Curve forecasting by functional autoregression Export

@article{citeulike:5041419, abstract = {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.}, author = {Kargin, V. and Onatski, A.}, citeulike-article-id = {5041419}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.jmva.2008.03.001}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0047259X08000961}, doi = {10.1016/j.jmva.2008.03.001}, issn = {0047259X}, journal = {Journal of Multivariate Analysis}, keywords = {autoregressive, fda}, month = {November}, number = {10}, pages = {2508--2526}, posted-at = {2009-07-02 13:54:50}, priority = {2}, title = {Curve forecasting by functional autoregression}, url = {http://dx.doi.org/10.1016/j.jmva.2008.03.001}, volume = {99}, year = {2008} }

TY - JOUR ID - citeulike:5041419 L3 - citeulike-article-id:5041419 N2 - 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. IS - 10 JF - Journal of Multivariate Analysis SN - 0047259X EP - 2526 TI - Curve forecasting by functional autoregression VL - 99 SP - 2508 KW - autoregressive KW - fda AU - Kargin, V AU - Onatski, A PY - 2008/11// UR - http://dx.doi.org/10.1016/j.jmva.2008.03.001 ER -