We construct risks around consensus forecasts of real GDP growth, unemployment, and inflation. We find that risks are time-varying, asymmetric, and partly predictable. Tight financial conditions forecast downside growth risk, upside unemployment risk, and increased uncertainty around the...

We estimate the evolution of the conditional joint distribution of economic and financial conditions. While the joint distribution is approximately Gaussian during normal periods, sharp tightenings of financial conditions lead to the emergence of additional modes. The U.S. economy has...

We use a quantitative equilibrium model with houses, collateralized debt, and foreign borrowing to study the impact of global imbalances on the U.S. economy in the 2000s. Our results suggest that the dynamics of foreign capital flows account for between one-fourth and one-third of the increase...

The paper considers the macroeconomic transmission of demand and supply shocks in an open economy under alternative assumptions about whether the zero interest rate floor (ZIF) is binding. It uses a two-country general-equilibrium simulation model calibrated to the Japanese economy relative to...

Many structural break and regime-switching models have been used with macroeconomic and financial data. In this paper, we develop an extremely flexible parametric model that accommodates virtually any of these specifications - and does so in a simple way that allows for straightforward Bayesian...

This paper develops a model of macroeconomic forecasting in which the wages firms pay their forecasters are a function of their accuracy as well as the publicity they generate for their employers by being correct. In the resulting Nash equilibrium, forecasters with identical models, information,...

We examine inventory adjustment in the U.S. manufacturing sector using quarterly firm-level data over the period 1978-97. Our evidence indicates that the inventory investment process is nonlinear and asymmetric, results consistent with a nonconvex adjustment cost structure. The inventory...

Factor-augmented regressions are often used as a parsimonious way of modeling a variable using information from a large data-set, through a few factors estimated from this data-set. But how does one determine the appropriate number of factors that are relevant for such a regression? Existing...

We compare a number of data-rich prediction methods that are widely used in macroeconomic forecasting with a lesser known alternative: partial least squares (PLS) regression. In this method, linear, orthogonal combinations of a large number of predictor variables are constructed such that the...

We provide a novel methodology for estimating time-varying weights in linear prediction pools, which we call dynamic pools, and use it to investigate the relative forecasting performance of dynamic stochastic general equilibrium (DSGE) models, with and without financial frictions, for output...