When can weak latent factors be statistically inferred?

TL;DR

This paper develops a new non-asymptotic theory for PCA under weak factor models with cross-sectional dependence, enabling more accurate inference in high-dimensional settings with minimal factor strength.

Contribution

It introduces a novel estimation and inference framework for PCA with weak factors, applicable regardless of the N and T growth rates, and provides finite-sample error bounds and statistical tests.

Findings

Asymptotic normality holds when SNR grows faster than a polynomial in log N.

The theory applies to cases with cross-sectional dependence and minimal factor strength.

Empirical results reveal correlations between test outcomes and economic cycles.

Abstract

This article establishes a new and comprehensive estimation and inference theory for principal component analysis (PCA) under the weak factor model that allow for cross-sectional dependent idiosyncratic components under the nearly minimal factor strength relative to the noise level or signal-to-noise ratio. Our theory is applicable regardless of the relative growth rate between the cross-sectional dimension $N$ and temporal dimension $T$. This more realistic assumption and noticeable result require completely new technical device, as the commonly-used leave-one-out trick is no longer applicable to the case with cross-sectional dependence. Another notable advancement of our theory is on PCA inference $ - $ for example, under the regime where $N\asymp T$, we show that the asymptotic normality for the PCA-based estimator holds as long as the signal-to-noise ratio (SNR) grows faster than a polynomial rate of $\log N$. This finding significantly surpasses prior work that required a polynomial rate of $N$. Our theory is entirely non-asymptotic, offering finite-sample characterizations for both the estimation error and the uncertainty level of statistical inference. A notable technical innovation is our closed-form first-order approximation of PCA-based estimator, which paves the way for various statistical tests. Furthermore, we apply our theories to design easy-to-implement statistics for validating whether given factors fall in the linear spans of unknown latent factors, testing structural breaks in the factor loadings for an individual unit, checking whether two units have the same risk exposures, and constructing confidence intervals for systematic risks. Our empirical studies uncover insightful correlations between our test results and economic cycles.