Modeling Multi-Period Inflation Uncertainty Using a Panel of Density Forcasts

This paper examines the determinants of inflation forecast uncertainty using a panel of density forecasts from the Survey of Professional Forecasters (SPF). Based on a dynamic heterogeneous panel data model, we find that the persistence in forecast uncertainty is much less than what the aggregate time series data would suggest. In addition, the strong link between past forecast errors and current forecast uncertainty, as often is noted in the ARCH literature, is largely lost in a multiperiod context with varying forecast horizons. We propose a novel way of estimating ¡°news¡± and its variance using the Kullback-Leibler Information, and show that the latter is an important determinant of forecast uncertainty. Our evidence suggests a strong relationship of forecast uncertainty with level of inflation, but not with forecaster discord or with the volatility of a number of other macroeconomic indicators.