Research PaperResearchia:202610.14054
Principal Component Analysis for High‐Dimensional Approximate Factor Models in Time Series: Assumptions, Asymptotic Theory, and Identification
Matteo Barigozzi
Abstract
Principal Component Analysis for High‐Dimensional Approximate Factor Models in Time Series: Assumptions, Asymptotic Theory, and Identification --- Source: OpenAlex - Journal of Time Series Analysis (Citations: 3) PDF: https://doi.org/10.1111/jtsa.70079 Original Link: https://doi.org/https://doi.org/10.1111/jtsa.70079
Submitted: October 14, 2026Subjects: Open Science; Estimator
Description / Details
Principal Component Analysis for High‐Dimensional Approximate Factor Models in Time Series: Assumptions, Asymptotic Theory, and Identification
Source: OpenAlex - Journal of Time Series Analysis (Citations: 3) PDF: https://doi.org/10.1111/jtsa.70079 Original Link: https://doi.org/https://doi.org/10.1111/jtsa.70079
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Submission Info
Date:
Oct 14, 2026
Oct 14, 2026
Topic:
Estimator
Estimator
Area:
Open Science
Open Science
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