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Comparison of Policy Functions from the Optimal Learning and Adaptive Control Frameworks

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  • D.A. Kendrick
  • H.M. Amman

Abstract

In this paper we turn our attention to comparing the policy function obtained by Beck and Wieland (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.

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File URL: http://dspace.library.uu.nl/bitstream/handle/1874/31430/08-19.pdf
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Bibliographic Info

Paper provided by Utrecht School of Economics in its series Working Papers with number 08-19.

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Length: 19 pages
Date of creation: 2008
Date of revision:
Handle: RePEc:use:tkiwps:0819

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Related research

Keywords: Active learning; dual control; optimal experimentation; stochastic optimization; time-varying parameters; numerical experiments;

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References

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  1. 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.
  2. 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 455-75, May.
  3. 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 472-84, June.
  4. 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.
  5. Hans M. Amman & David A. Kendrick, . "Computational Economics," Online economics textbooks, SUNY-Oswego, Department of Economics, number comp1, Spring.
  6. Wieland, Volker, 1999. "Monetary policy, parameter uncertainty and optimal learning," ZEI Working Papers B 09-1999, ZEI - Center for European Integration Studies, University of Bonn.
  7. 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.
  8. 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.
  9. Thomas F. Cosimano, 2003. "Optimal Experimentation and the Perturbation Method," Computing in Economics and Finance 2003 71, Society for Computational Economics.
  10. Marco P. Tucci & David A. Kendrick & Hans M. Amman, 2007. "The Parameter Set in an Adaptive Control Monte Carlo Experiment: Some Considerations," Department of Economics University of Siena 507, Department of Economics, University of Siena.
  11. 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.
  12. Volker Wieland, 1996. "Learning by doing and the value of optimal experimentation," Finance and Economics Discussion Series 96-5, Board of Governors of the Federal Reserve System (U.S.).
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