Stochastic Policy Design for Models with Rational Expectations and Time-Varying Parameters
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 (1996) to solve the rational-expectations part of the model. Parameter updating is done with a Kalman filter, and the optimal control is calculated using the variances and covariances of the uncertain time-varying parameter.
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|Date of creation:||01 Mar 1999|
|Date of revision:|
|Contact details of provider:|| Postal: CEF99, Boston College, Department of Economics, Chestnut Hill MA 02467 USA|
Web page: http://fmwww.bc.edu/CEF99/
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