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Conjectures on the policy function in the presence of optimal experimentation

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  • H.M. Amman
  • D.A. Kendrick

Abstract

In the economics literature there are two dominant approaches for solving models with optimal experimentation (also called active learning). The first approach is based on the value function and the second on an approximation method. In principle the value function approach is the preferred method. However, it suffers from the curse of dimensionality and is only applicable to small problems with a limited number of policy variables. The approximation method allows for a computationally larger class of models, but may produce results that deviate from the optimal solution. Our simulations indicate that when the effects of learning are limited, the differences may be small. However, when there is sufficient scope for learning, the value function solution is more aggressive in the use of the policy variable.

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Bibliographic Info

Paper provided by Utrecht School of Economics in its series Working Papers with number 12-09.

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Length: 21 pages
Date of creation: 2012
Date of revision:
Handle: RePEc:use:tkiwps:1209

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Related research

Keywords: design of fiscal policy; optimal experimentation; stochastic optimization; time-varying parameters; numerical experiments;

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References

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  1. Amman, Hans M. & Kendrick, David A., 2003. "Mitigation of the Lucas critique with stochastic control methods," Journal of Economic Dynamics and Control, Elsevier, vol. 27(11-12), pages 2035-2057, September.
  2. 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.
  3. Prescott, Edward C, 1972. "The Multi-Period Control Problem Under Uncertainty," Econometrica, Econometric Society, vol. 40(6), pages 1043-58, November.
  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. Williams, John C. & Levin, Andrew T. & Wieland, Volker, 2001. "The performance of forecast-based monetary policy rules under model uncertainty," Working Paper Series 0068, European Central Bank.
  6. Coenen, Günter & Levin, Andrew & Wieland, Volker, 2003. "Data Uncertainty and the Role of Money as an Information Variable for Monetary Policy," CEPR Discussion Papers 3812, C.E.P.R. Discussion Papers.
  7. Aghion Philippe & Bolton, Patrick & Harris Christopher & Jullien Bruno, 1991. "Optimal learning by experimentation," CEPREMAP Working Papers (Couverture Orange) 9104, CEPREMAP.
  8. Giuseppe Moscarini & Lones Smith, 2001. "The Optimal Level of Experimentation," Econometrica, Econometric Society, vol. 69(6), pages 1629-1644, November.
  9. Kiefer, Nicholas M & Nyarko, Yaw, 1989. "Optimal Control of an Unknown Linear Process with Learning," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 30(3), pages 571-86, August.
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  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 455-75, May.
  14. 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.
  15. Francisco J. Buera & Alexander Monge-Naranjo & Giorgio E. Primiceri, 2008. "Learning the Wealth of Nations," NBER Working Papers 14595, National Bureau of Economic Research, Inc.
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  17. 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.
  18. Kendrick, David, 1978. "Non-convexities from probing in adaptive control problems," Economics Letters, Elsevier, vol. 1(4), pages 347-351.
  19. Elizabeth Chase MacRae, 1972. "Linear Decision with Experimentation," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 1, number 4, pages 435-445 National Bureau of Economic Research, Inc.
  20. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
  21. Aghion, Philippe, et al, 1991. "Optimal Learning by Experimentation," Review of Economic Studies, Wiley Blackwell, vol. 58(4), pages 621-54, July.
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