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A dynamic Nelson-Siegel model with forward-looking indicators for the yield curve in the US

Listed author(s):
  • Fausto Vieira
  • Fernando Chague, Marcelo Fernandes

This paper proposes a Factor-Augmented Dynamic Nelson-Siegel (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 10-year yields. The out-of-sample analysis shows that the good performance comes mostly from the forward-looking 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.

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File URL: http://www.repec.eae.fea.usp.br/documentos/Vieira_Chague_Fernandes_31WP.pdf
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Paper provided by University of São Paulo (FEA-USP) in its series Working Papers, Department of Economics with number 2016_31.

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Date of creation: 07 Dec 2016
Handle: RePEc:spa:wpaper:2016wpecon31
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