Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data

By Jushan Bai, Serena Ng. 2019

130 citations recorded by the source corpus.

Abstract This article proposes an imputation procedure that uses the factors estimated from a tall block along with the re-rotated loadings estimated from a wide block to impute missing values in a panel of data. Assuming that a strong factor structure holds for the full panel of data and its sub-blocks, it is shown that the common component can be consistently estimated at four different rates of convergence without requiring regularization or iteration. An asymptotic analysis of the estimation error is obtained. An application of our analysis is estimation of counterfactuals when potential outcomes have a factor structure. We study the estimation of average and individual treatment effects on the treated and establish a normal distribution theory that can be useful for hypothesis testing.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/14e8db17524128079ff94d50ad4593d396531218

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Citations in the corpus

Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference (2019, unknown): “ankrupt. The inferential theory of large dimensional factor models with missing observations is an active area of research. Our paper is most closely related to the recent papers by Su et al. (2019), Bai and Ng (2019) and Chen et al. (2019). The papers dier in the algorithms to impute the missing ob”
Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects (2021, background): “They include Bai and Ng (2019); Brodersen et al.”