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GTAP Resource #6517

"CGE model calibration using multi-objective evolutionary algorithms"
by Nechifor, Victor, Mohammed Basheer and Emanuele Ferrari

The research aims to expand the possibilities for country-level model calibration with the interest of speeding up and facilitating parameter choice when data to inform these values is not available while certain model responses are desirable. For this aim, we use a single-country CGE model set up in a dynamic recursive model and embed it in a multi-objective evolutionary algorithm (MOEA) framework. The framework is thus employed to search the space of possible parameter values, notably in the case of elasticities, to minimise the error between the model target- and actual responses. For illustrative purposes, the case study of aligning a country-level model to a global multi-regional model is considered.

Resource Details (Export Citation) GTAP Keywords
Category: 2022 Conference Paper
Status: Not published
By/In: Presented during the 25th Annual Conference on Global Economic Analysis (Virtual Conference)
Date: 2022
Created: Nechifor, V. (4/12/2022)
Updated: Nechifor, V. (6/8/2022)
Visits: 610
- Baseline development
- Calibration and parameter estimation

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