A two factor model to combine US inflation forecasts

Abstract

The combination of individual forecasts is often a useful tool to improve forecast accuracy. The most commonly used technique for forecast combination is the mean, and it has frequently proven hard to beat. This paper considers factor analysis to combine US inflation forecasts showing that just one factor is not enough to beat the mean and that the second one is necessary. The first factor is usually a weighted mean of the variables and it can be interpreted as a consensus forecast, while the second factor generally provides the differences among the variables and, since our observations are forecasts, it may be related with the dispersion in the forecasting expectations and in a sense with its uncertainty. Within this approach, the paper also revisits Friedman's hypothesis relating the level of inflation with uncertainty in expectations at the beginning of the 21st century.