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Optimal control of nonlinear dynamic econometric models: An algorithm and an application

Listed author(s):
  • Blueschke-Nikolaeva, V.
  • Blueschke, D.
  • Neck, R.

OPTCON is an algorithm for the optimal control of nonlinear stochastic systems which is particularly applicable to econometric models. It delivers approximate numerical solutions to optimum control problems with a quadratic objective function for nonlinear econometric models with additive and multiplicative (parameter) uncertainties. The algorithm was programmed in C# and allows for deterministic and stochastic control, the latter with open-loop and passive learning (open-loop feedback) information patterns. The applicability of the algorithm is demonstrated by experiments with a small quarterly macroeconometric model for Slovenia. This illustrates the convergence and the practical usefulness of the algorithm and (in most cases) the superiority of open-loop feedback over open-loop controls.

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File URL: http://www.sciencedirect.com/science/article/pii/S0167947311001095
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Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 56 (2012)
Issue (Month): 11 ()
Pages: 3230-3240

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Handle: RePEc:eee:csdana:v:56:y:2012:i:11:p:3230-3240
DOI: 10.1016/j.csda.2010.10.030
Contact details of provider: Web page: http://www.elsevier.com/locate/csda

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  1. Lyra, M. & Paha, J. & Paterlini, S. & Winker, P., 2010. "Optimization heuristics for determining internal rating grading scales," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2693-2706, November.
  2. Neck, Reinhard, 1984. "Stochastic control theory and operational research," European Journal of Operational Research, Elsevier, vol. 17(3), pages 283-301, September.
  3. Chen, Baoline & Zadrozny, Peter A., 2009. "Multi-step perturbation solution of nonlinear differentiable equations applied to an econometric analysis of productivity," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2061-2074, April.
  4. Amman, Hans M & Kendrick, David A, 1999. "Should Macroeconomic Policy Makers Consider Parameter Covariances?," Computational Economics, Springer;Society for Computational Economics, vol. 14(3), pages 263-267, December.
  5. Amman, Hans, 1996. "Numerical methods for linear-quadratic models," Handbook of Computational Economics,in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 13, pages 587-618 Elsevier.
  6. Coomes, Paul A., 1987. "PLEM: A computer program for passive learning, stochastic control experiments," Journal of Economic Dynamics and Control, Elsevier, vol. 11(2), pages 223-227, June.
  7. David Kendrick & Hans Amman, 2006. "A Classification System for Economic Stochastic Control Models," Computational Economics, Springer;Society for Computational Economics, vol. 27(4), pages 453-481, June.
  8. Winker, Peter & Gilli, Manfred, 2004. "Applications of optimization heuristics to estimation and modelling problems," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 211-223, September.
  9. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1.
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