The Dynamics of Economic Functions: Modeling and Forecasting the Yield Curve
提出功能信号加噪声模型，用自然三次样条和协整向量自回归描述收益率曲线动态，在预测美国国债收益率曲线时优于动态Nelson-Siegel和随机游走模型。
The class of functional signal plus noise (FSN) models is introduced that provides a new, general method for modeling 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 be feasibly 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 U.S. Treasury bonds at the 1-month-ahead horizon. The method consistently outperforms the dynamic Nelson–Siegel and random walk forecasts on the basis of both mean squared forecast error criteria and economically relevant loss functions derived from the realized profits of pairs trading algorithms. The analysis also highlights in a concrete setting the dangers of attempting to infer the relative economic value of model forecasts on the basis of their associated mean squared forecast errors.