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This paper proposes a forecasting model that combines a factor augmented VAR (FAVAR) methodology with the Nelson and Siegel (NS) parametrization of the yield curve in order to predict the Brazilian term structure of interest rates. Importantly, we extract the principal components for the FAVAR from a large data set containing a range of forwardlooking macroeconomic and financial variables. Our forecasting model improves on the predictive accuracy of extant models in the literature significantly, particularly at shortterm horizons. For instance, the mean absolute forecast errors are 15–40% lower than those of the random walk benchmark on predictions at the three-month horizon. The outof-sample analysis shows that the inclusion of forward-looking indicators is the key to improving the predictive ability of the model. © 2016 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
This paper proposes that future values of yields at different maturities be forecast by means of a FAVAR model for the level, slope and curvature of the yield curve. In particular, we estimate an augmented VAR model for a system that includes not only the Nelson–Siegel factors of the Brazilian yield curve, but also the principal components of a large number of macroeconomic and financial indicators. We show that our forecasting approach outperformsthe extant models in the literature, including the random walk benchmark, even at shorter horizons. Further analysis reveals that the use of forward-looking state variables is vital for producing better forecasts.