This paper undertakes a Bayesian analysis of optimal monetary policy for the United Kingdom. We estimate a suite of monetary policy models that include both forward and backward-looking representations as well as large and small-scale models. We find an optimal simple Taylor-type rule that...

The goal of this paper is to present the dynamic stochastic general equilibrium (DSGE) model developed and used at the Federal Reserve Bank of New York. The paper describes how the model works, how it is estimated, how it rationalizes past history, including the Great Recession, and how it is...

We model the United States macroeconomic and financial sectors using a formal and unified econometric model. Through shrinkage, our Bayesian VAR provides a flexible framework for modeling the dynamics of thirty-one variables, many of which are tracked by the Federal Reserve. We show how the...

The goal of this paper is to present the dynamic stochastic general equilibrium (DSGE) model developed and used at the Federal Reserve Bank of New York. The paper describes how the model works, how it is estimated, how it rationalizes past history, including the Great Recession, and how it is...

We model the United States macroeconomic and financial sectors using a formal and unified econometric model. Through shrinkage, our Bayesian VAR provides a flexible framework for modeling the dynamics of thirty-one variables, many of which are tracked by the Federal Reserve. We show how the...

The U.S. business cycle expansion that started in March 1991 is the longest on record. This paper uses statistical techniques to examine whether this expansion is a onetime unique event or whether its length is a result of a change in the stability of the U.S. economy. Bayesian methods are used...

Time-varying parameter vector autoregressions (TVP-VARs) have become a popular tool to study the dynamics of macroeconomic time series. In this article, we discuss the specification and estimation of this class of models with a focus on implementability. We provide a step-by-step guide for...

We describe how to use the composite likelihood to ameliorate estimation, computational, and inferential problems in dynamic stochastic general equilibrium models. We present a number of situations where the methodology has the potential to resolve well-known problems. In each case we consider,...