You are not logged in.

Vector autoregressions and macroeconomic modeling: an error taxonomy

Poskitt, DS and Yao, Wenying 2015, Vector autoregressions and macroeconomic modeling: an error taxonomy, Journal of business & economic statistics, vol. 35, no. 3, pp. 1-14, doi: 10.1080/07350015.2015.1077139.

Attached Files
Name Description MIMEType Size Downloads

Title Vector autoregressions and macroeconomic modeling: an error taxonomy
Author(s) Poskitt, DS
Yao, WenyingORCID iD for Yao, Wenying orcid.org/0000-0001-6368-0160
Journal name Journal of business & economic statistics
Volume number 35
Issue number 3
Start page 1
End page 14
Total pages 14
Publisher Taylor & Francis
Place of publication New York, N.Y.
Publication date 2015-08-12
ISSN 0735-0015
1537-2707
Keyword(s) approximation error
estimation error
bias
structural VAR
order of magnitude
Summary In this article we investigate the theoretical behaviour of finite lag VAR(n) models fitted to time series that in truth come from an infinite order VAR(∞) data generating mechanism. We show that the overall error can be broken down into two basic components, an estimation error that stems from the difference between the parameter estimates and their population ensemble VAR(n) counterparts, and an approximation error that stems from the difference between the VAR(n) and the true VAR(∞). The two sources of error are shown to be present in other performance indicators previously employed in the literature to characterize, so called, truncation effects. Our theoretical analysis indicates that the magnitude of the estimation error exceeds that of the approximation error, but experimental results based upon a prototypical real business cycle model and a practical example indicate that the approximation error approaches its asymptotic position far more slowly than does the estimation error, their relative orders of magnitude notwithstanding. The experimental results suggest that with sample sizes and lag lengths like those commonly employed in practice VAR(n) models are likely to exhibit serious errors of both types when attempting to replicate the dynamics of the true underlying process and that inferences based on VAR(n) models can be very untrustworthy.
Language eng
DOI 10.1080/07350015.2015.1077139
Field of Research 140302 Econometric and Statistical Methods
140305 Time-Series Analysis
Socio Economic Objective 910199 Macroeconomics not elsewhere classified
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Taylor & Francis
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085228

Document type: Journal Article
Collection: Department of Economics
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 31 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Wed, 03 Aug 2016, 16:24:40 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.