A Dynamic Baseline Calibration Procedure for CGE models

Johannes Ziesmer (), Ding Jin, Sneha D Thube and Christian Henning
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Johannes Ziesmer: Leibniz Institute for Educational Trajectories
Ding Jin: Institute of Agricultural Economics, University of Kiel
Sneha D Thube: Kiel Institute for World Economy
Christian Henning: Institute of Agricultural Economics, University of Kiel

Computational Economics, 2023, vol. 61, issue 4, No 2, 1368 pages

Abstract: Abstract Baseline assumptions play a crucial role in conducting consistent quantitative policy assessments for dynamic Computable General Equilibrium (CGE) models. Two essential factors that influence the determination of the baselines are the data sources of projections and the applied calibration methods. We propose a general, Bayesian approach that can be employed to build a baseline for any recursive-dynamic CGE model. We use metamodeling techniques to transform the calibration problem into a tractable optimization problem while simultaneously reducing the computational costs. This transformation allows us to derive the exogenous model parameters that are needed to match the projections. We demonstrate how to apply the approach using a simple CGE and supply the full code. Additionally, we apply our method to a multi-region, multi-sector model and show that calibrated parameters matter as policy implications derived from simulations differ significantly between them.

Keywords: Dynamic baseline calibration; Model uncertainty; Bayesian approach; Metamodeling; Simulation optimization; Quantitative policy analysis (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10614-022-10248-4

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