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

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  • 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.

Suggested Citation

  • Viktoria Blüschke-Nikolaeva & Dmitri Blüschke & Reinhard Neck, 2010. "Optimal Control of Nonlinear Dynamic Econometric Models: An Algorithm and an Application," Working Papers 032, COMISEF.
    • Handle: RePEc:com:wpaper:032
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    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.
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    10. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1.
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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. Herrmann, J.K. & Savin, I., 2017. "Optimal policy identification: Insights from the German electricity market," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 71-90.
    3. 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.
    4. 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.
    5. Herrmann, Johannes & Savin, Ivan, 2015. "Evolution of the electricity market in Germany: Identifying policy implications by an agent-based model," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112959, Verein für Socialpolitik / German Economic Association.
    6. Dmitri Blueschke & Klaus Weyerstrass & Reinhard Neck, 2024. "How can the preferences of policy makers be operationalised in optimum control problems with macroeconometric models? A case study for Slovenian fiscal policies," Public Sector Economics, Institute of Public Finance, vol. 48(2), pages 151-169.
    7. Reinhard Neck & Sohbet Karbuz, 2017. "Dynamic Optimization under Uncertainty: A Case Study for Austrian Macroeconomic Policies," Proceedings of International Academic Conferences 5808250, International Institute of Social and Economic Sciences.
    8. D. Blueschke & I. Savin & V. Blueschke-Nikolaeva, 2020. "An Evolutionary Approach to Passive Learning in Optimal Control Problems," Computational Economics, Springer;Society for Computational Economics, vol. 56(3), pages 659-673, October.
    9. Dmitri Blueschke & Ivan Savin, 2015. "No such thing like perfect hammer: comparing different objective function specifications for optimal control," Jena Economics Research Papers 2015-005, Friedrich-Schiller-University Jena.
    10. V. Blueschke-Nikolaeva & D. Blueschke & R. Neck, 2020. "OPTCON3: An Active Learning Control Algorithm for Nonlinear Quadratic Stochastic Problems," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 145-162, June.
    11. Ivan Savin & Dmitri Blueschke, 2013. "Solving nonlinear stochastic optimal control problems using evolutionary heuristic optimization," Jena Economics Research Papers 2013-051, Friedrich-Schiller-University Jena.
    12. 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.
    13. 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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    Keywords

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