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A Baseline Calibration Procedure for CGE models: An Application for DART

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  • Jin, Ding
  • Thube, Sneha Dattatraya
  • Hedtrich, Johannes
  • Henning, Christian
  • Delzeit, Ruth

Abstract

In the recent years the research interests in the field of Computable General Equilibrium (CGE) modeling has been placed on calibrating the baseline dynamics to forecasts. This paper suggests the formal method to calibrate all exogenous parameters of the Dynamic Applied Reional Trade (DART) model to forecasts from the the World Energy Outlook 2018 re_x0002_port. First, we determine the exogenous parameters (inputs) and forecasts (outputs) for the calibration procedure. Then we use the metamodeling method to generate surrogate models for the DART model. In the next step, we implement the Maximum A Posterior (MAP) method to estimate the exogenous parameters that are used to calibrate the baseline dynamics. Finally, we run the simulation with the estimates to test the performance.

Suggested Citation

  • Jin, Ding & Thube, Sneha Dattatraya & Hedtrich, Johannes & Henning, Christian & Delzeit, Ruth, 2019. "A Baseline Calibration Procedure for CGE models: An Application for DART," Conference papers 333057, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
  • Handle: RePEc:ags:pugtwp:333057
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    1. 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.
    2. Andreas Raue & Marcel Schilling & Julie Bachmann & Andrew Matteson & Max Schelke & Daniel Kaschek & Sabine Hug & Clemens Kreutz & Brian D Harms & Fabian J Theis & Ursula Klingmüller & Jens Timmer, 2013. "Lessons Learned from Quantitative Dynamical Modeling in Systems Biology," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-17, September.
    3. 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.
    4. Heckelei, Thomas & Mittelhammer, Ronald C. & Jansson, Torbjorn, 2008. "A Bayesian Alternative To Generalized Cross Entropy Solutions For Underdetermined Econometric Models," Discussion Papers 56973, University of Bonn, Institute for Food and Resource Economics.
    5. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    6. Klepper, Gernot & Peterson, Sonja, 2003. "On the robustness of marginal abatement cost curves: the influence of world energy prices," Kiel Working Papers 1138, Kiel Institute for the World Economy (IfW Kiel).
    7. Janssen, Hans, 2013. "Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 123-132.
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    Cited by:

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