A Note on Mixing in High Dimensional Time Series

Abstract

Various mixing conditions have been imposed on high dimensional time series, including the strong mixing ($\alpha$-mixing), maximal correlation coefficient ($\rho$-mixing), absolute regularity ($\beta$-mixing), and $\phi$-mixing. $\alpha$-mixing condition is a routine assumption when studying autoregression models. $\rho$-mixing can lead to $\alpha$-mixing. In this paper, we prove a way to verify $\rho$-mixing under a high-dimensional triangular array time series setting by using the Pearson's $\phi^2$, mean square contingency. Vector autoregression model VAR(1) and vector autoregression moving average VARMA(1,1) are proved satisfying $\rho$-mixing condition based on low rank setting.