Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data

DOI10.48550/arXiv.1910.06677zbMath1506.62236arXiv1910.06677OpenAlexW3195607716MaRDI QIDQ91408

Jushan Bai, Serena Ng, Jushan Bai, Serena Ng

Publication date: 15 October 2019

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.06677

zbMATH Keywords

EM algorithmmissing-at-randomnuclear-norm regularizationsynthetic controls

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Missing data (62D10)

Related Items (10)

Prediction Intervals for Synthetic Control Methods ⋮ High-dimensional latent panel quantile regression with an application to asset pricing ⋮ Determining the number of change-points in high-dimensional factor models by cross-validation with matrix completion ⋮ High-dimensional conditionally Gaussian state space models with missing data ⋮ Factor-based imputation of missing values and covariances in panel data of large dimensions ⋮ Treatment effects in interactive fixed effects models with a small number of time periods ⋮ Inference for low-rank completion without sample splitting with application to treatment effect estimation ⋮ Confidence intervals of treatment effects in panel data models with interactive fixed effects ⋮ tensorFun ⋮ Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components

Uses Software

• RegEM
• astsa

Cites Work

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• Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
• Inference and missing data
• Forecasting Using Principal Components From a Large Number of Predictors
• Panel Data Models With Interactive Fixed Effects
• Inferential Theory for Factor Models of Large Dimensions
• Determining the Number of Factors in Approximate Factor Models

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