Large dimensional latent factor modeling with missing observations and applications to causal inference

Ruoxuan Xiong and Markus Pelger

Journal of Econometrics, 2023, vol. 233, issue 1, 271-301

Abstract: 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. The unobserved control group is modeled as missing values, which are inferred from the latent factor model. The inferential theory for the imputed values allows us to test for individual treatment effects at any time under general adoption patterns where the units can be affected by unobserved factors.

Keywords: Principal components; Synthetic control; Treatment effect; Large N and T; Matrix completion (search for similar items in EconPapers)
JEL-codes: C14 C38 C55 G12 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Working Paper: Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:233:y:2023:i:1:p:271-301

DOI: 10.1016/j.jeconom.2022.04.005

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