Estimation of Non-Gaussian Factors Using Higher-order Multi-cumulants in Weak Factor Models
37 Pages Posted: 12 Feb 2021 Last revised: 31 Jul 2023
Date Written: July 31, 2023
We estimate the latent factors in high-dimensional non-Gaussian panel data using the eigenvalue decomposition of the product between the higher-order multi-cumulant and its transpose. The proposed Higher order multi-cumulant Factor Analysis (HFA) approach comprises an eigenvalue ratio test to select the number of non-Gaussian factors and uses the eigenvector to estimate the factor loadings. Unlike covariance-based approaches, HFA remains reliable for estimating the non-Gaussian factors in weak factor models with Gaussian error terms. Simulation results confirm that HFA estimators improve the accuracy of factor selection and estimation compared to covariance-based approaches. We illustrate the use of HFA to detect and estimate the factors for the FRED-MD data set and use them to forecast the monthly S&P 500 equity premium.
Keywords: Higher-order multi-cumulants; High-dimensional factor models; Weak factors; Consistency; Singular values
JEL Classification: G11, G12, G15
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