We study the forecast power of yield curve data for macroeconomic time series, such as consumer price index, personal consumption expenditures, producer price index, real disposable income, unemployment rate and industrial production. We employ a state space model in which the forecasting objective is included in the state vector. This amounts to an augmented dynamic factor model in which the factors (level, slope and curvature of the yield curve) are supervised for the macroeconomic forecast target. In other words, the factors are informed about the dynamics of the forecast target. The factor loadings have the Nelson and Siegel (1987) structure and we consider one forecast target at a time. We compare the forecasting performance to benchmark models such as principal components regression, partial least squares, and ARMA(p,q) processes. We use the yield curve data of Gürkaynak, Sack, and Wright (2006) and of Diebold and Li (2006) and macroeconomic data from FRED, covering the sample period 1st January 1961 to 1st January 2012. We compare the models by means of the conditional predictive ability test of Giacomini and White (2006). We find that the yield curve does have forecast power for the macroeconomic time series and that supervising the factor extraction for the forecast target can improve forecast performance. We also compare direct and indirect forecasts for the different models and find that the indirect forecasts perform better for our data and specification.