Projected principal component analysis in factor models

TL;DR

This paper proposes Projected-PCA, a method that improves factor estimation in high-dimensional models by leveraging covariates, leading to faster convergence and better accuracy even with limited data.

Contribution

It introduces a novel projected PCA technique that enhances factor estimation by incorporating covariates, with theoretical convergence rates and nonparametric testing procedures.

Findings

Projected-PCA outperforms traditional PCA in high-dimensional settings.

Faster convergence rates for factor loadings are achieved.

Method is validated on simulated data and S&P 500 index components.

Abstract

This paper introduces a Projected Principal Component Analysis (Projected-PCA), which employs principal component analysis to the projected (smoothed) data matrix onto a given linear space spanned by covariates. When it applies to high-dimensional factor analysis, the projection removes noise components. We show that the unobserved latent factors can be more accurately estimated than the conventional PCA if the projection is genuine, or more precisely, when the factor loading matrices are related to the projected linear space. When the dimensionality is large, the factors can be estimated accurately even when the sample size is finite. We propose a flexible semiparametric factor model, which decomposes the factor loading matrix into the component that can be explained by subject-specific covariates and the orthogonal residual component. The covariates' effects on the factor loadings are further modeled by the additive model via sieve approximations. By using the newly proposed Projected-PCA, the rates of convergence of the smooth factor loading matrices are obtained, which are much faster than those of the conventional factor analysis. The convergence is achieved even when the sample size is finite and is particularly appealing in the high-dimension-low-sample-size situation. This leads us to developing nonparametric tests on whether observed covariates have explaining powers on the loadings and whether they fully explain the loadings. The proposed method is illustrated by both simulated data and the returns of the components of the S&P 500 index.