This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Identification of Refined ARMA Echelon Form Models for Multivariate Time Series

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Nsiri, Saïd
Roy, Roch
Abstract

In the present article, we are interested in the identification of canonical ARMA echelon form models represented in a "refined" form. An identification procedure for such models is given by Tsay (J. Time Ser. Anal.10(1989), 357-372). This procedure is based on the theory of canonical analysis. We propose an alternative procedure which does not rely on this theory. We show initially that an examination of the linear dependency structure of the rows of the Hankel matrix of correlations, with originkin (i.e., with correlation at lagkin position (1, 1)), allows us not only to identify the Kronecker indicesn1, ..., nd, whenk=1, but also to determine the autoregressive ordersp1, ..., pd, as well as the moving average ordersq1, ..., qdof the ARMA echelon form model by settingk>1 andk<1, respectively. Successive test procedures for the identification of the structural parametersni,pi, andqiare then presented. We show, under the corresponding null hypotheses, that the test statistics employed asymptotically follow chi-square distributions. Furthermore, under the alternative hypothesis, these statistics are unbounded in probability and are of the formN[delta]{1+op(1)}, where[delta]is a positive constant andNdenotes the number of observations. Finally, the behaviour of the proposed identification procedure is illustrated with a simulated series from a given ARMA model.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.sciencedirect.com/science/article/B6WK9-45PVKB3-3/2/f9e44e776ae12e50dd1be4cb5611d05b
File Format:
File Function:
Download Restriction: Full text for ScienceDirect subscribers only

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Publisher Info
Article provided by Elsevier in its journal Journal of Multivariate Analysis.

Volume (Year): 56 (1996)
Issue (Month): 2 (February)
Pages: 207-231
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:eee:jmvana:v:56:y:1996:i:2:p:207-231

Contact details of provider:
Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description

Order Information:
Postal: http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
Web: https://shop.elsevier.com/order?id=622892&ref=622892_01_ooc_1&version=01

For technical questions regarding this item, or to correct its listing, contact: (Heidi Boesdal).

Related research
Keywords: multivariate ARMA model canonical representation Kronecker indices Hankel matrix identifiability (null);

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Boubacar Mainassara, Yacouba & Francq, Christian, 2009. "Estimating structural VARMA models with uncorrelated but non-independent error terms," MPRA Paper 15141, University Library of Munich, Germany. [Downloadable!]
  2. George Athanasopoulos & D.S. Poskitt & Farshid Vahid, 2007. "Two canonical VARMA forms: Scalar component models vis-à-vis the Echelon form," Monash Econometrics and Business Statistics Working Papers 10/07, Monash University, Department of Econometrics and Business Statistics, revised May 2009. [Downloadable!]
Statistics
Access and download statistics

Did you know? You too can volunteer for RePEc, for example by encouraging others to use our services.

This page was last updated on 2009-12-3.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.