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Stochastic Policy Design in a Learning Environment with Rational Expectations

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  • H. M. Amman

    (University of Amsterdam)

  • D. A. Kendrick

    (University of Texas at Austin)

Abstract

In this paper, we present a method for using rational expectations in a stochastic linear-quadratic optimization framework in which the unknown parameters are updated through a learning scheme. We use the QZ decomposition as suggested by Sims (Ref. 1) to solve the rational expectations part of the model. The parameter updating is done with the Kalman filter and the optimal control is calculated using the covariance matrix of the uncertain parameter.

Suggested Citation

  • H. M. Amman & D. A. Kendrick, 2000. "Stochastic Policy Design in a Learning Environment with Rational Expectations," Journal of Optimization Theory and Applications, Springer, vol. 105(3), pages 509-520, June.
    • Handle: RePEc:spr:joptap:v:105:y:2000:i:3:d:10.1023_a:1004620021587
    DOI: 10.1023/A:1004620021587
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    References listed on IDEAS

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    1. Amman, Hans M. & Kendrick, David A., 1998. "Computing the steady state of linear quadratic optimization models with rational expectations," Economics Letters, Elsevier, vol. 58(2), pages 185-191, February.
    2. Marcet, Albert & Sargent, Thomas J., 1989. "Convergence of least squares learning mechanisms in self-referential linear stochastic models," Journal of Economic Theory, Elsevier, vol. 48(2), pages 337-368, August.
    3. Blanchard, Olivier Jean & Kahn, Charles M, 1980. "The Solution of Linear Difference Models under Rational Expectations," Econometrica, Econometric Society, vol. 48(5), pages 1305-1311, July.
    4. Amman, Hans & Kendrick, David, 1999. "Linear-Quadratic Optimization For Models With Rational Expectations," Macroeconomic Dynamics, Cambridge University Press, vol. 3(4), pages 534-543, December.
    5. Anderson, Gary & Moore, George, 1985. "A linear algebraic procedure for solving linear perfect foresight models," Economics Letters, Elsevier, vol. 17(3), pages 247-252.
    6. Fisher, P. G. & Holly, S. & Hughes Hallett, A. J., 1986. "Efficient solution techniques for dynamic non-linear rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 10(1-2), pages 139-145, June.
    7. Prescott, Edward C, 1972. "The Multi-Period Control Problem Under Uncertainty," Econometrica, Econometric Society, vol. 40(6), pages 1043-1058, November.
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    Cited by:

    1. Blueschke-Nikolaeva, V. & Blueschke, D. & Neck, R., 2012. "Optimal control of nonlinear dynamic econometric models: An algorithm and an application," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3230-3240.
    2. David Kendrick & Hans Amman, 2006. "A Classification System for Economic Stochastic Control Models," Computational Economics, Springer;Society for Computational Economics, vol. 27(4), pages 453-481, June.
    3. Kwang Mong Sim, 2023. "An Incentive-Compatible and Computationally Efficient Fog Bargaining Mechanism," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1883-1918, December.
    4. Kendrick, David A., 2005. "Stochastic control for economic models: past, present and the paths ahead," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 3-30, January.
    5. 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.
    6. George Halkos & Georgia Argyropoulou, 2021. "Pollution and Health Effects: A Nonparametric Approach," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 691-714, October.
    7. D. Blueschke & V. Blueschke-Nikolaeva & R. Neck, 2013. "Stochastic Control of Linear and Nonlinear Econometric Models: Some Computational Aspects," Computational Economics, Springer;Society for Computational Economics, vol. 42(1), pages 107-118, June.

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