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Tractable Bayesian Optimal Policy in the Presence of Regime Change and Local Parameter Uncertainty

This paper proposes an approximation to the optimal policy problem in forward-looking models with regime change that allows for tractable solution for the optimal policy even when the parameters of the model and the current regime are not known with certainty. The linear-quadratic framework is adhered to as much as possible, with a particular emphasis on maintaining the property of separation of estimation and control, which is crucial for maintaining tractability. Generality is achieved by allowing for full Bayesian updating of all aspects of the model, including model parameters, the probability that a regime change has occurred, and the values of all (generally non-normal) shocks hitting the model each period. The methods of this paper provide results that can be quite useful in practice—for example, they fit the policy behavior of the Federal Reserve in the late 1990s very well, which standard methods fail to do.