Large sample behaviour of high dimensional autocovariance matrices

Abstract

The existence of limiting spectral distribution (LSD) of ${\hat{\Gamma }}_u+{\hat{\Gamma }}_u^{\ast }$, the symmetric sum of the sample autocovariance matrix ${\hat{\Gamma }}_u$ of order $u$, is known when the observations are from an infinite dimensional vector linear process with appropriate (strong) assumptions on the coefficient matrices. Under significantly weaker conditions, we prove, in a unified way, that the LSD of any symmetric polynomial in these matrices such as ${\hat{\Gamma }}_u+{\hat{\Gamma }}_u^{\ast }$, ${\hat{\Gamma }}_u{\hat{\Gamma }}_u^{\ast }$, ${\hat{\Gamma }}_u{\hat{\Gamma }}_u^{\ast }+{\hat{\Gamma }}_k{\hat{\Gamma }}_k^{\ast }$ exist. Our approach is through the more intuitive algebraic method of free probability in conjunction with the method of moments. Thus, we are able to provide a general description for the limits in terms of some freely independent variables. All the previous results follow as special cases. We suggest statistical uses of these LSD and related results in order determination and white noise testing.

arXiv commentPublished at http://dx.doi.org/10.1214/15-AOS1378 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (5)

• A note on a Marčenko-Pastur type theorem for time series
• On the Marčenko-Pastur law for linear time series
• Large sample behaviour of high dimensional autocovariance matrices
• Eigenvalue distribution of large sample covariance matrices of linear processes
• Spectral Analysis of High-dimensional Time Series

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