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Reaching the planners: Generating detailed commodity Forecasts from a computable general equilibrium model

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Philip D. Adams
Peter B.Dixon

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Abstract

The largest computable general equilibrium (CGE) models currently in operation produce forecasts for about 100 commodities (goods and services). This level of detail may seem overwhelming to macroeconomists, but is often inadequate for micro planning. For example, a forecast for business services (a typical commodity at the 100-level) is of marginal interest in planning educational programs for sub-categories of business services such as accountancy, advertising and architecture. As a step towards generating information for micro planning, this paper describes a top-down method for disaggregating CGE forecasts. The method relies on detailed sales data often collected by input-output sections of statistical agencies. An application is reported in which forecasts from a 114-commodity CGE model are disaggregated into forecasts for 780 commodities. Within each of the 114 core commodities, differences in prospects are forecast for sub-commodities reflecting differences in their sales patterns and in the degree to which they face import competition.

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Paper provided by Monash University, Centre of Policy Studies/IMPACT Centre in its series Centre of Policy Studies/IMPACT Centre Working Papers with number op-83.

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Date of creation: Mar 1996
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Handle: RePEc:cop:wpaper:op-83

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Find related papers by JEL classification:
C68 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computable General Equilibrium Models
C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Microeconomic Data
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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  1. Alan A. Powell & Richard H. Snape, 1992. "The Contribution of Applied General Equilibrium Analysis to Policy Reform in Australia," Centre of Policy Studies/IMPACT Centre Working Papers g-98, Monash University, Centre of Policy Studies/IMPACT Centre. [Downloadable!]
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  2. Bandara, Jayatilleke S, 1991. " Computable General Equilibrium Models for Development Policy Analysis in LDCs," Journal of Economic Surveys, Blackwell Publishing, vol. 5(1), pages 3-69.
  3. Robinson, Sherman, 1989. "Multisectoral models," Handbook of Development Economics, in: Hollis Chenery† & T.N. Srinivasan (ed.), Handbook of Development Economics, edition 1, volume 2, chapter 18, pages 885-947 Elsevier. [Downloadable!] (restricted)
  4. Adams, Philip D. & Dixon, Peter B. & McDonald, Daina & Meagher, G. A. & Parmenter, Brian R., 1994. "Forecasts for the Australian economy using the MONASH model," International Journal of Forecasting, Elsevier, vol. 10(4), pages 557-571, December. [Downloadable!] (restricted)
  5. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, March. [Downloadable!]
  6. Robinson, Sherman, 1991. "Macroeconomics, financial variables, and computable general equilibrium models," World Development, Elsevier, vol. 19(11), pages 1509-1525, November. [Downloadable!] (restricted)
  7. Pereira, Alfredo M. & Shoven, John B., 1988. "Survey of dynamic computational general equilibrium models for tax policy evaluation," Journal of Policy Modeling, Elsevier, vol. 10(3), pages 401-436. [Downloadable!] (restricted)
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