Quantitative methods for policy analysis course notes
Historically economists have relied on econometric (or statistical) methods to estimate parameters from observed data. In this approach we observe a rich cross-section or time-series dataset, specify an economic model which implicitly defines the underlying behavior (say, simple linear regression),...
| Autores principales: | , , |
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| Formato: | Training Material |
| Lenguaje: | Inglés |
| Publicado: |
International Food Policy Research Institute
2014
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| Acceso en línea: | https://hdl.handle.net/10568/150063 |
| _version_ | 1855533887733628928 |
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| author | Howitt, Richard E. Msangi, Siwa MacEwan, Duncan |
| author_browse | Howitt, Richard E. MacEwan, Duncan Msangi, Siwa |
| author_facet | Howitt, Richard E. Msangi, Siwa MacEwan, Duncan |
| author_sort | Howitt, Richard E. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Historically economists have relied on econometric (or statistical) methods to estimate parameters from observed data. In this approach we observe a rich cross-section or time-series dataset, specify an economic model which implicitly defines the underlying behavior (say, simple linear regression), and estimate key parameters of interest (such as supply and demand elasticities). Econometric analysis typically requires a large dataset and we will often specify a reduced-form model. What do we do when data are limited? What do we do when we want to predict response to policies that simultaneously affect multiple resources, production activities, prices, and markets? Computational methods such as linear programming, calibrated optimization, and dynamic programming allow us to calibrate parameters using limited data and specify a framework that is consistent with economic theory that we can then use to simulate the interaction of complex resource policies. |
| format | Training Material |
| id | CGSpace150063 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1500632025-11-06T07:06:41Z Quantitative methods for policy analysis course notes Howitt, Richard E. Msangi, Siwa MacEwan, Duncan mathematical models policies economics econometrics statistical methods Historically economists have relied on econometric (or statistical) methods to estimate parameters from observed data. In this approach we observe a rich cross-section or time-series dataset, specify an economic model which implicitly defines the underlying behavior (say, simple linear regression), and estimate key parameters of interest (such as supply and demand elasticities). Econometric analysis typically requires a large dataset and we will often specify a reduced-form model. What do we do when data are limited? What do we do when we want to predict response to policies that simultaneously affect multiple resources, production activities, prices, and markets? Computational methods such as linear programming, calibrated optimization, and dynamic programming allow us to calibrate parameters using limited data and specify a framework that is consistent with economic theory that we can then use to simulate the interaction of complex resource policies. 2014 2024-08-01T02:50:35Z 2024-08-01T02:50:35Z Training Material https://hdl.handle.net/10568/150063 en Open Access application/pdf International Food Policy Research Institute Howitt, Richard E.; Msangi, Siwa and MacEwan, Duncan. 2015. Quantitative methods for policy analysis course notes. Washington, DC: International Food Policy Research Institute (IFPRI). https://hdl.handle.net/10568/150063 |
| spellingShingle | mathematical models policies economics econometrics statistical methods Howitt, Richard E. Msangi, Siwa MacEwan, Duncan Quantitative methods for policy analysis course notes |
| title | Quantitative methods for policy analysis course notes |
| title_full | Quantitative methods for policy analysis course notes |
| title_fullStr | Quantitative methods for policy analysis course notes |
| title_full_unstemmed | Quantitative methods for policy analysis course notes |
| title_short | Quantitative methods for policy analysis course notes |
| title_sort | quantitative methods for policy analysis course notes |
| topic | mathematical models policies economics econometrics statistical methods |
| url | https://hdl.handle.net/10568/150063 |
| work_keys_str_mv | AT howittricharde quantitativemethodsforpolicyanalysiscoursenotes AT msangisiwa quantitativemethodsforpolicyanalysiscoursenotes AT macewanduncan quantitativemethodsforpolicyanalysiscoursenotes |