Lag length selection for vector error correction models.

Sharp, Gary David (2010) Lag length selection for vector error correction models. PhD thesis, Rhodes University.

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Abstract

This thesis investigates the problem of model identification in a Vector Autoregressive framework. The study reviews the existing research, conducts an extensive simulation based analysis of thirteen information theoretic criterion (IC), one of which is a novel derivation. The simulation exercise considers the evaluation of seven alternative error restricted vector autoregressive models with four different lag lengths. Alternative sample sizes and parameterisations are also evaluated and compared to results in the existing literature. The results of the comparative analysis provide strong support for the efficiency based criterion of Akaike and in particular the selection capability of the novel criterion, referred to as a modified corrected Akaike information criterion, demonstrates useful finite sample properties .

Item Type:Thesis (PhD)
Uncontrolled Keywords:Akaike Information Criterion, Mathematical models - Evaluation, Autoregression (Statistics), Error analysis (Mathematics),
Subjects:Q Science > QA Mathematics > QA273 Probabilities. Mathematical statistics
Divisions:Faculty > Faculty of Commerce > Statistics
Faculty > Faculty of Science > Statistics
Supervisors:Radloff, Sarah
ID Code:1809
Deposited By: Madireng Monyela
Deposited On:07 Dec 2010 07:33
Last Modified:06 Jan 2012 16:21
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