Integrating fundamental model uncertainty in policy analysis: A Bayesian averaging approach combining CGE-models with metamodeling techniques
Sustainable economic development in the future is driven by public policy on regional, national and global levels. Therefore a comprehensive policy analysis is needed that provides consistent and effective policy support. However, a general problem facing classical policy analysis is model uncertain...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/140020 |
| _version_ | 1855521785980649472 |
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| author | Ziesmer, Johannes Jin, Ding Mukashov, Askar Henning, Christian |
| author_browse | Henning, Christian Jin, Ding Mukashov, Askar Ziesmer, Johannes |
| author_facet | Ziesmer, Johannes Jin, Ding Mukashov, Askar Henning, Christian |
| author_sort | Ziesmer, Johannes |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Sustainable economic development in the future is driven by public policy on regional, national and global levels. Therefore a comprehensive policy analysis is needed that provides consistent and effective policy support. However, a general problem facing classical policy analysis is model uncertainty. All actors, those involved in the policy choice and those in the policy analysis, are fundamentally uncertain which of the different models corresponds to the true generative mechanism that represents the natural, economic, or social phenomena on which policy analysis is focused. In this paper, we propose a general framework that explicitly incorporates model uncertainty into the derivation of a policy choice. Incorporating model uncertainty into the analysis is limited by the very high required computational effort. In this regard, we apply metamodeling techniques as a way to reduce computational complexity. We demonstrate the effect of different metamodel types using a reduced model for the case of CAADP in Senegal. Furthermore, we explicitly show that ignoring model uncertainty leads to inefficient policy choices and results in a large waste of public resources. |
| format | Journal Article |
| id | CGSpace140020 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1400202025-10-26T13:01:22Z Integrating fundamental model uncertainty in policy analysis: A Bayesian averaging approach combining CGE-models with metamodeling techniques Ziesmer, Johannes Jin, Ding Mukashov, Askar Henning, Christian models policies economic development uncertainty bayesian theory modelling computable general equilibrium models quantitative analysis Sustainable economic development in the future is driven by public policy on regional, national and global levels. Therefore a comprehensive policy analysis is needed that provides consistent and effective policy support. However, a general problem facing classical policy analysis is model uncertainty. All actors, those involved in the policy choice and those in the policy analysis, are fundamentally uncertain which of the different models corresponds to the true generative mechanism that represents the natural, economic, or social phenomena on which policy analysis is focused. In this paper, we propose a general framework that explicitly incorporates model uncertainty into the derivation of a policy choice. Incorporating model uncertainty into the analysis is limited by the very high required computational effort. In this regard, we apply metamodeling techniques as a way to reduce computational complexity. We demonstrate the effect of different metamodel types using a reduced model for the case of CAADP in Senegal. Furthermore, we explicitly show that ignoring model uncertainty leads to inefficient policy choices and results in a large waste of public resources. 2023-06 2024-03-14T12:08:49Z 2024-03-14T12:08:49Z Journal Article https://hdl.handle.net/10568/140020 en Open Access Elsevier Ziesmer, Johannes; Jin, Ding; Mukashov, Askar; and Henning, Christian. 2023. Integrating fundamental model uncertainty in policy analysis: A Bayesian averaging approach combining CGE-models with metamodeling techniques. Socio-Economic Planning Sciences 87: 101591. https://doi.org/10.1016/j.seps.2023.101591 |
| spellingShingle | models policies economic development uncertainty bayesian theory modelling computable general equilibrium models quantitative analysis Ziesmer, Johannes Jin, Ding Mukashov, Askar Henning, Christian Integrating fundamental model uncertainty in policy analysis: A Bayesian averaging approach combining CGE-models with metamodeling techniques |
| title | Integrating fundamental model uncertainty in policy analysis: A Bayesian averaging approach combining CGE-models with metamodeling techniques |
| title_full | Integrating fundamental model uncertainty in policy analysis: A Bayesian averaging approach combining CGE-models with metamodeling techniques |
| title_fullStr | Integrating fundamental model uncertainty in policy analysis: A Bayesian averaging approach combining CGE-models with metamodeling techniques |
| title_full_unstemmed | Integrating fundamental model uncertainty in policy analysis: A Bayesian averaging approach combining CGE-models with metamodeling techniques |
| title_short | Integrating fundamental model uncertainty in policy analysis: A Bayesian averaging approach combining CGE-models with metamodeling techniques |
| title_sort | integrating fundamental model uncertainty in policy analysis a bayesian averaging approach combining cge models with metamodeling techniques |
| topic | models policies economic development uncertainty bayesian theory modelling computable general equilibrium models quantitative analysis |
| url | https://hdl.handle.net/10568/140020 |
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