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Optimal Control of Nonlinear Dynamic Econometric Models: An Algorithm and an Application

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Author Info

  • Viktoria Blüschke-Nikolaeva
  • Dmitri Blüschke
  • Reinhard Neck

Abstract

In this paper, we present a new version of the OPTCON algorithm for the optimal control of nonlinear stochastic systems with special reference to econometric models. It delivers approximate numerical solutions of 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. We demonstrate the applicability of the algorithm by experiments with a small quarterly macroeconometric model for Slovenia. This shows 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://comisef.eu/files/wps032.pdf
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Bibliographic Info

Paper provided by COMISEF in its series Working Papers with number 032.

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Length: 48 pages
Date of creation: 22 Feb 2010
Date of revision:
Handle: RePEc:com:wpaper:032

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Web page: http://www.comisef.eu

Related research

Keywords: Optimal control; Stochastic control; Algorithms; Econometric modeling; Policy applications;

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References

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  1. Hans M. Amman & David A. Kendrick, 1997. "Should Macroeconomic Policy Makers Consider Parameter Covariances?," CARE Working Papers 9701, The University of Texas at Austin, Center for Applied Research in Economics.
  2. Hans M. Amman & David A. Kendrick, 2003. "A Classification System for Economic Stochastic Control Models," Computing in Economics and Finance 2003 114, Society for Computational Economics.
  3. 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.
  4. 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.
  5. Neck, Reinhard, 1984. "Stochastic control theory and operational research," European Journal of Operational Research, Elsevier, vol. 17(3), pages 283-301, September.
  6. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, Spring.
  7. 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.
  8. 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.
  9. 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.
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Citations

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Cited by:
  1. Blueschke, D. & Blueschke-Nikolaeva, V. & Savin, I., 2013. "New insights into optimal control of nonlinear dynamic econometric models: Application of a heuristic approach," Journal of Economic Dynamics and Control, Elsevier, vol. 37(4), pages 821-837.
  2. Reinhard Neck & Dmitri Blueschke & Klaus Weyerstrass, 2011. "Optimal macroeconomic policies in a financial and economic crisis: a case study for Slovenia," Empirica, Springer, vol. 38(3), pages 435-459, July.
  3. D. Blueschke & V. Blueschke-Nikolaeva & R. Neck, 2013. "Stochastic Control of Linear and Nonlinear Econometric Models: Some Computational Aspects," Computational Economics, Society for Computational Economics, vol. 42(1), pages 107-118, June.
  4. Ivan Savin & Dmitri Blueschke, 2013. "Solving nonlinear stochastic optimal control problems using evolutionary heuristic optimization," Jena Economic Research Papers 2013-051, Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics.

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