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Journal article 1328 views

Autoregression with Non-Gaussian Innovations

Yuzhi Cai Orcid Logo

Journal of Time Series Econometrics, Volume: 1, Issue: 2

Swansea University Author: Yuzhi Cai Orcid Logo

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DOI (Published version): 10.2202/1941-1928.1016

Abstract

Most economics and finance time series are non-Gaussian. In this paper, we propose aBayesian approach to non-Gaussian autoregressive time series models via quantile functions.This approach is parametric, so we also compare the proposed parametric approach with a semiparametricapproach. Simulation st...

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Published in: Journal of Time Series Econometrics
ISSN: 1941-1928
Published: 2009
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URI: https://cronfa.swan.ac.uk/Record/cronfa11977
first_indexed 2013-07-23T12:06:39Z
last_indexed 2019-07-10T13:54:59Z
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spelling 2019-07-10T11:42:02.9078828 v2 11977 2012-07-12 Autoregression with Non-Gaussian Innovations eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2012-07-12 CBAE Most economics and finance time series are non-Gaussian. In this paper, we propose aBayesian approach to non-Gaussian autoregressive time series models via quantile functions.This approach is parametric, so we also compare the proposed parametric approach with a semiparametricapproach. Simulation studies and applications to real time series show that this methodworks very well. Journal Article Journal of Time Series Econometrics 1 2 1941-1928 Bayesian method, quantile function, non-Gaussian time series, simulation, parametric and semi-parametric approaches 31 12 2009 2009-12-31 10.2202/1941-1928.1016 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University 2019-07-10T11:42:02.9078828 2012-07-12T14:10:25.4867034 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yuzhi Cai 0000-0003-3509-9787 1
title Autoregression with Non-Gaussian Innovations
spellingShingle Autoregression with Non-Gaussian Innovations
Yuzhi Cai
title_short Autoregression with Non-Gaussian Innovations
title_full Autoregression with Non-Gaussian Innovations
title_fullStr Autoregression with Non-Gaussian Innovations
title_full_unstemmed Autoregression with Non-Gaussian Innovations
title_sort Autoregression with Non-Gaussian Innovations
author_id_str_mv eff7b8626ab4cc6428eef52516fda7d6
author_id_fullname_str_mv eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
author Yuzhi Cai
author2 Yuzhi Cai
format Journal article
container_title Journal of Time Series Econometrics
container_volume 1
container_issue 2
publishDate 2009
institution Swansea University
issn 1941-1928
doi_str_mv 10.2202/1941-1928.1016
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
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description Most economics and finance time series are non-Gaussian. In this paper, we propose aBayesian approach to non-Gaussian autoregressive time series models via quantile functions.This approach is parametric, so we also compare the proposed parametric approach with a semiparametricapproach. Simulation studies and applications to real time series show that this methodworks very well.
published_date 2009-12-31T10:37:55Z
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score 11.08899

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