The University of Southampton
University of Southampton Institutional Repository

Lag length estimation in large dimensional systems

Record type: Article

We study the impact of the system dimension on commonly used model selection criteria (AIC, BIC, HQ) and LR based general to specific testing strategies for lag length estimation in VARs. We show that AIC's well known overparameterization feature becomes quickly irrelevant as we move away from univariate models, with the criterion leading to consistent estimates under sufficiently large system dimensions. Unless the sample size is unrealistically small, all model selection criteria will tend to point towards low orders as the system dimension increases, with the AIC remaining by far the best performing criterion. This latter point is also illustrated via the use of an analytical power function for model selection criteria. The comparison between the model selection and general to specific testing strategy is discussed within the context of a new penalty term leading to the same choice of lag length under both approaches.

Full text not available from this repository.

Citation

Gonzalo, Jesus and Pitarakis, Jean-Yves (2002) Lag length estimation in large dimensional systems Journal of Time Series Analysis, 23, (4), pp. 401-423. (doi:10.1111/1467-9892.00270).

More information

Published date: 2002
Keywords: dimensionality, information criteria, lag length selection, VAR

Identifiers

Local EPrints ID: 33367
URI: http://eprints.soton.ac.uk/id/eprint/33367
PURE UUID: f1371f7f-17a0-4d2e-bb98-2b3647bf7974

Catalogue record

Date deposited: 16 May 2006
Last modified: 17 Jul 2017 15:53

Export record

Altmetrics

Contributors

Author: Jesus Gonzalo
Author: Jean-Yves Pitarakis

University divisions


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.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

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.

×