A state space approach to bootstrapping conditional forecasts in ARMA models

被引:20
作者
Wall, KD [1 ]
Stoffer, DS
机构
[1] USN, Postgrad Sch, Washington, DC 20350 USA
[2] Univ Pittsburgh, Pittsburgh, PA 15260 USA
关键词
bootstrap; state space; forecasting; prediction errors; simulation;
D O I
10.1111/1467-9892.00288
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
A bootstrap approach to evaluating conditional forecast errors in ARMA models is presented. The key to this method is the derivation of a reverse-time state space model for generating conditional data sets that capture the salient stochastic properties of the observed data series. We demonstrate the utility of the method using several simulation experiments for the MA(q) and ARMA(p, q) models. Using the state space form, we are able to investigate conditional forecast errors in these models quite easily whereas the existing literature has only addressed conditional forecast error assessment in the pure AR(p) form. Our experiments use short data sets and non-Gaussian, as well as Gaussian, disturbances. The bootstrap is found to provide useful information on error distributions in all cases and serves as a broadly applicable alternative to the asymptotic Gaussian theory.
引用
收藏
页码:733 / 751
页数:19
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