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This paper proposes a FactorAugmented Dynamic NelsonSiegel (FADNS) model to predict the yield curve in the US that relies on a large data set of weekly financial and macroeconomic variables. The FADNS model significantly improves interest rate forecasts relative to the extant models in the literature. For longer horizons, it beats autoregressive alternatives, with a reduction in mean absolute error of up to 40%. For shorter horizons, it offers a good challenge to autoregressive forecasting models, outperforming them for the 7 and 10year yields. The outofsample analysis shows that the good performance comes mostly from the forwardlooking nature of the variables we employ. Including them reduces the mean absolute error in 5 basis points on average with respect to models that reflect only past macroeconomic events.
Date: 201703
Authors:
Vieira, Fausto José Araújo
Chague, Fernando Daniel
Fernandes, Marcelo
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