Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference. (arXiv:1910.08273v3 [econ.EM] UPDATED)
This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We estimate 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…