Target PCA: transfer learning large dimensional panel data
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Publication:6664641
DOI10.1016/j.jeconom.2023.105521MaRDI QIDQ6664641
Junting Duan, Ruoxuan Xiong, Markus Pelger
Publication date: 16 January 2025
Published in: Journal of Econometrics (Search for Journal in Brave)
factor analysismissing dataprincipal componentslarge \(N\) and \(T\)transfer learningweak factorsmultiple data sets
Statistics (62-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)
Cites Work
- Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data
- Are more data always better for factor analysis?
- Asymptotics of the principal components estimator of large factor models with weakly influential factors
- Regression models with mixed sampling frequencies
- Large-dimensional factor modeling based on high-frequency observations
- On factor models with random missing: EM estimation, inference, and cross validation
- Estimating latent asset-pricing factors
- On time-varying factor models: estimation and testing
- Exact matrix completion via convex optimization
- 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
- Eigenvalue Ratio Test for the Number of Factors
- GLS Estimation of Dynamic Factor Models
- Mixed data sampling (MIDAS) regression models
- The central role of the propensity score in observational studies for causal effects
- State Space Models and MIDAS Regressions
- Inference and uncertainty quantification for noisy matrix completion
- Panel Data Models With Interactive Fixed Effects
- EFFICIENT ESTIMATION OF FACTOR MODELS
- 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
- Projected principal component analysis in factor models
- Approximate factor models with weaker loadings
- State-Varying Factor Models of Large Dimensions
- Interpretable Sparse Proximate Factors for Large Dimensions
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