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

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

Abstract

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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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

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

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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.
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  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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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. D. Blueschke & I. Savin, 2017. "No such thing as a perfect hammer: comparing different objective function specifications for optimal control," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(2), pages 377-392, June.
  3. Reinhard Neck & Dmitri Blueschke & Klaus Weyerstrass, 2011. "Optimal macroeconomic policies in a financial and economic crisis: a case study for Slovenia," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 38(3), pages 435-459, July.
  4. Herrmann, Johannes & Savin, Ivan, 2015. "Evolution of the electricity market in Germany: Identifying policy implications by an agent-based model," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112959, Verein für Socialpolitik / German Economic Association.
  5. Johannes Herrmann & Ivan Savin, 2016. "Optimal Policy Identification: Insights from the German Electricity Market," Working Papers of BETA 2016-16, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
  6. Dmitri Blueschke & Ivan Savin, 2015. "No such thing like perfect hammer: comparing different objective function specifications for optimal control," Jena Economic Research Papers 2015-005, Friedrich-Schiller-University Jena.
  7. 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.
  8. Ivan Savin & Dmitri Blueschke, 2016. "Lost in Translation: Explicitly Solving Nonlinear Stochastic Optimal Control Problems Using the Median Objective Value," Computational Economics, Springer;Society for Computational Economics, vol. 48(2), pages 317-338, August.
  9. D. Blueschke & V. Blueschke-Nikolaeva & R. Neck, 2013. "Stochastic Control of Linear and Nonlinear Econometric Models: Some Computational Aspects," Computational Economics, Springer;Society for Computational Economics, vol. 42(1), pages 107-118, June.

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