Parameter estimation for a computable general equilibrium model: a maximum entropy approach
The authors introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of nonlinear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium const...
| Main Authors: | , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Elsevier
2002
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/156294 |
| _version_ | 1855525367808262144 |
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| author | Arndt, Channing Robinson, Sherman Tarp, Finn |
| author_browse | Arndt, Channing Robinson, Sherman Tarp, Finn |
| author_facet | Arndt, Channing Robinson, Sherman Tarp, Finn |
| author_sort | Arndt, Channing |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The authors introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of nonlinear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints. Second, it permits incorporation of prior information on parameter values. Third, it can be applied in the absence of copious data. Finally, it supplies measures of the capacity of the model to reproduce the historical record and the statistical significance of parameter estimates. The method is applied to estimating a CGE model of Mozambique."-- from Abstract. |
| format | Journal Article |
| id | CGSpace156294 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2002 |
| publishDateRange | 2002 |
| publishDateSort | 2002 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1562942025-01-24T08:54:13Z Parameter estimation for a computable general equilibrium model: a maximum entropy approach Arndt, Channing Robinson, Sherman Tarp, Finn equilibrium theory models computable general equilibrium models agriculture The authors introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of nonlinear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints. Second, it permits incorporation of prior information on parameter values. Third, it can be applied in the absence of copious data. Finally, it supplies measures of the capacity of the model to reproduce the historical record and the statistical significance of parameter estimates. The method is applied to estimating a CGE model of Mozambique."-- from Abstract. 2002-05 2024-10-24T12:43:43Z 2024-10-24T12:43:43Z Journal Article https://hdl.handle.net/10568/156294 en Limited Access Elsevier Arndt, Channing; Robinson, Sherman; Tarp, Finn. 2002. Parameter estimation for a computable general equilibrium model: a maximum entropy approach. Economic Modelling 19(3): 375-398. https://doi.org/10.1016/S0264-9993(01)00068-2 |
| spellingShingle | equilibrium theory models computable general equilibrium models agriculture Arndt, Channing Robinson, Sherman Tarp, Finn Parameter estimation for a computable general equilibrium model: a maximum entropy approach |
| title | Parameter estimation for a computable general equilibrium model: a maximum entropy approach |
| title_full | Parameter estimation for a computable general equilibrium model: a maximum entropy approach |
| title_fullStr | Parameter estimation for a computable general equilibrium model: a maximum entropy approach |
| title_full_unstemmed | Parameter estimation for a computable general equilibrium model: a maximum entropy approach |
| title_short | Parameter estimation for a computable general equilibrium model: a maximum entropy approach |
| title_sort | parameter estimation for a computable general equilibrium model a maximum entropy approach |
| topic | equilibrium theory models computable general equilibrium models agriculture |
| url | https://hdl.handle.net/10568/156294 |
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