# Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data

*Jushan Bai* and
*Serena Ng* ()

*Journal of the American Statistical Association*, 2021, vol. 116, issue 536, 1746-1763

**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.

**Date:** 2021

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Working Paper: Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data (2021)

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**Persistent link:** https://EconPapers.repec.org/RePEc:taf:jnlasa:v:116:y:2021:i:536:p:1746-1763

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**DOI:** 10.1080/01621459.2021.1967163

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