Target PCA: Transfer learning large dimensional panel data

Junting Duan, Markus Pelger and Ruoxuan Xiong

Journal of Econometrics, 2024, vol. 244, issue 2

Abstract: This paper develops a novel method to estimate a latent factor model for a large target panel with missing observations by optimally using the information from auxiliary panel data sets. We refer to our estimator as target-PCA. Transfer learning from auxiliary panel data allows us to deal with a large fraction of missing observations and weak signals in the target panel. We show that our estimator is more efficient and can consistently estimate weak factors, which are not identifiable with conventional methods. We provide the asymptotic inferential theory for target-PCA under very general assumptions on the approximate factor model and missing patterns. In an empirical study of imputing data in a mixed-frequency macroeconomic panel, we demonstrate that target-PCA significantly outperforms all benchmark methods.

Keywords: Factor analysis; Principal components; Transfer learning; Multiple data sets; Large N and T; Missing data; Weak factors (search for similar items in EconPapers)
JEL-codes: C33 C38 C55 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:244:y:2024:i:2:s0304407623002373

DOI: 10.1016/j.jeconom.2023.105521

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