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Comparison of policy functions from the optimal learning and adaptive control frameworks

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  • Hans Amman
  • David Kendrick

Abstract

In this paper we turn our attention to comparing the policy function obtained by Beck and Wieland (J Econ Dyn Control 26:1359–1377, 2002 ) to the one obtained with adaptive control methods. It is an integral part of the optimal learning method used by Beck and Wieland to obtain a policy function that provides the optimal control as a feedback function of the state of the system. However, computing this function is not necessary when doing Monte Carlo experiments with adaptive control methods. Therefore, we have modified our software in order to obtain the policy function for comparison to the BW results. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Hans Amman & David Kendrick, 2014. "Comparison of policy functions from the optimal learning and adaptive control frameworks," Computational Management Science, Springer, vol. 11(3), pages 221-235, July.
    • Handle: RePEc:spr:comgts:v:11:y:2014:i:3:p:221-235
    DOI: 10.1007/s10287-014-0215-9
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    References listed on IDEAS

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    1. Wieland, Volker, 2000. "Learning by doing and the value of optimal experimentation," Journal of Economic Dynamics and Control, Elsevier, vol. 24(4), pages 501-534, April.
    2. 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.
    3. Hans M. Amman & David A. Kendrick, 1996. "The DUALI/DUALPC Software for Optimal Control Models: Introduction," CARE Working Papers 9602, The University of Texas at Austin, Center for Applied Research in Economics.
    4. Beck, Gunter W. & Wieland, Volker, 2002. "Learning and control in a changing economic environment," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1359-1377, August.
    5. H. M. Amman & D. A. Kendrick & J. Rust (ed.), 1996. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 1, number 1.
    6. Cosimano, Thomas F., 2008. "Optimal experimentation and the perturbation method in the neighborhood of the augmented linear regulator problem," Journal of Economic Dynamics and Control, Elsevier, vol. 32(6), pages 1857-1894, June.
    7. Tucci, Marco P. & Kendrick, David A. & Amman, Hans M., 2010. "The parameter set in an adaptive control Monte Carlo experiment: Some considerations," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1531-1549, September.
    8. Wieland, Volker, 2000. "Monetary policy, parameter uncertainty and optimal learning," Journal of Monetary Economics, Elsevier, vol. 46(1), pages 199-228, August.
    9. Gunter Coenen, Volker Wieland, Andrew Levin, 2001. "Evaluating Information Variables for Monetary Policy in a Noisy Economic Environment," Computing in Economics and Finance 2001 131, Society for Computational Economics.
    10. Taylor, John B, 1974. "Asymptotic Properties of Multiperiod Control Rules in the Linear Regression Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(2), pages 472-484, June.
    11. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1.
    12. Kiefer, Nicholas M., 1989. "A value function arising in the economics of information," Journal of Economic Dynamics and Control, Elsevier, vol. 13(2), pages 201-223, April.
    13. Amman, Hans M & Kendrick, David A, 1995. "Nonconvexities in Stochastic Control Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 36(2), pages 455-475, May.
    14. Prescott, Edward C, 1972. "The Multi-Period Control Problem Under Uncertainty," Econometrica, Econometric Society, vol. 40(6), pages 1043-1058, November.
    15. Thomas F. Cosimano, 2003. "Optimal Experimentation and the Perturbation Method," Computing in Economics and Finance 2003 71, Society for Computational Economics.
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    1. George E. Halkos & Kyriaki D. Tsilika, 2021. "Towards Better Computational Tools for Effective Environmental Policy Planning," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 555-572, October.
    2. Amman, Hans M. & Kendrick, David A. & Tucci, Marco P., 2020. "Approximating The Value Function For Optimal Experimentation," Macroeconomic Dynamics, Cambridge University Press, vol. 24(5), pages 1073-1086, July.
    3. H.M. Amman & D.A. Kendrick, 2012. "Conjectures on the policy function in the presence of optimal experimentation," Working Papers 12-09, Utrecht School of Economics.

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    More about this item

    Keywords

    Active learning; Dual control; Optimal experimentation; Stochastic optimization; Time-varying parameters; Numerical experiments; C63; E61;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination

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