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Multiregional and Intertemporal AGE Modelling via GEMPACK

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  • W. Jill Harrison
  • K.R. Pearson

Abstract

The past few years have seen increasing use of multiregional and intertemporal (that is, forward-looking) applied general equilibrium (AGE) models. These models share the characteristic of rapidly expanding dimensions. Because multilateral trade models increase in size with the square of the number of regions and intertemporal models increase in proportion to the number of time periods, they can be very difficult to solve purely because of their size. This paper considers three recent models of these kinds, namely, GTAP, the Global Trade Analysis Project's multiregional model of the world; MRES, a multiregional forecasting model of Australia; and ORANI-INT, an intertemporal model of the Australian economy. Each of these has been implemented using the GEMPACK suite of general-purpose economic modelling software. In this paper we discuss features of GEMPACK which are especially relevant for multiregional and/or intertemporal models. These include an automatic facility to condense models to a manageable size; separation of theory, data, and closure/shocks; automatic creation of updated (that is, post-simulation) data files; and a solver that is well suited to intertemporal models with substantial elements of forward-looking behaviour. Each feature is illustrated via references to one or more of the three models mentioned above.

Suggested Citation

  • W. Jill Harrison & K.R. Pearson, 1994. "Multiregional and Intertemporal AGE Modelling via GEMPACK," Centre of Policy Studies/IMPACT Centre Working Papers ip-66, Victoria University, Centre of Policy Studies/IMPACT Centre.
    • Handle: RePEc:cop:wpaper:ip-66
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    References listed on IDEAS

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    1. M. Kohlhaas & K.R. Pearson, 2002. "Introduction to GEMPACK for GAMS Users," Centre of Policy Studies/IMPACT Centre Working Papers ip-79, Victoria University, Centre of Policy Studies/IMPACT Centre.
    2. Arthur M. Geoffrion, 1987. "An Introduction to Structured Modeling," Management Science, INFORMS, vol. 33(5), pages 547-588, May.
    3. Harrison, W Jill & Pearson, K. R. & Powell, Alan A. & Small, John E., 1994. "Solving Applied General Equilibrium Models Represented as a Mixture of Linearized and Levels Equations," Computational Economics, Springer;Society for Computational Economics, vol. 7(3), pages 203-223.
    4. Hertel, Thomas W. & Mark Horridge, J. & Pearson, K. R., 1992. "Mending the family tree a reconciliation of the linearization and levels schools of AGE modelling," Economic Modelling, Elsevier, vol. 9(4), pages 385-407, October.
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    Cited by:

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    2. Eduardo A. Haddad & Geoffrey J.D. Hewings, 2004. "Transportation Costs, Increasing Returns and Regional growth: An Interregional CGE Analysis," Working papers - Textos para Discussao do Departamento de Economia da Universidade de Brasilia 315, Departamento de Economia da Universidade de Brasilia.
    3. World Bank, 2008. "Brazil : Evaluating the Macroeconomic and Distributional Impacts of Lowering Transportation Costs," World Bank Publications - Reports 8083, The World Bank Group.

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    More about this item

    JEL classification:

    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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