The authors 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 nonexpectational Phillips curve. A dynamic programming problem implies that the monetary...

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 integrate the housing market and the labor market in a dynamic general equilibrium model with credit and search frictions. The model is confronted with the U.S. macroeconomic time series. Our estimated model can account for two prominent facts observed in the data. First, the land price and...

Although macroeconomic forecasting forms an integral part of the policymaking process, there has been a serious lack of rigorous and systematic research in the evaluation of out-of-sample modelbased forecasts of China's real gross domestic product (GDP) growth and consumer price index inflation....