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Journal article 1311 views

A new Bayesian approach to quantile autoregressive time series model estimation and forecasting

Yuzhi Cai Orcid Logo, Julian Stander, Neville Davies

Journal of Time Series Analysis, Volume: 33, Issue: 4, Pages: 684 - 698

Swansea University Author: Yuzhi Cai Orcid Logo

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DOI (Published version): 10.1111/j.1467-9892.2012.00800.x

Abstract

This paper proposes a Bayesian approach to quantile autoregressive (QAR) time series model estimation andforecasting. We establish that the joint posterior distribution of the model parameters and future values is welldefined. The associated Markov chain Monte Carlo algorithm for parameter estimatio...

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Published in: Journal of Time Series Analysis
ISSN: 0143-9782
Published: wileyonlinelibrary.com 2012
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URI: https://cronfa.swan.ac.uk/Record/cronfa11711
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first_indexed 2013-07-23T12:06:09Z
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spelling 2016-05-01T15:17:17.5568052 v2 11711 2012-06-19 A new Bayesian approach to quantile autoregressive time series model estimation and forecasting eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2012-06-19 BAF This paper proposes a Bayesian approach to quantile autoregressive (QAR) time series model estimation andforecasting. We establish that the joint posterior distribution of the model parameters and future values is welldefined. The associated Markov chain Monte Carlo algorithm for parameter estimation and forecasting convergesto the posterior distribution quickly. We also present a combining forecasts technique to produce more accurateout-of-sample forecasts by using a weighted sequence of fitted QAR models. A moving window method to checkthe quality of the estimated conditional quantiles is developed. We verify our methodology using simulationstudies and then apply it to currency exchange rate data. The results obtained show that an unequally weightedcombining method performs better than other forecasting methodology. Journal Article Journal of Time Series Analysis 33 4 684 698 wileyonlinelibrary.com 0143-9782 Combining forecasts; MCMC; quantile modelling; quantile forecasting; predictive density functions 30 6 2012 2012-06-30 10.1111/j.1467-9892.2012.00800.x COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2016-05-01T15:17:17.5568052 2012-06-19T09:57:01.8547617 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yuzhi Cai 0000-0003-3509-9787 1 Julian Stander 2 Neville Davies 3
title A new Bayesian approach to quantile autoregressive time series model estimation and forecasting
spellingShingle A new Bayesian approach to quantile autoregressive time series model estimation and forecasting
Yuzhi Cai
title_short A new Bayesian approach to quantile autoregressive time series model estimation and forecasting
title_full A new Bayesian approach to quantile autoregressive time series model estimation and forecasting
title_fullStr A new Bayesian approach to quantile autoregressive time series model estimation and forecasting
title_full_unstemmed A new Bayesian approach to quantile autoregressive time series model estimation and forecasting
title_sort A new Bayesian approach to quantile autoregressive time series model estimation and forecasting
author_id_str_mv eff7b8626ab4cc6428eef52516fda7d6
author_id_fullname_str_mv eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
author Yuzhi Cai
author2 Yuzhi Cai
Julian Stander
Neville Davies
format Journal article
container_title Journal of Time Series Analysis
container_volume 33
container_issue 4
container_start_page 684
publishDate 2012
institution Swansea University
issn 0143-9782
doi_str_mv 10.1111/j.1467-9892.2012.00800.x
publisher wileyonlinelibrary.com
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 This paper proposes a Bayesian approach to quantile autoregressive (QAR) time series model estimation andforecasting. We establish that the joint posterior distribution of the model parameters and future values is welldefined. The associated Markov chain Monte Carlo algorithm for parameter estimation and forecasting convergesto the posterior distribution quickly. We also present a combining forecasts technique to produce more accurateout-of-sample forecasts by using a weighted sequence of fitted QAR models. A moving window method to checkthe quality of the estimated conditional quantiles is developed. We verify our methodology using simulationstudies and then apply it to currency exchange rate data. The results obtained show that an unequally weightedcombining method performs better than other forecasting methodology.
published_date 2012-06-30T03:13:33Z
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score 10.998116

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