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Neural-Network approximation of reduced forms for CGE models explained by elementary examples

Author

Listed:
  • Peter B. Dixon
  • Maureen T. Rimmer
  • Florian Schiffmann

Abstract

Neural Network (NN) theory provides a powerful method for approximating the reduced form of a large-scale multi-regional CGE model. However, NN methods are relatively unknown by CGE modellers. We set out the theory of the NN approximation method and demonstrate how it works with simple examples. The paper is motivated by a project for a client with limited in-house CGE capabilities but requiring the ability to obtain CGE solutions at short notice in a confidential environment. We describe how an NN approximation meets the client's needs. The NN approximation is more accurate and broadly applicable than earlier approaches that CGE modellers have used based on regression equations and matrices of elasticities.

Suggested Citation

  • Peter B. Dixon & Maureen T. Rimmer & Florian Schiffmann, 2024. "Neural-Network approximation of reduced forms for CGE models explained by elementary examples," Centre of Policy Studies/IMPACT Centre Working Papers g-348, Victoria University, Centre of Policy Studies/IMPACT Centre.
    • Handle: RePEc:cop:wpaper:g-348
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    References listed on IDEAS

    as
    1. Glyn Wittwer & Mark Horridge, 2018. "Prefectural Representation of the Regions of China in a Bottom-up CGE Model: SinoTERM365," Journal of Global Economic Analysis, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, vol. 3(2), pages 178-213, December.
    2. Britz, Wolfgang & Li, Jingwen & Shang, Linmei, 2021. "Combining large-scale sensitivity analysis in Computable General Equilibrium models with Machine Learning: An Example Application to policy supporting the bio-economy," Conference papers 333285, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
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    Cited by:

    1. Peter Dixon & Michael Jerie & Dean Mustakinov & Maureen T. Rimmer & Nicholas Sheard & Florian Schiffmann & Glyn Wittwer, 2024. "Constructing a Destructive Events Tool using Small Rectangular Areas, Computable General Equilibrium Modelling and Neural Networks," Centre of Policy Studies/IMPACT Centre Working Papers g-349, Victoria University, Centre of Policy Studies/IMPACT Centre.

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    1. Peter Dixon & Michael Jerie & Dean Mustakinov & Maureen T. Rimmer & Nicholas Sheard & Florian Schiffmann & Glyn Wittwer, 2024. "Constructing a Destructive Events Tool using Small Rectangular Areas, Computable General Equilibrium Modelling and Neural Networks," Centre of Policy Studies/IMPACT Centre Working Papers g-349, Victoria University, Centre of Policy Studies/IMPACT Centre.
    2. Wittwer, Glyn, 2022. "Preparing a multi-country, sub-national CGE model: EuroTERM including Ukraine," Conference papers 333470, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.

    More about this item

    Keywords

    Neural network method explained; Neural network approximations to reduced forms; Multi-regional computable general equilibrium models;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models

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