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Bayesian Estimation of Regional Production for CGE Modeling

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  • Lee C. Adkins
  • Dan S. Rickman
  • Abid Hameed

Abstract

Computable general equilibrium (CGE) models are often criticized for using restrictive functional forms and relying on external sources for parameter values in their calibration. CGE modelers argue that in many instances reliable econometric estimates of important model parameters are unavailable because they must be estimated using small numbers of time‐series observations. To address these criticisms, this paper uses a Bayesian approach to estimate the parameters of a translog production function in a regional computable general equilibrium model. Using priors from more reliable national estimates, and parameter restrictions required by neoclassical production theory, estimation is done by Markov chain Monte Carlo simulation. A stylized regional CGE model is then used to contrast policy responses of a Cobb‐Douglas specification with those from the estimated translog equation.

Suggested Citation

  • Lee C. Adkins & Dan S. Rickman & Abid Hameed, 2003. "Bayesian Estimation of Regional Production for CGE Modeling," Journal of Regional Science, Wiley Blackwell, vol. 43(4), pages 641-661, November.
    • Handle: RePEc:bla:jregsc:v:43:y:2003:i:4:p:641-661
    DOI: 10.1111/j.0022-4146.2003.00314.x
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    Cited by:

    1. Luc Bauwens & Dimitris Korobilis, 2013. "Bayesian methods," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 16, pages 363-380, Edward Elgar Publishing.
    2. Mark Partridge & Dan Rickman, 2010. "Computable General Equilibrium (CGE) Modelling for Regional Economic Development Analysis," Regional Studies, Taylor & Francis Journals, vol. 44(10), pages 1311-1328.
    3. Michael R. Greenberg & Michael Lahr & Nancy Mantell, 2007. "Understanding the Economic Costs and Benefits of Catastrophes and Their Aftermath: A Review and Suggestions for the U.S. Federal Government," Risk Analysis, John Wiley & Sons, vol. 27(1), pages 83-96, February.
    4. Hendrik Wolff & Thomas Heckelei & Ron Mittelhammer, 2010. "Imposing Curvature and Monotonicity on Flexible Functional Forms: An Efficient Regional Approach," Computational Economics, Springer;Society for Computational Economics, vol. 36(4), pages 309-339, December.
    5. Dan S. Rickman, 2010. "Modern Macroeconomics And Regional Economic Modeling," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 23-41, February.
    6. Euijune Kim & Geoffrey Hewings & Chowoon Hong, 2004. "An Application of an Integrated Transport Network- Multiregional CGE Model: a Framework for the Economic Analysis of Highway Projects," Economic Systems Research, Taylor & Francis Journals, vol. 16(3), pages 235-258.
    7. Lecca, Patrizio & Swales, Kim & Turner, Karen, 2011. "An investigation of issues relating to where energy should enter the production function," Economic Modelling, Elsevier, vol. 28(6), pages 2832-2841.
    8. Wolff, Hendrik & Heckelei, Thomas & Mittelhammer, Ronald C., 2004. "Imposing Monotonicity And Curvature On Flexible Functional Forms," 2004 Annual meeting, August 1-4, Denver, CO 20256, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    9. Ha, Soo Jung & Lange, Ian & Lecca, Patrizio & Turner, Karen, 2012. "Econometric estimation of nested production functions and testing in a computable general equilibrium analysis of economy-wide rebound effec ts," Stirling Economics Discussion Papers 2012-08, University of Stirling, Division of Economics.
    10. Qin Jin & Xiangzheng Deng & Zhan Wang & Chenchen Shi & Xing Li, 2014. "Analysis and Projection of the Relationship between Industrial Structure and Land Use Structure in China," Sustainability, MDPI, vol. 6(12), pages 1-28, December.

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