Actually, There is No Rotational Indeterminacy in the Approximate Factor Model

Fuente: arXiv
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Main Author: Gersing, Philipp
Format: Preprint
Published: 2024
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author Gersing, Philipp
author_facet Gersing, Philipp
contents We show that in the approximate factor model the population normalised principal components converge in mean square (up to sign) under the standard assumptions for $n\to \infty$. Consequently, we have a generic interpretation of what the principal components estimator is actually identifying and existing results on factor identification are reinforced and refined. Based on this result, we provide a new asymptotic theory for the approximate factor model entirely without rotation matrices. We show that the factors space is consistently estimated with finite $T$ for $n\to \infty$ while consistency of the factors a.k.a the $L^2$ limit of the normalised principal components requires that both $(n, T)\to \infty$.
format Preprint
id arxiv_https___arxiv_org_abs_2408_11676
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Actually, There is No Rotational Indeterminacy in the Approximate Factor Model
Gersing, Philipp
Econometrics
We show that in the approximate factor model the population normalised principal components converge in mean square (up to sign) under the standard assumptions for $n\to \infty$. Consequently, we have a generic interpretation of what the principal components estimator is actually identifying and existing results on factor identification are reinforced and refined. Based on this result, we provide a new asymptotic theory for the approximate factor model entirely without rotation matrices. We show that the factors space is consistently estimated with finite $T$ for $n\to \infty$ while consistency of the factors a.k.a the $L^2$ limit of the normalised principal components requires that both $(n, T)\to \infty$.
title Actually, There is No Rotational Indeterminacy in the Approximate Factor Model
topic Econometrics
url https://arxiv.org/abs/2408.11676