Author: Amman H.M. Kendrick D.A.
Publisher: Springer Publishing Company
ISSN: 0022-3239
Source: Journal of Optimization Theory and Applications, Vol.105, Iss.3, 2000-06, pp. : 509-520
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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.
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