Testing for Trends in High-Dimensional Time Series

• 成果类型: Article
• 署名作者: Chen, Likai; Wu, Wei Biao
• 署名单位: University of Chicago
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2018.1456935
• 发表日期: 2019
• 页码: 869-881
• 关键词: integrated square error; Nonparametric Regression; parametric assumptions; bandwidth selection; kernel regression; EQUALITY; estimators; CURVES; models; CHOICE

摘要

The article considers statistical inference for trends of high-dimensional time series. Based on a modified distance between parametric and nonparametric trend estimators, we propose a de-diagonalized quadratic form test statistic for testing patterns on trends, such as linear, quadratic, or parallel forms. We develop an asymptotic theory for the test statistic. A Gaussian multiplier testing procedure is proposed and it has an improved finite sample performance. Our testing procedure is applied to a spatial temporal temperature data gathered from various locations across America. A simulation study is also presented to illustrate the performance of our testing method. Supplementary materials for this article are available online.