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 proved hard to surpass. This study 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 the observations are forecasts, it may be related with the dispersion in forecasting expectations and, in a sense, with its uncertainty. Within this approach, the study also revisits Friedman's hypothesis relating the level of inflation with expectations uncertainty at the beginning of the twenty-first century.

Acknowledgements

The authors acknowledge financial support from Comunidad de Madrid contract grant 06/HSE/0016/2004, from the Spanish Ministry of Science and Technology contract from the CICYT, BEC2002-00081 (1st author) and from Universidad de Alcalá contract PI2004/012 (2nd author).