FUNCTIONAL-COEFFICIENT AUTOREGRESSIVE MODELS

• 成果类型: Article
• 署名作者: CHEN, R; TSAY, RS
• 署名单位: University of Chicago
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2290725
• 发表日期: 1993
• 页码: 298-308
• 关键词: nonlinear time-series

摘要

In this article we propose a new class of models for nonlinear time series analysis, investigate properties of the proposed model, and suggest a modeling procedure for building such a model. The proPosed modeling procedure makes use of ideas from both parametric and nonparametric statistics, A consistency result is given to support the procedure. For illustration we apply the proposed model and procedure to several data sets and show that the resulting models substantially improve postsample multi-step ahead forecasts over other models.