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Forecasting the Yield Curve in a Data-Rich Environment using the Factor-Augmented Nelson-Siegel Model

Peter Exterkate (), Dick van Dijk (), Christiaan Heij and Patrick Groenen ()

No EI 2010-06, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

Abstract: Various ways of extracting macroeconomic information from a data-rich environment are compared with the objective of forecasting yield curves using the Nelson-Siegel model. Five issues in factor extraction are addressed, namely, selection of a subset of the available information, incorporation of the forecast objective in constructing factors, specification of a multivariate forecast objective, data grouping before constructing factors, and selection of the number of factors in a data-driven way. Our empirical results show that each of these features helps to improve forecast accuracy, especially for the shortest and longest maturities. The data-driven methods perform well in relatively volatile periods, when simpler models do not suffice.

Keywords: Nelson-Siegel model; factor extraction; variable selection; yield curve prediction (search for similar items in EconPapers)
Date: 2010-02-23
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Related works:
Journal Article: Forecasting the Yield Curve in a Data‐Rich Environment Using the Factor‐Augmented Nelson–Siegel Model (2013)
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