Semiparametric non-linear time series model selection

• 成果类型: Article
• 署名作者: Gao, JT; Tong, H
• 署名单位: University of Western Australia; University of Hong Kong; University of London; London School Economics & Political Science
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1369-7412.2004.05303.x
• 发表日期: 2004
• 页码: 321-336
• 关键词: Nonparametric identification; Asymptotic Normality; Adaptive estimation; variable selection; regression; CHOICE; tests

摘要

Semiparametric time series regression is often used without checking its suitability, resulting in an unnecessarily complicated model. In practice, one may encounter computational difficulties caused by the curse of dimensionality. The paper suggests that to provide more precise predictions we need to choose the most significant regressors for both the parametric and the nonparametric time series components. We develop a novel cross-validation-based model selection procedure for the simultaneous choice of both the parametric and the nonparametric time series components, and we establish some asymptotic properties of the model selection procedure proposed. In addition, we demonstrate how to implement it by using both simulated and real examples. Our empirical studies show that the procedure works well.