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Numerical Steady State Solutions for Nonlinear Dynamic Optimization Models

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  • Hans M. Amman

    (University of Amsterdam)

  • David A. Kendrick

    (University of Texas)

  • Heinz Neudecker

    (University of Amsterdam)

Abstract

Nonlinear dynamic optimization models are widely used in theoretical and empirical economic modeling, especially in the field of optimal growth and intertemporal macroeconomic modeling. In this paper we present a sequential quadratic programming algorithm for computing directly the steady state solution for a wide class of nonlinear dynamic optimization problems in discrete time.

Suggested Citation

  • Hans M. Amman & David A. Kendrick & Heinz Neudecker, "undated". "Numerical Steady State Solutions for Nonlinear Dynamic Optimization Models," Computing in Economics and Finance 1996 _003, Society for Computational Economics.
    • Handle: RePEc:sce:scecf6:_003
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    Cited by:

    1. Arnulfo Rodriguez, 2004. "Robust Control: A Note on the Timing of Model Uncertainty," Computing in Economics and Finance 2004 147, Society for Computational Economics.
    2. Amman, Hans M. & Kendrick, David A., 1998. "Computing the steady state of linear quadratic optimization models with rational expectations," Economics Letters, Elsevier, vol. 58(2), pages 185-191, February.
    3. George Halkos & Kyriaki Tsilika, 2016. "Dynamic Input–Output Models in Environmental Problems: A Computational Approach with CAS Software," Computational Economics, Springer;Society for Computational Economics, vol. 47(3), pages 489-497, March.
    4. P. Mercado & David Kendrick, 2006. "Parameter Uncertainty and Policy Intensity: Some Extensions and Suggestions for Further Work," Computational Economics, Springer;Society for Computational Economics, vol. 27(4), pages 483-496, June.
    5. Arnulfo Rodriguez, 2004. "Robust Control: A Note on the Timing of Model Uncertainty," Computational Economics, Springer;Society for Computational Economics, vol. 24(3), pages 209-221, July.

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