The development of tractable forward looking models of monetary policy has lead to an explosion of research on the implications of adopting Taylor-type interest rate rules. Indeterminacies have been found to arise for some specifications of the interest rate rule, raising the possibility of...

Earlier studies of the seigniorage inflation model have found that the high-inflation steady state is not stable under adaptive learning. We reconsider this issue and analyze the full set of solutions for the linearized model. Our main focus is on stationary hyperinflationary paths near the...

We investigate both the rational explosive inflation paths studied by McCallum (2001) and the classification of fiscal and monetary policies proposed by Leeper (1991) for stability under learning of rational expectations equilibria (REE).Our first result is that the fiscalist REE in the model of...

We review the recent work on interest rate setting, which emphasizes the desirability of designing policy to ensure stability under private agent learning.Appropriately designed expectations based rules can yield optimal rational expectations equilibria that are both determinate and stable under...

We consider the robust stability of a rational expectations equilibrium, which we define as stability under discounted (constant gain) least-squares learning, for a range of gain parameters. We find that for operational forms of policy rules, ie rules that do not depend on contemporaneous values...

Expectations about the future are central for determination of current macroeconomic outcomes and the formulation of monetary policy. Recent literature has explored ways for supplementing the benchmark of rational expectations with explicit models of expectations formation that rely on...

Stagnation as the new norm and fiscal policy are examined in a New Keynesian model with adaptive learning determining expectations. We impose inflation and consumption lower bounds, which can be relevant when agents are pessimistic. The inflation target is locally stable under learning....