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The Dynamics of Economic Functions: Modelling and Forecasting the Yield Curve

Bowsher, CG and Meeks, R (2008) The Dynamics of Economic Functions: Modelling and Forecasting the Yield Curve. UNSPECIFIED. OFRC Working Papers Series 2008fe24.

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Abstract

The class of Functional Signal plus Noise (FSN) models is introduced that provides a new, general method for modelling and forecasting time series of economic functions. The underlying, continuous economic function (or 'signal') is a natural cubic spline whose dynamic evolution is driven by a cointegrated vector autoregression for the ordinates (or 'y-values') at the knots of the spline. The natural cubic spline provides flexible cross-sectional fit and results in a linear, state space model. This FSN model achieves dimension reduction, provides a coherent description of the observed yield curve and its dynamics as the cross-sectional dimension N becomes large, and can feasibly be estimated and used for forecasting when N is large. The integration and cointegration properties of the model are derived. The FSN models are then applied to forecasting 36-dimensional yield curves for US Treasury bonds at the one month ahead horizon. The method consistently outperforms the Diebold and Li (2006) and random walk forecasts on the basis of both mean square forecast error criteria and economically relevant loss functions derived from the realised profits of pairs trading algorithms. The analysis also highlights in a concrete setting the dangers of attempts to infer the relative economic value of model forecasts on the basis of their associated mean square forecast errors.

Item Type: Monograph (UNSPECIFIED)
Uncontrolled Keywords: FSN-ECM models; functional time series; term structure; forecasting interest rates; natural cubic spline; state space form
Subjects: H Social Sciences > HB Economic Theory
Divisions: Faculty of Social Sciences > Economics, Department of
Depositing User: Jim Jamieson
Date Deposited: 07 Jan 2013 18:43
Last Modified: 17 Aug 2017 18:04
URI: http://repository.essex.ac.uk/id/eprint/5000

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