No Cover Image

Journal article 1325 views

Quantile self-exciting threshold autoregressive time series models

Yuzhi Cai Orcid Logo, Julian Stander

journal of time series analysis, Volume: 29, Issue: 1, Pages: 186 - 202

Swansea University Author: Yuzhi Cai Orcid Logo

Full text not available from this repository: check for access using links below.

Check full text

DOI (Published version): 10.1111/j.1467-9892.2007.00551.x

Abstract

<p>In this paper we present a Bayesian approach to quantile self-exciting threshold autoregressive time series models. The simulation work shows that the method can deal very well with nonstationary time series with very large, but not necessarily symmetric, variations. The methodology has als...

Full description

Published in: journal of time series analysis
ISSN: 1647-9892
Published: Blackwell Publishing Ltd 2008
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa7004
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2013-07-23T11:57:01Z
last_indexed 2018-02-09T04:34:43Z
id cronfa7004
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2016-08-01T10:48:33.0441608</datestamp><bib-version>v2</bib-version><id>7004</id><entry>2012-02-01</entry><title>Quantile self-exciting threshold autoregressive time series models</title><swanseaauthors><author><sid>eff7b8626ab4cc6428eef52516fda7d6</sid><ORCID>0000-0003-3509-9787</ORCID><firstname>Yuzhi</firstname><surname>Cai</surname><name>Yuzhi Cai</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2012-02-01</date><deptcode>BAF</deptcode><abstract>&lt;p&gt;In this paper we present a Bayesian approach to quantile self-exciting threshold autoregressive time series models. The simulation work shows that the method can deal very well with nonstationary time series with very large, but not necessarily symmetric, variations. The methodology has also been applied to the growth rate of US real GNP data and some interesting results have been obtained.&lt;/p&gt;</abstract><type>Journal Article</type><journal>journal of time series analysis</journal><volume>29</volume><journalNumber>1</journalNumber><paginationStart>186</paginationStart><paginationEnd>202</paginationEnd><publisher>Blackwell Publishing Ltd</publisher><issnPrint>1647-9892</issnPrint><keywords>Bayesian methods; MCMC; quantile SETAR model; simulation; US GNP.</keywords><publishedDay>31</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2008</publishedYear><publishedDate>2008-12-31</publishedDate><doi>10.1111/j.1467-9892.2007.00551.x</doi><url>http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9892</url><notes/><college>COLLEGE NANME</college><department>Accounting and Finance</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>BAF</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2016-08-01T10:48:33.0441608</lastEdited><Created>2012-02-01T09:32:07.6270000</Created><path><level id="1">Faculty of Humanities and Social Sciences</level><level id="2">School of Management - Accounting and Finance</level></path><authors><author><firstname>Yuzhi</firstname><surname>Cai</surname><orcid>0000-0003-3509-9787</orcid><order>1</order></author><author><firstname>Julian</firstname><surname>Stander</surname><order>2</order></author></authors><documents/><OutputDurs/></rfc1807>
spelling 2016-08-01T10:48:33.0441608 v2 7004 2012-02-01 Quantile self-exciting threshold autoregressive time series models eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2012-02-01 BAF <p>In this paper we present a Bayesian approach to quantile self-exciting threshold autoregressive time series models. The simulation work shows that the method can deal very well with nonstationary time series with very large, but not necessarily symmetric, variations. The methodology has also been applied to the growth rate of US real GNP data and some interesting results have been obtained.</p> Journal Article journal of time series analysis 29 1 186 202 Blackwell Publishing Ltd 1647-9892 Bayesian methods; MCMC; quantile SETAR model; simulation; US GNP. 31 12 2008 2008-12-31 10.1111/j.1467-9892.2007.00551.x http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9892 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2016-08-01T10:48:33.0441608 2012-02-01T09:32:07.6270000 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yuzhi Cai 0000-0003-3509-9787 1 Julian Stander 2
title Quantile self-exciting threshold autoregressive time series models
spellingShingle Quantile self-exciting threshold autoregressive time series models
Yuzhi Cai
title_short Quantile self-exciting threshold autoregressive time series models
title_full Quantile self-exciting threshold autoregressive time series models
title_fullStr Quantile self-exciting threshold autoregressive time series models
title_full_unstemmed Quantile self-exciting threshold autoregressive time series models
title_sort Quantile self-exciting threshold autoregressive time series models
author_id_str_mv eff7b8626ab4cc6428eef52516fda7d6
author_id_fullname_str_mv eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
author Yuzhi Cai
author2 Yuzhi Cai
Julian Stander
format Journal article
container_title journal of time series analysis
container_volume 29
container_issue 1
container_start_page 186
publishDate 2008
institution Swansea University
issn 1647-9892
doi_str_mv 10.1111/j.1467-9892.2007.00551.x
publisher Blackwell Publishing Ltd
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-9892
document_store_str 0
active_str 0
description <p>In this paper we present a Bayesian approach to quantile self-exciting threshold autoregressive time series models. The simulation work shows that the method can deal very well with nonstationary time series with very large, but not necessarily symmetric, variations. The methodology has also been applied to the growth rate of US real GNP data and some interesting results have been obtained.</p>
published_date 2008-12-31T03:08:39Z
_version_ 1763749835877384192
score 11.013731

Similar Items