Inference for non-stationary time series regression with or without inequality constraints

• 成果类型: Article
• 署名作者: Zhou, Zhou
• 署名单位: University of Toronto
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12077
• 发表日期: 2015
• 页码: 349-371
• 关键词: change-point estimation; likelihood ratio test; linear-regression; Nonparametric Regression; structural-change; diffusion-models; dependence; parameter; bootstrap

摘要

We consider statistical inference for time series linear regression where the response and predictor processes may experience general forms of abrupt and smooth non-stationary behaviours over time. Meanwhile, the regression parameters may be subject to linear inequality constraints. A simple and unified procedure for structural stability checks and parameter inference is proposed. In the case where the regression parameters are constrained, the methodology proposed is shown to be consistent whether or not the true regression parameters are on the boundary of the restricted parameter space via utilizing an asymptotically invariant geometric property of polyhedral cones.