We examine optimal and other monetary policies in a linear-quadratic setup with a relatively general form of model uncertainty, so-called Markov jump-linear-quadratic systems extended to include forward-looking variables. The form of model uncertainty our framework encompasses includes: simple...

We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG...

We develop analytic asymptotic methods to characterize time series properties of nonlinear dynamic stochastic models. We focus on a stochastic growth model which is representative of the models underlying much of modern macroeconomics. Taking limits as the stochastic shocks become small, we...

We use a Bayesian Markov Chain Monte Carlo algorithm to estimate a model that allows temporary gaps between a true expectational Phillips curve and the monetary authority's approximating non-expectational Phillips curve. A dynamic programming problem implies that the monetary authority's...

Recently there has been much interest in studying monetary policy under model uncertainty. We develop methods to analyze different sources of uncertainty in one coherent structure useful for policy decisions. We show how to estimate the size of the uncertainty based on time series data, and...

Recently there has been a great deal of interest in studying monetary policy under model uncertainty. We point out that different assumptions about the uncertainty may result in drastically different robust' policy recommendations. Therefore, we develop new methods to analyze uncertainty about...

We study the properties of generalized stochastic gradient (GSG) learning in forward-looking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG...

The authors study the design of optimal unemployment insurance in an environment with moral hazard and cyclical fluctuations. The optimal unemployment insurance contract balances the insurance motive to provide consumption for the unemployed with the provision of incentives to search for a job....