From fixed-event to fixed-horizon density forecasts: obtaining measures of multi-horizon uncertainty from survey density forecasts
Series: Working Papers. 1947.
Author: Gergely Ganics, Barbara Rossi and Tatevik Sekhposyan.
Topics: Economic growth and convergence | Quantitative methods | Macroeconomic projections | Uncertainty.
Published in: Journal of Money, Credit and Banking, v. 56, Issue 7, October 2024, pp. 1675 - 1704
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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 “fixed 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 fixed-event nature limits the
usefulness of survey density predictions for policymakers and market participants, who
often wish to characterize uncertainty a fixed number of periods ahead (“fixed-horizon”). Is
it possible to obtain fixed-horizon density forecasts using the available fixed-event ones?
We propose a density combination approach that weights fixed-event density forecasts
according to a uniformity of the probability integral transform criterion, aiming at obtaining
a correctly calibrated fixed-horizon density forecast. Using data from the US Survey of
Professional Forecasters, we show that our combination method produces competitive
density forecasts relative to widely used alternatives based on historical forecast errors or
Bayesian VARs. Thus, our proposed fixed-horizon predictive densities are a new and useful
tool for researchers and policy makers.