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  • Gary Anderson


Anderson's papers describe a method for solving linear saddle point models. The numerical implementation of the algorithm has proved useful in a wide array of applications including analyzing linear perfect foresight models, and providing initial solutions and asymptotic constraints for nonlinear models. In contract, although the symbolic algebra version has been available for years, few researchers have used it in their work. Most researchers believe that symbolic algebraic solution of useful rational expectations models remains beyond the capacity of present day computers. This papers provides practical advice on how to use symbolic algebra packages to solve moderate sized linear rational expectations models. The computational bottleneck lies in the determination of the invariant space associated with large roots of the transition matrix. This paper provides strategies for avoiding the solution of high order polynomial characteristic, and provides techniques for "switching" to numeric computation at strategic points. With present day PC's, one can solve linear models with ten or twenty equations using these strategies.

Suggested Citation

  • Gary Anderson, 2001. "Practical," Computing in Economics and Finance 2001 138, Society for Computational Economics.
  • Handle: RePEc:sce:scecf1:138

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    References listed on IDEAS

    1. Tracy R. Lewis & Richard Schmalensee, 1979. "Non-convexity and Optimal Harvesting Strategies for Renewable Resources," Canadian Journal of Economics, Canadian Economics Association, vol. 12(4), pages 677-691, November.
    2. Rognvaldur Hannesson, 1975. "Fishery Dynamics: A North Atlantic Cod Fishery," Canadian Journal of Economics, Canadian Economics Association, vol. 8(2), pages 151-173, May.
    3. Jorgensen, Steffen & Kort, Peter M., 1997. "Optimal investment and finance in renewable resource harvesting," Journal of Economic Dynamics and Control, Elsevier, vol. 21(2-3), pages 603-630.
    4. Dawid, Herbert & Kopel, Michael, 1997. "On the Economically Optimal Exploitation of a Renewable Resource: The Case of a Convex Environment and a Convex Return Function," Journal of Economic Theory, Elsevier, vol. 76(2), pages 272-297, October.
    5. Jorgensen, Steffen & Kort, Peter M., 1993. "Optimal dynamic investment policies under concave-convex adjustment costs," Journal of Economic Dynamics and Control, Elsevier, vol. 17(1-2), pages 153-180.
    6. Russell Davidson & Richard Harris, 1981. "Non-Convexities in Continuous Time Investment Theory," Review of Economic Studies, Oxford University Press, vol. 48(2), pages 235-253.
    7. Wirl Franz, 1995. "The Cyclical Exploitation of Renewable Resource Stocks May Be Optimal," Journal of Environmental Economics and Management, Elsevier, vol. 29(2), pages 252-261, September.
    8. Feichtinger, Gustav & Novak, Andreas & Wirl, Franz, 1994. "Limit cycles in intertemporal adjustment models : Theory and applications," Journal of Economic Dynamics and Control, Elsevier, vol. 18(2), pages 353-380, March.
    9. Liski, Matti, 2002. "Taxing average emissions to overcome the shutdown problem," Journal of Public Economics, Elsevier, vol. 85(3), pages 363-384, September.
    10. Clark, Colin W & Clarke, Frank H & Munro, Gordon R, 1979. "The Optimal Exploitation of Renewable Resource Stocks: Problems of Irreversible Investment," Econometrica, Econometric Society, vol. 47(1), pages 25-47, January.
    11. Trond Bjørndal & Jon M. Conrad & Kjell G. Salvanes, 1993. "Stock Size, Harvesting Costs, and the Potential for Extinction: The Case of Sealing," Land Economics, University of Wisconsin Press, vol. 69(2), pages 156-167.
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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques


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