Factor models with local factors — Determining the number of relevant factors

Simon Freyaldenhoven

Journal of Econometrics, 2022, vol. 229, issue 1, 80-102

Abstract: We extend the theory on factor models by incorporating “local” factors into the model. Local factors affect only an unknown subset of the observed variables. This implies a continuum of eigenvalues of the covariance matrix, as is commonly observed in applications. We derive which factors are pervasive enough to be economically important and which factors are pervasive enough to be estimable using the common principal component estimator. We then introduce a new class of estimators to determine the number of those relevant factors. Unlike existing estimators, our estimators use not only the eigenvalues of the covariance matrix, but also its eigenvectors. We find that incorporating partial sums of the eigenvectors into our estimators leads to significant gains in performance in simulations.

Keywords: High-dimensional data; Factor models; Weak factors; Local factors; Sparsity (search for similar items in EconPapers)
JEL-codes: C38 C52 C55 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Working Paper: Factor Models with Local Factors—Determining the Number of Relevant Factors (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:229:y:2022:i:1:p:80-102

DOI: 10.1016/j.jeconom.2021.04.006

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