On factor models with random missing: EM estimation, inference, and cross validation

DOI10.1016/j.jeconom.2020.08.002zbMath1471.62532OpenAlexW2911817888MaRDI QIDQ2024446

Liangjun Su, Ke Miao, Sainan Jin

Publication date: 4 May 2021

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://ink.library.smu.edu.sg/soe_research/2231

zbMATH Keywords

singular value decompositionmatrix completionprincipal component analysiscross-validationmissing at randomfactor modelsexpectation-maximization (EM) algorithm

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (7)

Quantifying noise in survey expectations ⋮ Determining the number of change-points in high-dimensional factor models by cross-validation with matrix completion ⋮ Factor-based imputation of missing values and covariances in panel data of large dimensions ⋮ Large dimensional latent factor modeling with missing observations and applications to causal inference ⋮ Inference for low-rank completion without sample splitting with application to treatment effect estimation ⋮ Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data ⋮ Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components

Cites Work

• Unnamed Item
• A two-step estimator for large approximate dynamic factor models based on Kalman filtering
• Asymptotics of the principal components estimator of large factor models with weakly influential factors
• Maximum likelihood estimation for dynamic factor models with missing data
• Monetary, fiscal and oil shocks: evidence based on mixed frequency structural FAVARs
• Shrinkage estimation of dynamic panel data models with interactive fixed effects
• On time-varying factor models: estimation and testing
• Rank regularized estimation of approximate factor models
• Specification test for panel data models with interactive fixed effects
• Eigenvalue Ratio Test for the Number of Factors
• DYNAMIC LINEAR PANEL REGRESSION MODELS WITH INTERACTIVE FIXED EFFECTS
• Robust principal component analysis?
• A Singular Value Thresholding Algorithm for Matrix Completion
• Testing Hypotheses About the Number of Factors in Large Factor Models
• Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
• Stability Selection
• TESTING HOMOGENEITY IN PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS
• Stable Convergence and Stable Limit Theorems
• Determining the Number of Factors in the General Dynamic Factor Model
• Restricted strong convexity and weighted matrix completion: Optimal bounds with noise
• Inferential Theory for Factor Models of Large Dimensions
• Determining the Number of Factors in Approximate Factor Models
• Large Covariance Estimation by Thresholding Principal Orthogonal Complements

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