Denoising and Multilinear Projected-Estimation of High-Dimensional Matrix-Variate Factor Time Series

TL;DR

This paper introduces a novel multi-linear projection method for denoising and estimating high-dimensional matrix-variate factor time series, improving convergence and forecasting accuracy in noisy, high-dimensional settings.

Contribution

It develops an iterative projection and two-way PCA approach for better estimation and noise mitigation, with proven asymptotic properties and demonstrated finite-sample performance.

Findings

Method achieves faster convergence than traditional techniques.

Proposed approach improves out-of-sample forecasting accuracy.

Effective even with serially correlated idiosyncratic terms.

Abstract

This paper proposes a new multi-linear projection method for denoising and estimation of high-dimensional matrix-variate factor time series. It assumes that a $p_1\times p_2$ matrix-variate time series consists of a dynamically dependent, lower-dimensional matrix-variate factor process and a $p_1\times p_2$ matrix idiosyncratic series. In addition, the latter series assumes a matrix-variate factor structure such that its row and column covariances may have diverging/spiked eigenvalues to accommodate the case of low signal-to-noise ratio often encountered in applications. We use an iterative projection procedure to reduce the dimensions and noise effects in estimating front and back loading matrices and to obtain faster convergence rates than those of the traditional methods available in the literature. We further introduce a two-way projected Principal Component Analysis to mitigate the diverging noise effects, and implement a high-dimensional white-noise testing procedure to estimate the dimension of the matrix factor process. Asymptotic properties of the proposed method are established if the dimensions and sample size go to infinity. We also use simulations and real examples to assess the performance of the proposed method in finite samples and to compare its forecasting ability with some existing ones in the literature. The proposed method fares well in out-of-sample forecasting. In a supplement, we demonstrate the efficacy of the proposed approach even when the idiosyncratic terms exhibit serial correlations with or without a diverging white noise effect.