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Forecasting and Policy Analysis with a Dynamic CGE Model of Australia

Author

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  • Peter B. Dixon
  • Maureen T. Rimmer

Abstract

The main ideas in this paper are: (a) that CGE models can be used in forecasting; and (b) that forecasts matter for policy analysis. We demonstrate these ideas by describing an application of MONASH, a dynamic CGE model of Australia, to the Australian motor vehicle industry over the period 1987 to 2016. The key to generating believable forecasts is to use detailed information available from expert groups specializing in the analysis of different aspects of the economy. In MONASH we incorporate forecasts by specialists: on the domestic macro economy; on Australian economic policy; on world commodity markets; on international tourism; on production technologies; and on consumer preferences. We have found that CGE forecasts incorporating such specialist information are readily saleable to public and private organizations concerned with investment, employment, training and education issues. This is partly because the economy-wide consistency guaranteed by the CGE approach enables users of economic intelligence to see the disparate forecasts dealing with different parts and aspects of the economy within an integrated perspective. Over the last thirty five years, CGE models have been used almost exclusively as aids to "what if" (usually policy) analysis. In almost all cases it has been assumed that the effects of the shock under consideration are independent of the future path of the economy. Thus, for "what if" analysis, a common implicit view is that realistic basecase forecasts are unnecessary. Contrary to this view, we find that "what if" answers depend significantly on the basecase forecasts. This is not surprising when we are concerned with unemployment and other adjustment costs. However, we find that basecase forecasts are critical even when our concern is the long-run welfare implications of a policy change. For example, we find that the simulated long-run effects of a tariff cut on imported cars are strongly influenced by the basecase forecast of the rate of technical progress in the car industry relative to that in other industries.

Suggested Citation

  • Peter B. Dixon & Maureen T. Rimmer, 1998. "Forecasting and Policy Analysis with a Dynamic CGE Model of Australia," Centre of Policy Studies/IMPACT Centre Working Papers op-90, Victoria University, Centre of Policy Studies/IMPACT Centre.
  • Handle: RePEc:cop:wpaper:op-90
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    References listed on IDEAS

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    1. 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.
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    Cited by:

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    2. Tien Duc Pham & Son Nghiem & Larry Dwyer, 2018. "The economic impacts of a changing visa fee for Chinese tourists to Australia," Tourism Economics, , vol. 24(1), pages 109-126, February.
    3. Edson Paulo Domingues & Kênia Barreiro de Souza, 2012. "The Welfare Impacts of Changes in the Brazilian Domestic Work Market," Working Papers 96, International Policy Centre for Inclusive Growth.
    4. Chuang, Yih-chyi & Lee, Chun-yuan, 2002. "Industry-Specific Human Capital and the Wage Profile: Evidence from Taiwan," Conference papers 330989, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    5. Guilhoto, Joaquim José Martins & Hasegawa, Marcos & Lopes, Ricardo Luis, 2002. "A estrutura teórica do modelo inter-regional para a economia brasileira - MIBRA [The theoretical structure of inter regional model for the Brazilian economy - MIBRA]," MPRA Paper 54019, University Library of Munich, Germany.
    6. Weslem Rodrigues Faria & Eduardo Amaral Haddad, 2017. "Modeling Land Use And The Effects Of Climate Change In Brazil," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 1-37, February.
    7. Ana Medina-López & Montserrat Jiménez-Partearroyo & Ángeles Cámara, 2023. "A Dynamic CGE Model for Optimization in Business Analytics: Simulating the Impact of Investment Shocks," Mathematics, MDPI, vol. 12(1), pages 1-42, December.
    8. Marcos Minoru Hasegawa, 2010. "The Tax Policy in the Chilean Economy: a Regional Applied General Equilibrium Analysis," Documentos de Trabajo en Economia y Ciencia Regional 05, Universidad Catolica del Norte, Chile, Department of Economics, revised Dec 2010.
    9. Kym Anderson, 2016. "Agricultural Trade, Policy Reforms, and Global Food Security," Palgrave Studies in Agricultural Economics and Food Policy, Palgrave Macmillan, number 978-1-137-46925-0, November.
    10. Brian Parmenter, 2004. "Distinguished Fellow of the Economic Society of Australia, 2003: Peter Dixon," The Economic Record, The Economic Society of Australia, vol. 80(249), pages 141-144, June.
    11. Edson Ramos de Medeiros & Terciane Sabadini Carvalho & Kênia Barreiro de Souza, 2022. "São Francisco River Transposition Project: Socio‐economic impacts in Brazilian Northeast semi‐arid region," Papers in Regional Science, Wiley Blackwell, vol. 101(6), pages 1441-1468, December.
    12. Wittwer, Glyn, 2000. "The Sensitivity Of Wine Industry Outcomes To Model Assumptions In Gst Scenarios," 2000 Conference (44th), January 23-25, 2000, Sydney, Australia 123742, Australian Agricultural and Resource Economics Society.
    13. Cook, Larry & Harslett, Philip, 2015. "An introduction to entropy estimation of parameters in economic models," Conference papers 332651, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    14. Pant, Hom, 2015. "A generic approach to investment allocation in recursively dynamic CGE models," Conference papers 332649, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    15. Fontagné, Lionel & Foure, Jean, 2017. "General Equilibrium in the Long Run: a Tentative Quantification of the SSP scenarios," Conference papers 332833, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    16. W. Jill Harrison & J. Mark Horridge & K.R. Pearson, 2000. "Decomposing Simulation Results with Respect to Exogenous Shocks," Computational Economics, Springer;Society for Computational Economics, vol. 15(3), pages 227-249, June.
    17. Yoonkyo Cho & Taehwan Kim & Jaewhak Roh, 2021. "An analysis of the effects of electronic commerce on the Korean economy using the CGE model," Electronic Commerce Research, Springer, vol. 21(3), pages 831-854, September.
    18. Xi He & Edward J. Balistreri & Gyu Hyun Kim & Wendong Zhang, 2022. "A general equilibrium assessment of COVID-19's labor productivity impacts on china's regional economies," Journal of Productivity Analysis, Springer, vol. 58(2), pages 129-150, December.
    19. Jean Foure & Angel Aguiar & Ruben Bibas & Jean Chateau & Shinichiro Fujimori & Julien Lefevre & Marian Leimbach & Luis Rey-Los-Santos & Hugo Valin, 2020. "Macroeconomic Drivers of Baseline Scenarios in Dynamic CGE models: Review and Guidelines Proposal," Journal of Global Economic Analysis, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, vol. 5(1), pages 28-62, June.
    20. Holmøy, Erling, 2016. "The development and use of CGE models in Norway," Journal of Policy Modeling, Elsevier, vol. 38(3), pages 448-474.
    21. Banerjee, Onil & Cicowiez, Martin & Vargas, Renato & Obst, Carl & Cala, Javier Rojas & Alvarez-Espinosa, Andrés Camilo & Melo, Sioux & Riveros, Leidy & Romero, Germán & Meneses, Diego Sáenz, 2021. "Gross domestic product alone provides misleading policy guidance for post-conflict land use trajectories in Colombia," Ecological Economics, Elsevier, vol. 182(C).
    22. Huang, Jikun & van Meijl, Hans & van Tongeren, Frank, 2002. "Biotechnology Boosts to Crop Productivity in China and Its Impact on Global," Conference papers 330986, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.

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    More about this item

    Keywords

    CGE model; forecasting; policy analysis; MONASH model; automobile industry; adjustment costs;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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