Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference

Understand

This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations.

• We propose an easy-to-use all-purpose estimator for a latent factor model by applying principal component analysis to an adjusted covariance matrix estimated from partially observed panel data.
• We derive the asymptotic distribution for the estimated factors, loadings and the imputed values under an approximate factor model and general missing patterns.
• The key application is to estimate counterfactual outcomes in causal inference from panel data.