Testing for Trends in High-Dimensional Time Series

Likai Chen and Wei Biao Wu

Journal of the American Statistical Association, 2019, vol. 114, issue 526, 869-881

Abstract: The article considers statistical inference for trends of high-dimensional time series. Based on a modified L2$\mathcal {L}^2$ 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.

Date: 2019
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Citations: View citations in EconPapers (5)

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DOI: 10.1080/01621459.2018.1456935

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