Autoregressive model selection based on a prediction perspective

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题名:	Autoregressive model selection based on a prediction perspective
作者:	陳春樹;Lee, Yun-Huan;Chen, Chun-Shu
贡献者:	理學院統計研究所
关键词:	Akaike information criterion;Applied statistics;Bayesian analysis;Bayesian information criterion;Criteria;Degrees of freedom;Estimating techniques;generalized degrees of freedom;Mathematical analysis;Mathematical models;mean-squared prediction error;Perturbation methods;Raw materials;Regression analysis;Semiconductors;Studies;time series
日期:	2012-04-01
上传时间:	2026-04-23 12:51:08 (UTC+8)
出版者:	Routledge;Abingdon: Taylor & Francis
摘要:	摘要： The autoregressive (AR) model is a popular method for fitting and prediction in analyzing time-dependent data, where selecting an accurate model among considered orders is a crucial issue. Two commonly used selection criteria are the Akaike information criterion and the Bayesian information criterion. However, the two criteria are known to suffer potential problems regarding overfit and underfit, respectively. Therefore, using them would perform well in some situations, but poorly in others. In this paper, we propose a new criterion in terms of the prediction perspective based on the concept of generalized degrees of freedom for AR model selection. We derive an approximately unbiased estimator of mean-squared prediction errors based on a data perturbation technique for selecting the order parameter, where the estimation uncertainty involved in a modeling procedure is considered. Some numerical experiments are performed to illustrate the superiority of the proposed method over some commonly used order selection criteria. Finally, the methodology is applied to a real data example to predict the weekly rate of return on the stock price of Taiwan Semiconductor Manufacturing Company and the results indicate that the proposed method is satisfactory. 出版者： Abingdon: Taylor & Francis 出版日期： 2012-04 出處： Journal of applied statistics, 2012-04, Vol.39 (4), p.913-922 資源來源： Taylor & Francis Journals Auto-Holdings Collection 版權： Copyright Taylor & Francis Group, LLC 2012 版權： Copyright Taylor & Francis Ltd. 2012 識別號： ISSN: 0266-4763 識別號： EISSN: 1360-0532 識別號： DOI: 10.1080/02664763.2011.636418
显示于类别:	[統計研究所] 期刊論文

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