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.

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Erasmus School of Economics
Econometric Institute Research Papers
Report / Econometric Institute, Erasmus University Rotterdam
Erasmus School of Economics

Exterkate, P., van Dijk, D., Heij, C., & Groenen, P. (2010). Forecasting the Yield Curve in a Data-Rich Environment using the Factor-Augmented Nelson-Siegel Model (No. EI 2010-06). Report / Econometric Institute, Erasmus University Rotterdam (pp. 1–40). Retrieved from