Recent studies attempt to quantify the empirical importance of news shocks (ie., anticipated future shocks) in business cycle fluctuations. This paper identifies news shocks in a dynamic stochastic general equilibrium model estimated with not only actual data but also forecast data. The...

Large Bayesian VARs with stochastic volatility are increasingly used in empirical macroeconomics. The key to make these highly parameterized VARs useful is the use of shrinkage priors. We develop a family of priors that captures the best features of two prominent classes of shrinkage priors:...

We introduce a new class of stochastic volatility models with autoregressive moving average (ARMA) innovations. The conditional mean process has a flexible form that can accommodate both a state space representation and a conventional dynamic regression. The ARMA component introduces serial...

Adding multivariate stochastic volatility of a flexible form to large Vector Autoregressions (VARs) involving over a hundred variables has proved challenging due to computational considerations and over-parameterization concerns. The existing literature either works with homoskedastic models or...

Vector autoregressions combined with Minnesota-type priors are widely used for macroeconomic forecasting. The fact that strong but sensible priors can substantially improve forecast performance implies VAR forecasts are sensitive to prior hyperparameters. But the nature of this sensitivity is...

We examine whether knowledge of in-sample co-movement across countries can be used in a more systematic way to improve forecast accuracy at the national level. In particular, we ask if a model with common international business cycle factors adds marginal predictive power compared to a domestic...

This paper studies the joint dynamics of U.S. inflation and the average inflation predictions of the Survey of Professional Forecasters (SPF) on a sample running from 1968Q4 to 2014Q2. The joint data generating process (DGP) of these data consists of the unobserved components (UC) model of Stock...

This paper constructs a monthly real-time oil price dataset using backcasting and compares the forecast performance of alternative models of constant and time-varying volatility based on the accuracy of point and density forecasts of real oil prices of both real-time and ex-post revised data....

This paper studies the joint dynamics of real time U.S. inflation and the mean inflation predictions of the Survey of Professional Forecasters (SPF) on a 1968Q4 to 2017Q2 sample. The joint data generating process (DGP) is an unobserved components (UC) model of inflation and a sticky information...

This paper explores the domestic and international effects of an increase in observed interest rates (conventional monetary policy) and expected interest rates (forward guidance). We find significant spillovers to a broad range of countries when both are subject to a tightening shock: Output...