Target PCA: Transfer Learning Large Dimensional Panel Data

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-Dimensional Panel Data, Large N and T, Missing Data, Weak Factors, Causal Inference

JEL Classification: C14, C38, C55, G12

Suggested Citation: Suggested Citation

Duan, Junting and Pelger, Markus and Xiong, Ruoxuan, Target PCA: Transfer Learning Large Dimensional Panel Data (December 14, 2022). Journal of Econometrics, accepted, Available at SSRN: https://ssrn.com/abstract=4302869 or http://dx.doi.org/10.2139/ssrn.4302869