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

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Author Info
Hans M. Amman ()
David A. Kendrick ()

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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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Publisher 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: Aug 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;

Find related papers by JEL classification:
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - 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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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. 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. [Downloadable!] (restricted)
  2. Wieland, Volker, 2000. "Monetary policy, parameter uncertainty and optimal learning," Journal of Monetary Economics, Elsevier, vol. 46(1), pages 199-228, August. [Downloadable!] (restricted)
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  3. 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.
  4. Hans M. Amman & David A. Kendrick, 1996. "The DUALI/DUALPC Software for Optimal Control Models: Introduction," Economics, University of Texas at Austin 9602, Center for Applied Research in Economics. [Downloadable!]
  5. 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. [Downloadable!] (restricted)
  6. 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. [Downloadable!] (restricted)
  7. Thomas F. Cosimano, 2003. "Optimal Experimentation and the Perturbation Method," Computing in Economics and Finance 2003 71, Society for Computational Economics.
  8. 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. [Downloadable!] (restricted)
  9. 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.). [Downloadable!]
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  10. 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. [Downloadable!] (restricted)
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