We propose a methodology to gauge the uncertainty in output gap nowcasts across a large number of commonly deployed vector autoregressions in US inflation and various measures of the output gap. Our approach constructs ensemble nowcast densities using a linear opinion pool. This yields...

Many alternative measures of core, or underlying, inflation have been proposed that are based on stripping out some unwanted or excessively volatile elements from the headline rate. A potential drawback of such measures is that they are necessarily atheoretic--based largely on purely statistical...

We examine the effectiveness of recursive-weight and equal-weight combination strategies for forecasting using many time-varying models of the relationship between inflation and the output gap. The forecast densities for inflation reflect the uncertainty across models using many statistical...

A popular macroeconomic forecasting strategy takes combinations across many models to hedge against model instabilities of unknown timing; see (among others) Stock andWatson (2004) and Clark and McCracken (2009). In this paper, we examine the effectiveness of recursive-weight and equal-weight...

We propose a methodology for producing density forecasts for the output gap in real time using a large number of vector autoregessions in inflation and output gap measures. Density combination utilizes a linear mixture of experts framework to produce potentially non-Gaussian ensemble densities...