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Constructing a Destructive Events Tool using Small Rectangular Areas, Computable General Equilibrium Modelling and Neural Networks

Author

Listed:
  • Peter Dixon
  • Michael Jerie
  • Dean Mustakinov
  • Maureen T. Rimmer
  • Nicholas Sheard
  • Florian Schiffmann
  • Glyn Wittwer

Abstract

This paper describes a destructive events tool (DET) for anticipating the national and regional economic effects of a destructive event occurring at any latitude/longitude in a country. The event is characterized by areas of complete destruction and evacuation. The event could be a natural disaster, major industrial accident, or terrorist attack. The key ingredient for a DET is data showing population and employment by industry in small rectangular areas (SRAs). In the Poland DET, motivating the paper, there are 600,000 SRAs, each 0.5 sq km. This spatial resolution greatly improves the accuracy of the estimation of the economic impacts of events where physical impacts vary substantially across small areas. The second ingredient is an economic model with sufficient regional/industrial definition to translate shocks at an SRA level into implications at the sub-national and national levels. This requirement is met by a multi-regional computable general equilibrium (CGE) model. The final ingredient is an approximation for the model's reduced form. This is necessary so that the DET can be applied by organizations, without in-house CGE expertise, that need quick turnaround in a secure environment. We implement an approximation method for CGE reduced forms based on Neural Networks.

Suggested Citation

  • 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.
    • Handle: RePEc:cop:wpaper:g-349
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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. 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.
    3. Periklis Gogas & Theophilos Papadimitriou, 2021. "Machine Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 1-4, January.
    4. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Destructive events tool; Small rectangular areas; Multi-regional computable general equilibrium models; Neural network approximations to reduced forms;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • H84 - Public Economics - - Miscellaneous Issues - - - Disaster Aid

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