This paper studies the problem of an agent who wants to prevent the state from exceeding a critical threshold. Even though the agent is presumed to know the model, the optimal policy is computed by solving a conventional robust control problem. That is, robustness is induced here by objectives...

This paper studies adaptive learning with multiple models. An agent operating in a self-referential environment is aware of potential model misspecification, and tries to detect it, in real-time, using an econometric specification test. If the current model passes the test, it is used to...

This paper studies exchange rate volatility within the context of the monetary model of exchange rates. We assume that agents regard this model as merely a benchmark, or reference model, and attempt to construct forecasts that are robust to model misspecification. We show that revisions of...

This paper attempts to relate two apparently distinct literatures. One is the so-called `robust control' literature. Robust control methods were developed by engineers during the 1980s, and are designed to make traditional Linear-Quadratic control more robust to model misspecification. Rather...

This paper studies a class of models developed by Townsend (1983) and Sargent (1991). These models feature dynamic signal extraction problems and an infinite regress in expectations. This paper uses frequency domain methods to compute an analytical solution to the fixed point problem posed by...