Flexible Time Series Forecasting Using Shrinkage Techniques and Focused Selection Criteria
Nonlinear time series models can exhibit components such as long range trends and seasonalities that may be modeled in a flexible fashion. The resulting unconstrained maximum likelihood estimator can be too heavily parameterized and suboptimal for forecasting purposes. The paper proposes the use of a class of shrinkage estimators that includes the Ridge estimator for forecasting time series, with a special attention to GARCH and ACD models. The local large sample properties of this class of shrinkage estimators is investigated. Moreover, we propose symmetric and asymmetric focused selection criteria of shrinkage estimators. The focused information criterion selection strategy consists of picking up the shrinkage estimator that minimizes the estimated risk (e.g. MSE) of a given smooth function of the parameters of interest to the forecaster. The usefulness of such shrinkage techniques is illustrated by means of a simulation exercise and an intra-daily financial durations forecasting application. The empirical application shows that an appropriate shrinkage forecasting methodology can significantly outperform the unconstrained ML forecasts of rich flexible specifications.
|Date of creation:||May 2007|
|Date of revision:|
|Contact details of provider:|| Postal: Viale G.B. Morgagni, 59 - I-50134 Firenze - Italy|
Phone: +39 055 2751500
Fax: +39 055 2751525
Web page: http://www.disia.unifi.it/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:fir:econom:wp2007_02. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Francesco Calvori)
If references are entirely missing, you can add them using this form.