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Predicting the term structure of interest rates incorporating parameter uncertainty, model uncertainty and macroeconomic information

Listed author(s):
  • De Pooter, Michiel
  • Ravazzolo, Francesco
  • van Dijk, Dick

We forecast the term structure of U.S. Treasury zero-coupon bond yields by analyzing a range of models that have been used in the literature. We assess the relevance of parameter uncertainty by examining the added value of using Bayesian inference compared to frequentist estimation techniques, and model uncertainty by combining forecasts from individual models. Following current literature we also investigate the benefits of incorporating macroeconomic information in yield curve models. Our results show that adding macroeconomic factors is very beneficial for improving the out-of-sample forecasting performance of individual models. Despite this, the predictive accuracy of models varies over time considerably, irrespective of using the Bayesian or frequentist approach. We show that mitigating model uncertainty by combining forecasts leads to substantial gains in forecasting performance, especially when applying Bayesian model averaging.

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File URL: https://mpra.ub.uni-muenchen.de/2512/1/MPRA_paper_2512.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 2512.

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Date of creation: 06 Nov 2006
Date of revision: 03 Mar 2007
Handle: RePEc:pra:mprapa:2512
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  25. Don H. Kim & Jonathan H. Wright, 2005. "An arbitrage-free three-factor term structure model and the recent behavior of long-term yields and distant-horizon forward rates," Finance and Economics Discussion Series 2005-33, Board of Governors of the Federal Reserve System (U.S.).
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  34. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, Oxford University Press, vol. 120(1), pages 387-422.
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