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A forecasting procedure for nonlinear autoregressive time series models

Yuzhi Cai Orcid Logo

Journal of Forecasting, Volume: 24

Swansea University Author: Yuzhi Cai Orcid Logo

Abstract

Forecasting for nonlinear time series is an important topic intime series analysis. Existing numerical algorithms for multi-stepahead forecasting ignore accuracy checking, alternative MonteCarlo methods are also computationally very demanding and itsaccuracy is difficult to control too. In this pape...

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Published in: Journal of Forecasting
Published: 2005
URI: https://cronfa.swan.ac.uk/Record/cronfa15295
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first_indexed 2013-08-22T01:57:37Z
last_indexed 2018-02-09T04:47:08Z
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spelling 2013-07-30T10:57:14.5077091 v2 15295 2013-07-30 A forecasting procedure for nonlinear autoregressive time series models eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2013-07-30 BAF Forecasting for nonlinear time series is an important topic intime series analysis. Existing numerical algorithms for multi-stepahead forecasting ignore accuracy checking, alternative MonteCarlo methods are also computationally very demanding and itsaccuracy is difficult to control too. In this paper a numericalforecasting procedure for nonlinear autoregressive time seriesmodels is proposed.The forecasting procedure can be used to obtain approximate \(m\)-step ahead predictiveprobability density function, predictive distribution function, predictive mean and variance etc. for a range of nonlinearautoregressive time series models. Examples in the paper showthat the forecasting procedure works very well both in theaccuracy of the results and in the ability of dealing withdifferent nonlinear autoregressive time series models. Journal Article Journal of Forecasting 24 351 Chapman-Kolmogorov equation; Forecasting procedure; Nonlinear autoregressive Time series. 30 6 2005 2005-06-30 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2013-07-30T10:57:14.5077091 2013-07-30T10:57:14.5077091 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yuzhi Cai 0000-0003-3509-9787 1
title A forecasting procedure for nonlinear autoregressive time series models
spellingShingle A forecasting procedure for nonlinear autoregressive time series models
Yuzhi Cai
title_short A forecasting procedure for nonlinear autoregressive time series models
title_full A forecasting procedure for nonlinear autoregressive time series models
title_fullStr A forecasting procedure for nonlinear autoregressive time series models
title_full_unstemmed A forecasting procedure for nonlinear autoregressive time series models
title_sort A forecasting procedure for nonlinear autoregressive time series models
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 Forecasting
container_volume 24
publishDate 2005
institution Swansea University
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 Forecasting for nonlinear time series is an important topic intime series analysis. Existing numerical algorithms for multi-stepahead forecasting ignore accuracy checking, alternative MonteCarlo methods are also computationally very demanding and itsaccuracy is difficult to control too. In this paper a numericalforecasting procedure for nonlinear autoregressive time seriesmodels is proposed.The forecasting procedure can be used to obtain approximate \(m\)-step ahead predictiveprobability density function, predictive distribution function, predictive mean and variance etc. for a range of nonlinearautoregressive time series models. Examples in the paper showthat the forecasting procedure works very well both in theaccuracy of the results and in the ability of dealing withdifferent nonlinear autoregressive time series models.
published_date 2005-06-30T03:17:25Z
_version_ 1763750388163411968
score 11.013731

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