Inflation Forecasts Extracted from Nominal and Real Yield Curves

The aim of this paper is to evaluate the performance of inflation forecasts backed out from the nominal and real yield curves in the United Kingdom. We use the Nelson–Siegel (NS) framework to model the break-even inflation term structure, and we base our analysis on the one-day break-even inflation derived from NS factors, which avoids the need for a direct estimation of the inflation risk premium. Fitting (vector) autoregression models augmented with nominal and/or real Cochrane-Piazzesi factors, we find that parsimonious models based on the one-day break-even inflation outperform other models in forecasting inflation out-of-sample. In addition, we quantify the parameter uncertainty and show that it may have considerable impact on inflation forecasts.