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 consider the fundamental issue of what makes a “good” probability forecast for a central bank operating within an inflation targeting framework. We provide two examples in which the candidate forecasts comfortably outperform those from benchmark specifications by conventional 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...

We provide a new way to filter US inflation into trend and cycle components, based on extracting long-run forecasts from the Survey of Professional Forecasters. We operate the Kalman filter in reverse, beginning with observed forecasts, then estimating parameters, and then extracting the...