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Bootstrap Approximation to Prediction MSE for State-Space Models with Estimated Parameters

Record type: Monograph (Project Report)

We propose a simple but general bootstrap method for estimating the Prediction Mean Square Error (PMSE) of the state vector predictors when the unknown model parameters are estimated from the observed series. As is well known, substituting the model parameters by the sample estimates in the theoretical PMSE expression that assumes known parameter values results in under-estimation of the true PMSE. Methods proposed in the literature to deal with this problem in state-space modelling are inadequate and may not even be operational when fitting complex models, or when some of the parameters are close to their boundary values. The proposed method consists of generating a large number of series from the model fitted to the original observations, re-estimating the model parameters using the same method as used for the observed series and then estimating separately the component of PMSE resulting from filter uncertainty and the component resulting from parameter uncertainty. Application of the method to a model fitted to sample estimates of employment ratios in the U.S.A. that contains eighteen unknown parameters estimated by a three-step procedure yields accurate results. The procedure is applicable to mixed linear models that can be cast into state-space form. (Updated 6th October 2004)

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Citation

Pfeffermann, Danny and Tiller, Richard (2004) Bootstrap Approximation to Prediction MSE for State-Space Models with Estimated Parameters , Southampton, UK Southampton Statistical Sciences Research Institute 32pp. (S3RI Methodology Working Papers, M03/05).

More information

Submitted date: 6 October 2004
Published date: 6 October 2004

Identifiers

Local EPrints ID: 9731
URI: http://eprints.soton.ac.uk/id/eprint/9731
PURE UUID: ec1b8ab5-9c05-4dd1-b35b-fd24be36e60c

Catalogue record

Date deposited: 06 Oct 2004
Last modified: 17 Jul 2017 17:09

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