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.

翻译：本文提出了一种新方法，通过最优利用辅助面板数据集中的信息，来估计存在缺失观测值的大维目标面板的潜在因子模型。我们将该估计量称为目标-PCA。利用辅助面板数据的迁移学习，使我们能够处理目标面板中大量缺失观测值和弱信号的问题。我们证明，该估计量更高效，且能一致地估计传统方法无法识别的弱因子。在关于近似因子模型和缺失模式的非常一般性假设下，我们给出了目标-PCA的渐近推断理论。通过一项关于混合频率宏观经济面板数据插值的实证研究，我们展示了目标-PCA在所有基准方法中表现显著优越。