Latent Factor Models with Functional Single-Index Loadings

Jean-David Fermanian () and Léonard Thélot ()
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Jean-David Fermanian: ENSAE - Ecole Nationale de la Statistique et de l'Analyse Economique - Ecole Nationale de la Statistique et de l'Analyse Economique
Léonard Thélot: ENSAE - Ecole Nationale de la Statistique et de l'Analyse Economique - Ecole Nationale de la Statistique et de l'Analyse Economique

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Abstract: We extend static linear factor models in a semiparametric framework, by assuming the loadings are unknown functions that depend on some exogeneous covariates. Since the latter covariates may be possibly numerous, their effect is summarized through individual univariate indices. We state the consistency and the asymptotic normality of our estimated factors and loading functions, when the number of individuals and their history length go to the infinity (large N , large T asymptotics). By simulation and on real data, the relevance of this approach is empirically assessed.

Keywords: Latent factors; single-index; principal components; identifiability; kernel regression (search for similar items in EconPapers)
Date: 2025-08
Note: View the original document on HAL open archive server: https://hal.science/hal-05528979v1
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