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Applying Meta modeling for extended CGE-modeling: Sample techniques and potential application

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  • Jin, Ding
  • Hedtrich, Johannes
  • Henning, Christian

Abstract

CGE-applications are workhorse models in applied economic policy analysis, i.e. the development economic literature or modeling climate and energy policies. However, beyond its prominent application CGE-model approaches are also heavily criticized. On the one hand, while the general equilibrium model has the advantages in terms of internal consistency and allowing for clearer identification of causality, the application of a CGE model requires simplifying assumptions that are open to challenge. Moreover, empirical results derived from the CGE-model application are very sensitive to specific model specifications, that are often only weakly empirically justified, e.g. assumed closure rules and assumed elasticity parameters. Thus, many results, e.g. growth-poverty linkages, that are derived from a CGE model are in fact plagued by high model uncertainty implying a limited potential to generate robust policy-relevant messages. To overcome theoretical shortcomings of ad hoc CGE-approaches we suggest a combined approach incorporating econometric approaches to assess policy-growth linkages that are integrated into the CGE-approach modeling growth-poverty linkages. However, estimation of integrated econometric and CGE-modeling approaches are often tedious.Finally, CGE-model approaches are often applied to provide scientific expertise to advise the government in political practice. Hence, it would be necessary to incorporate general equilibrium models into overall decision-making models. However, given the size and complexity of CGE-models integration of these approaches into an overall decision-making modeling approach is rather difficult and often numerically not tractable. Therefore, in the context of such a situation, we suggest application of meta-modeling as a potential solution of the application problems of standard CGE-models in advanced policy modeling frameworks and we are motivated to begin with tackling the problem of elasticity parameters and closure rules.

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  • Jin, Ding & Hedtrich, Johannes & Henning, Christian, 2018. "Applying Meta modeling for extended CGE-modeling: Sample techniques and potential application," Conference papers 332947, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
  • Handle: RePEc:ags:pugtwp:332947
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    References listed on IDEAS

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    1. Francois Bourguignon & Luiz A. Pereira da Silva, 2003. "The Impact of Economic Policies on Poverty and Income Distribution : Evaluation Techniques and Tools," World Bank Publications - Books, The World Bank Group, number 15090, December.
    2. Jack P. C. Kleijnen, 1975. "A Comment on Blanning's “Metamodel for Sensitivity Analysis: The Regression Metamodel in Simulation”," Interfaces, INFORMS, vol. 5(3), pages 21-23, May.
    3. Fan, Shenggen (ed.), 2008. "Public expenditures, growth, and poverty: Lessons from developing countries," IFPRI books, International Food Policy Research Institute (IFPRI), number 978-0-8018-8859-5.
    4. Jack P. C. Kleijnen & Susan M. Sanchez & Thomas W. Lucas & Thomas M. Cioppa, 2005. "State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments," INFORMS Journal on Computing, INFORMS, vol. 17(3), pages 263-289, August.
    5. Chunhua Wang, 2007. "A dynamic stochastic frontier production model with time-varying efficiency: comment," Applied Economics Letters, Taylor & Francis Journals, vol. 14(6), pages 415-417.
    6. Kleijnen, Jack P. C. & Sargent, Robert G., 2000. "A methodology for fitting and validating metamodels in simulation," European Journal of Operational Research, Elsevier, vol. 120(1), pages 14-29, January.
    7. Kleijnen, Jack P. C. & Standridge, Charles R., 1988. "Experimental design and regression analysis in simulation: An FMS case study," European Journal of Operational Research, Elsevier, vol. 33(3), pages 257-261, February.
    8. Hazledine, Tim, 1992. "A Critique of Computable General Equilibrium Models for Trade Policy Analysis," Working Papers 51131, International Agricultural Trade Research Consortium.
    9. Fan, Shenggen, 2008. "Public expenditures, growth, and poverty in developing countries: Lessons from developing countries," Issue briefs 51, International Food Policy Research Institute (IFPRI).
    10. Robert W. Blanning, 1974. "The Sources and Uses of Sensitivity Information," Interfaces, INFORMS, vol. 4(4), pages 32-38, August.
    11. Vonk Noordegraaf, Antonie & Nielen, Mirjam & Kleijnen, Jack P. C., 2003. "Sensitivity analysis by experimental design and metamodelling: Case study on simulation in national animal disease control," European Journal of Operational Research, Elsevier, vol. 146(3), pages 433-443, May.
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