya, Rwanda, and Malawi |
| title_full_unstemmed | Systematic risk profiling: A novel approach with applications to Kenya, Rwanda, and Malawi |
| title_short | Systematic risk profiling: A novel approach with applications to Kenya, Rwanda, and Malawi |
| title_sort | systematic risk profiling a novel approach with applications to kenya rwanda and malawi |
| topic | climate computable general equilibrium models machine learning risk uncertainty |
| url | https://hdl.handle.net/10568/158180 |
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