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Identification of vector AR models with recursive structural errors using conditional independence graphs.

Tunnicliffe Wilson, Granville and Reale, Marco (2001) Identification of vector AR models with recursive structural errors using conditional independence graphs. Statistical Methods & Applications, 10 (1-3). pp. 49-65. ISSN 1618-2510

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Abstract

In canonical vector time series autoregressions, which permit dependence only on past values, the errors generally show contemporaneous correlation. By contrast structural vector autoregressions allow contemporaneous series dependence and assume errors with no contemporaneous correlation. Such models having a recursive structure can be described by a directed acyclic graph. We show, with the use of a real example, how the identification of these models may be assisted by examination of the conditional independence graph of contemporaneous and lagged variables. In this example we identify the causal dependence of monthly Italian bank loan interest rates on government bond and repurchase agreement rates. When the number of series is larger, the structural modelling of the canonical errors alone is a useful initial step, and we first present such an example to demonstrate the general approach to identifying a directed graphical model.

Item Type: Article
Journal or Publication Title: Statistical Methods & Applications
Additional Information: RAE_import_type : Journal article RAE_uoa_type : Statistics and Operational Research
Uncontrolled Keywords: Partial correlation - moralization - causality - graphical modelling - lending channel
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 2446
Deposited By: ep_importer
Deposited On: 29 Mar 2008 16:25
Refereed?: Yes
Published?: Published
Last Modified: 26 Jul 2012 16:25
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/2446

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