From Fixed-event to Fixed-horizon Density Forecasts: Obtaining Measures of Multi-horizon Uncertainty from Survey Density Forecasts

Abstract

Surveys of professional forecasters produce precise and timely point forecasts for key macroeconomic variables. However, the accompanying density forecasts are not as widely utilized, and there is no consensus about their quality. This is partly because such surveys are often conducted for “�xed events�. For example, in each quarter panelists are asked to forecast output growth and inflation for the current calendar year and the next, implying that the forecast horizon changes with each survey round. The �xed-event nature limits the usefulness of survey density predictions for policymakers and market participants, who often wish to characterize uncertainty a �xed number of periods ahead (“�xed-horizon�). Is it possible to obtain �xed-horizon density forecasts using the available �xed-event ones? The authors propose a density combination approach that weights �xed-event density forecasts according to a uniformity of the probability integral transform criterion, aiming at obtaining a correctly calibrated �xed-horizon density forecast. Using data from the US Survey of Professional Forecasters, they show that our combination method produces competitive density forecasts relative to widely used alternatives based on historical forecast errors or Bayesian VARs. Thus, the proposed �xed-horizon predictive densities are a new and useful tool for researchers and policy makers.