Numerical Steady State Solutions for Nonlinear Dynamic Optimization Models
AbstractNonlinear 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.
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Bibliographic InfoPaper provided by The University of Texas at Austin, Center for Applied Research in Economics in its series CARE Working Papers with number 9503.
Length: 26 pages
Date of creation: Jul 1994
Date of revision:
Other versions of this item:
- Hans M. Amman & David A. Kendrick & Heinz Neudecker, . "Numerical Steady State Solutions for Nonlinear Dynamic Optimization Models," Computing in Economics and Finance 1996 _003, Society for Computational Economics.
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- Arnulfo Rodriguez, 2004. "Robust Control: A Note on the Timing of Model Uncertainty," Computational Economics, Society for Computational Economics, vol. 24(3), pages 209-221, July.
- Amman, Hans M. & Kendrick, David A., 1998.
"Computing the steady state of linear quadratic optimization models with rational expectations,"
Elsevier, vol. 58(2), pages 185-191, February.
- Hans M. Amman & David A. Kendrick, 1997. "Computing the Steady State of Linear Quadratic Optimization Models with Rational Expectations," CARE Working Papers 9707, The University of Texas at Austin, Center for Applied Research in Economics.
- P. Mercado & David Kendrick, 2006. "Parameter Uncertainty and Policy Intensity: Some Extensions and Suggestions for Further Work," Computational Economics, Society for Computational Economics, vol. 27(4), pages 483-496, June.
- Arnulfo Rodriguez, 2004. "Robust Control: A Note on the Timing of Model Uncertainty," Computing in Economics and Finance 2004 147, Society for Computational Economics.
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