Article ID Journal Published Year Pages File Type
1149524 Journal of Statistical Planning and Inference 2010 14 Pages PDF
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
, ,