Currently, there is a renewed interest in the use of optimal experimentation (adaptive control) in economics. Example are found in [Amman, H. M. & Kendrick, D. A. (1999). Should macroeconomic policy makers consider parameter covariances. Computational Economics 14, 263– 267; Amman, H. M. & Kendrick, D. A. (2003). Mitigation of the Lucas critique with stochastic control methods. Journal of Economic Dynamics and Control 27, 2035–2057; Cosimano, T. F. Optimal experimentation and the perturbation method in the neighborhood of the augmented linear regulator problem. Journal of Economics, Dynamics and Control (in press); Cosimano, T. F., & Gapen, M. T. (2005b). Recursive methods of dynamic linear economics and optimal experimentation using the perturbation method, Working paper. Notre Dame, Indiana, USA: Department of Finance, University of Notre Dame; Cosimano, T. F., & Gapen, M. T. (2005a). Program notes for optimal experimentation and the perturbation method in the neighborhood of the augmented linear regulator problem, Working paper. Notre Dame, Indiana, USA: Department of Finance, University of Notre Dame; Cosimano, T. F., & Gapen, M. T. (2006). An algorithm for approximating optimal experimentation problems using the perturbation method, Working paper. Notre Dame, Indiana, USA: Department of Finance, University of Notre Dame; Tesfaselassie, M. F., Schaling, E., & Eijffinger, S. (2007). Learning about the term structure and optimal rules for inflation targeting, Working paper. Tilburg, The Netherlands: Tilburg University; Tucci, M. P. (1997). Adaptive control in the presence of time-varying parameters. Journal of Economic Dynamics and Control, 22, 39–47; Wieland, V. (2000a). Learning by doing and the value of optimal experimentation. Journal of Economic Dynamics and Control, 24, 501–543; Wieland, V., (2000b). Monetary policy, parameter uncertainty and optimal learning. Journal of Monetary Economics, 46, 199–228]. In this paper we present the Beck & Wieland model [Beck, G., & Wieland, V. (2002). Learning and control in a changing economic environment. Journal of Economic Dynamics and Control, 26, 1359–1378] and the methodology to solve this model with time-varying parameters using the various control methods described in [Kendrick, D. A. (1981). Stochastic control for economic models (1st ed.), New York, NY, USA: McGraw-Hill Book Company; Kendrick, D. A. (2002). Stochastic control for economic models (2nd ed.) Available at url: http://www.eco.utexas.edu/faculty/Kendrick]. Furthermore, we also provide numerical results using the DualPC software [Amman, H. M., & Kendrick, D. A. (1999). The DualI/DualPC software for optimal control models: User’s guide. Working paper, Austin, TX 78712, USA: Center for Applied Research in Economics, University of Texas] and show first evidence that optimal experimentation or Dual Control may produce better results than Expected Optimal Feedback.

Tucci, M.P., Amman, H.M., Kendrick, D.A. (2008). Solving the Beck and Wieland Model with Optimal Experimentation in DualPC. AUTOMATICA, 44, 1504-1510.

Solving the Beck and Wieland Model with Optimal Experimentation in DualPC

TUCCI, MARCO PAOLO;
2008-01-01

Abstract

Currently, there is a renewed interest in the use of optimal experimentation (adaptive control) in economics. Example are found in [Amman, H. M. & Kendrick, D. A. (1999). Should macroeconomic policy makers consider parameter covariances. Computational Economics 14, 263– 267; Amman, H. M. & Kendrick, D. A. (2003). Mitigation of the Lucas critique with stochastic control methods. Journal of Economic Dynamics and Control 27, 2035–2057; Cosimano, T. F. Optimal experimentation and the perturbation method in the neighborhood of the augmented linear regulator problem. Journal of Economics, Dynamics and Control (in press); Cosimano, T. F., & Gapen, M. T. (2005b). Recursive methods of dynamic linear economics and optimal experimentation using the perturbation method, Working paper. Notre Dame, Indiana, USA: Department of Finance, University of Notre Dame; Cosimano, T. F., & Gapen, M. T. (2005a). Program notes for optimal experimentation and the perturbation method in the neighborhood of the augmented linear regulator problem, Working paper. Notre Dame, Indiana, USA: Department of Finance, University of Notre Dame; Cosimano, T. F., & Gapen, M. T. (2006). An algorithm for approximating optimal experimentation problems using the perturbation method, Working paper. Notre Dame, Indiana, USA: Department of Finance, University of Notre Dame; Tesfaselassie, M. F., Schaling, E., & Eijffinger, S. (2007). Learning about the term structure and optimal rules for inflation targeting, Working paper. Tilburg, The Netherlands: Tilburg University; Tucci, M. P. (1997). Adaptive control in the presence of time-varying parameters. Journal of Economic Dynamics and Control, 22, 39–47; Wieland, V. (2000a). Learning by doing and the value of optimal experimentation. Journal of Economic Dynamics and Control, 24, 501–543; Wieland, V., (2000b). Monetary policy, parameter uncertainty and optimal learning. Journal of Monetary Economics, 46, 199–228]. In this paper we present the Beck & Wieland model [Beck, G., & Wieland, V. (2002). Learning and control in a changing economic environment. Journal of Economic Dynamics and Control, 26, 1359–1378] and the methodology to solve this model with time-varying parameters using the various control methods described in [Kendrick, D. A. (1981). Stochastic control for economic models (1st ed.), New York, NY, USA: McGraw-Hill Book Company; Kendrick, D. A. (2002). Stochastic control for economic models (2nd ed.) Available at url: http://www.eco.utexas.edu/faculty/Kendrick]. Furthermore, we also provide numerical results using the DualPC software [Amman, H. M., & Kendrick, D. A. (1999). The DualI/DualPC software for optimal control models: User’s guide. Working paper, Austin, TX 78712, USA: Center for Applied Research in Economics, University of Texas] and show first evidence that optimal experimentation or Dual Control may produce better results than Expected Optimal Feedback.
2008
Tucci, M.P., Amman, H.M., Kendrick, D.A. (2008). Solving the Beck and Wieland Model with Optimal Experimentation in DualPC. AUTOMATICA, 44, 1504-1510.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11365/17708
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