On the penalized maximum likelihood estimation of high-dimensional approximate factor model

TL;DR

This paper introduces an accelerated proximal gradient algorithm combined with EM to improve the estimation of high-dimensional approximate factor models, ensuring positive definiteness and enhanced efficiency.

Contribution

It proposes a novel estimation procedure that guarantees positive definite error covariance matrices and improves efficiency in high-dimensional factor models.

Findings

The new algorithm provides positive definite estimates.

It improves estimation and forecasting efficiency.

Validated through simulation and real data analysis.

Abstract

In this paper, we mainly focus on the penalized maximum likelihood estimation (MLE) of the high-dimensional approximate factor model. Since the current estimation procedure can not guarantee the positive definiteness of the error covariance matrix, by reformulating the estimation of error covariance matrix and based on the lagrangian duality, we propose an accelerated proximal gradient (APG) algorithm to give a positive definite estimate of the error covariance matrix. Combined the APG algorithm with EM method, a new estimation procedure is proposed to estimate the high-dimensional approximate factor model. The new method not only gives positive definite estimate of error covariance matrix but also improves the efficiency of estimation for the high-dimensional approximate factor model. Although the proposed algorithm can not guarantee a global unique solution, it enjoys a desirable non-increasing property. The efficiency of the new algorithm on estimation and forecasting is also investigated via simulation and real data analysis.