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Forecasting for quantile self-exciting threshold autoregressive time series models

Y Cai, Yuzhi Cai Orcid Logo

Biometrika, Volume: 97, Issue: 1, Pages: 199 - 208

Swansea University Author: Yuzhi Cai Orcid Logo

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DOI (Published version): 10.1093/biomet/asp070

Abstract

Self-exciting threshold autoregressive time series models have been used extensively and the conditional mean obtained from these models can be used to predict the future value of a random variable. In this paper we consider quantile forecasts of a time series based on the quantile self-exciting thr...

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Published in: Biometrika
ISSN: 0006-3444
Published: Oxford University Press 2010
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URI: https://cronfa.swan.ac.uk/Record/cronfa6996
first_indexed 2013-07-23T11:57:00Z
last_indexed 2018-02-09T04:34:42Z
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spelling 2016-05-01T15:26:48.0790336 v2 6996 2012-01-31 Forecasting for quantile self-exciting threshold autoregressive time series models eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2012-01-31 CBAE Self-exciting threshold autoregressive time series models have been used extensively and the conditional mean obtained from these models can be used to predict the future value of a random variable. In this paper we consider quantile forecasts of a time series based on the quantile self-exciting threshold autoregressive time series models proposed by Cai and Stander (2008) and present a new forecasting method for these quantile models. Simulation studies and application to real time series show that the method works very well. Journal Article Biometrika 97 1 199 208 Oxford University Press 0006-3444 Monte Carlo method, Forecasting method, Predictive density function, Quantile forecast. 31 12 2010 2010-12-31 10.1093/biomet/asp070 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University 2016-05-01T15:26:48.0790336 2012-01-31T10:44:33.4800000 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Y Cai 1 Yuzhi Cai 0000-0003-3509-9787 2
title Forecasting for quantile self-exciting threshold autoregressive time series models
spellingShingle Forecasting for quantile self-exciting threshold autoregressive time series models
Yuzhi Cai
title_short Forecasting for quantile self-exciting threshold autoregressive time series models
title_full Forecasting for quantile self-exciting threshold autoregressive time series models
title_fullStr Forecasting for quantile self-exciting threshold autoregressive time series models
title_full_unstemmed Forecasting for quantile self-exciting threshold autoregressive time series models
title_sort Forecasting for 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 Y Cai
Yuzhi Cai
format Journal article
container_title Biometrika
container_volume 97
container_issue 1
container_start_page 199
publishDate 2010
institution Swansea University
issn 0006-3444
doi_str_mv 10.1093/biomet/asp070
publisher Oxford University Press
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
document_store_str 0
active_str 0
description Self-exciting threshold autoregressive time series models have been used extensively and the conditional mean obtained from these models can be used to predict the future value of a random variable. In this paper we consider quantile forecasts of a time series based on the quantile self-exciting threshold autoregressive time series models proposed by Cai and Stander (2008) and present a new forecasting method for these quantile models. Simulation studies and application to real time series show that the method works very well.
published_date 2010-12-31T12:13:38Z
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score 11.048042

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