The University of Southampton
University of Southampton Institutional Repository

Bootstrap approximation to prediction MSE for state-space models with estimated parameters

Pfeffermann, Danny and Tiller, Richard (2005) Bootstrap approximation to prediction MSE for state-space models with estimated parameters Journal of Time Series Analysis, 26, (6), pp. 893-916. (doi:10.1111/j.1467-9892.2005.00448.x).

Record type: Article


We propose simple parametric and nonparametric bootstrap methods for estimating the prediction mean square error (PMSE) of state vector predictors that use estimated model parameters. As is well known, substituting the model parameters by their estimates in the theoretical PMSE expression that assumes known parameter values results in underestimation of the true PMSE. The parametric method consists of generating parametrically a large number of bootstrap series from the model fitted to the original series, re-estimating the model parameters for each series using the same method as used for the original series and then estimating the separate components of the PMSE.
The nonparametric method generates the series by bootstrapping the standardized innovations estimated for the original series. The bootstrap methods are compared with other methods considered in the literature in a simulation study that also examines the robustness of the various methods to non-normality of the model error terms. Application of the bootstrap method to a model fitted to employment ratios in the USA that contains 18 unknown parameters, estimated by a three-step procedure yields unbiased PMSE estimators.

Full text not available from this repository.

More information

Published date: November 2005
Keywords: hyper-parameters, kalman filter, mle, order of bias, reml


Local EPrints ID: 39064
PURE UUID: 114ab3b4-6b53-4e5c-9a18-a9111efef700

Catalogue record

Date deposited: 19 Jun 2006
Last modified: 17 Jul 2017 15:37

Export record


Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.