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Principal components for approximate factor models

Published on:

1 November 2023

Primary Category:

Statistics Theory

Paper Authors:

Peiyun Jiang,

Yoshimasa Uematsu,

Takashi Yamagata


Key Details

Shows existence of pseudo-true rotation for approximate factor models

Proves consistency of PC estimators for pseudo-true parameters

Derives asymptotic normality of PC estimators

Allows for weak factor models with differing strength

Applies approach to factor augmented regressions

AI generated summary

Principal components for approximate factor models

This paper explores what the principal component estimators actually estimate for approximate factor models. It shows that under mild assumptions, the model can be rotated to a pseudo-true version that is separately identifiable. The paper proves consistency and asymptotic normality of the estimators relative to this pseudo-true parameter.

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