Curve forecasting by functional autoregression

JOURNAL OF MULTIVARIATE ANALYSIS

卷:99

Article

Kargin, V.¹  Onatski, A.² 

[1] Courant Inst Math Sci, Forest Hills, NY 11375 USA

[2] Columbia Univ, Dept Econ, New York, NY 10027 USA

关键词: Functional data analysis;    Dimension reduction;    Reduced-rank regression;    Principal component;    Singular value decomposition;    Predictive factor;    Term structure;    Interest rates;   

DOI  :  10.1016/j.jmva.2008.03.001

来源: Elsevier

【 摘 要 】

This paper deals with the prediction of curve-valued autoregression processes. It develops a novel technique, predictive factor decomposition, for the estimation of the autoregression operator. The technique is based on finding a reduced-rank approximation to the autoregression operator that minimizes the expected squared norm of the prediction error. Implementing this idea, we relate the operator approximation problem to the singular value decomposition of a combination of cross-covariance and covariance operators. We develop an estimation method based on regularization of the empirical counterpart of this singular value decomposition, prove its consistency and evaluate convergence rates. The method is illustrated by an example of the term structure of the Eurodollar futures rates. In the sample corresponding to the period of normal growth, the predictive factor technique outperforms the principal components method and performs on a par with custom-designed prediction methods. (C) 2008 Elsevier Inc. All rights reserved.

【 授权许可】

Free