Two sample tests for high-dimensional autocovariances

Changryong Baek, Katheleen M. Gates, Benjamin Leinwand and Vladas Pipiras

Computational Statistics & Data Analysis, 2021, vol. 153, issue C

Abstract: The problem of testing for the equality of autocovariances of two independent high-dimensional time series is studied. Tests based on the suprema or sums of suitable averages across the dimensions are adapted from the available literature. Another test based on principal component analysis (PCA) is introduced and studied in theory. An extension is also considered to the setting of testing for the equality of autocovariances of two populations, having multiple individual high-dimensional series from the two populations. The proposed methodologies are assessed on simulated data, with the performance of the introduced PCA testing being superior overall. An application using fMRI data from individuals experiencing two different emotional states is provided.

Keywords: High-dimensional time series; Autocovariances; Block multiplier bootstrap; Dynamic factor models; Principal components; Hypothesis tests (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:153:y:2021:i:c:s0167947320301584

DOI: 10.1016/j.csda.2020.107067

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