Lag length selection for vector error correction models

Title: Lag length selection for vector error correction models
Creator: Sharp, Gary David
Subject: Akaike Information Criterion Mathematical models -- Evaluation Autoregression (Statistics) Error analysis (Mathematics)
Date Issued: 2010
Date: 2010
Type: Thesis
Type: Doctoral
Type: PhD
Identifier: vital:5568
Identifier: http://hdl.handle.net/10962/d1002808
Description: 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.
Format: 178 leaves
Format: pdf
Publisher: Rhodes University
Publisher: Faculty of Science, Statistics
Language: English
Rights: Sharp, Gary David

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