Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1149524 | Journal of Statistical Planning and Inference | 2010 | 14 Pages |
Abstract
In this paper, we consider estimation of the mean squared prediction error (MSPE) of the best linear predictor of (possibly) nonlinear functions of finitely many future observations in a stationary time series. We develop a resampling methodology for estimating the MSPE when the unknown parameters in the best linear predictor are estimated. Further, we propose a bias corrected MSPE estimator based on the bootstrap and establish its second order accuracy. Finite sample properties of the method are investigated through a simulation study.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
S. Bandyopadhyay, S.N. Lahiri,