In a Markov decision problem with hidden state variables, a posterior distribution serves as a state variable and Bayes' law under an approximating model gives its law of motion. A decision maker expresses fear that his model is misspecified by surrounding it with a set of alternatives that are...

This paper describes methods for conveniently formulating and estimating dynamic linear econometric models under the hypothesis of rational expectations. An econometrically convenient formula for the cross-equation rational expectations restrictions is derived. Models of error terms and the role...

This paper shows how the cross-equation restrictions delivered by the hypothesis of rational expectations can serve to solve the aliasing identification problem. It is shown how the rational expectations restrictions uniquely identify the parameters of a continuous time model from statistics of...

This paper describes methods for estimating the parameters of continuous time linear stochastic rational expectations models from discrete time observations. The economic models that we study are continuous time, multiple variable, stochastic, linear-quadratic rational expectations models. The...

This paper describes how to specify and estimate rational expectations models in which there are exact linear relationships among variables and expectations of variables that the econometrician observes.

This paper catalogues formulas that are useful for estimating dynamic linear economic models. We describe algorithms for computing equilibria of an economic model and for recursively computing a Gaussian likelihood function and its gradient with respect to parameters. We apply these methods to...

This paper shows how the cross-equation restrictions implied by dynamic rational expectations models can be used to resolve the aliasing identification problem. Using a continuous time, linear-quadratic optimization environment, this paper describes how the resulting restrictions are sufficient...