The Annals of Applied Probability

Limiting spectral distribution of a symmetrized auto-cross covariance matrix

Baisuo Jin, Chen Wang, Z. D. Bai, K. Krishnan Nair, and Matthew Harding

Full-text: Open access

Abstract

This paper studies the limiting spectral distribution (LSD) of a symmetrized auto-cross covariance matrix. The auto-cross covariance matrix is defined as $\mathbf{M}_{\tau}=\frac{1}{2T}\sum_{j=1}^{T}(\mathbf{e} _{j}\mathbf{e} _{j+\tau}^{*}+\mathbf{e} _{j+\tau}\mathbf{e} _{j}^{*})$, where $\mathbf{e} _{j}$ is an $N$ dimensional vectors of independent standard complex components with properties stated in Theorem 1.1, and $\tau$ is the lag. $\mathbf{M}_{0}$ is well studied in the literature whose LSD is the Marčenko–Pastur (MP) Law. The contribution of this paper is in determining the LSD of $\mathbf{M}_{\tau}$ where $\tau\ge1$. It should be noted that the LSD of the $\mathbf{M}_{\tau}$ does not depend on $\tau$. This study arose from the investigation of and plays an key role in the model selection of any large dimensional model with a lagged time series structure, which is central to large dimensional factor models and singular spectrum analysis.

Article information

Source
Ann. Appl. Probab., Volume 24, Number 3 (2014), 1199-1225.

Dates
First available in Project Euclid: 23 April 2014

Permanent link to this document
https://projecteuclid.org/euclid.aoap/1398258099

Digital Object Identifier
doi:10.1214/13-AAP945

Mathematical Reviews number (MathSciNet)
MR3199984

Zentralblatt MATH identifier
1296.60006

Subjects
Primary: 60F15: Strong theorems 15A52 62H25: Factor analysis and principal components; correspondence analysis
Secondary: 60F05: Central limit and other weak theorems 60F17: Functional limit theorems; invariance principles

Keywords
Auto-cross covariance factor analysis Marčenko–Pastur law limiting spectral distribution order detection random matrix theory Stieltjes transform

Citation

Jin, Baisuo; Wang, Chen; Bai, Z. D.; Nair, K. Krishnan; Harding, Matthew. Limiting spectral distribution of a symmetrized auto-cross covariance matrix. Ann. Appl. Probab. 24 (2014), no. 3, 1199--1225. doi:10.1214/13-AAP945. https://projecteuclid.org/euclid.aoap/1398258099


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