Factor-guided estimation of large covariance matrix function with conditional functional sparsity

Dong Li, Xinghao Qiao and Zihan Wang

Journal of Econometrics, 2025, vol. 251, issue C

Abstract: This paper addresses the fundamental task of estimating covariance matrix functions for high-dimensional functional data/functional time series. We consider two functional factor structures encompassing either functional factors with scalar loadings or scalar factors with functional loadings, and postulate functional sparsity on the covariance of idiosyncratic errors after taking out the common unobserved factors. To facilitate estimation, we rely on the spiked matrix model and its functional generalization, and derive some novel asymptotic identifiability results, based on which we develop DIGIT and FPOET estimators under two functional factor models, respectively. Both estimators involve performing associated eigenanalysis to estimate the covariance of common components, followed by adaptive functional thresholding applied to the residual covariance. We also develop functional information criteria for model selection with theoretical guarantees. The convergence rates of involved estimated quantities are respectively established for DIGIT and FPOET estimators. Numerical studies including extensive simulations and a real data application on functional portfolio allocation are conducted to examine the finite-sample performance of the proposed methodology.

Keywords: Adaptive functional thresholding; Asymptotic identifiability; Eigenanalysis; Functional factor model; High-dimensional functional time series; Model selection (search for similar items in EconPapers)
JEL-codes: C13 C32 C38 C55 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:251:y:2025:i:c:s0304407625001241

DOI: 10.1016/j.jeconom.2025.106070

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