Mining the factor zoo: estimation of latent factor models with sufficient proxies
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Publication:6150517
DOI10.1016/j.jeconom.2022.08.013arXiv2212.12845OpenAlexW4319079738MaRDI QIDQ6150517
Runzhe Wan, Wen-Bin Lu, Ying-Ying Li, Rui Song
Publication date: 6 March 2024
Published in: Journal of Econometrics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2212.12845
Statistics (62-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)
Cites Work
- Unnamed Item
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- Regularized estimation in sparse high-dimensional time series models
- Supervised singular value decomposition and its asymptotic properties
- Factor modeling for high-dimensional time series: inference for the number of factors
- High-dimensional covariance matrix estimation in approximate factor models
- Statistical analysis of factor models of high dimension
- On estimation in the reduced-rank regression with a large number of responses and predictors
- Unexplained factors and their effects on second pass \(R\)-squared's
- Determining the number of factors when the number of factors can increase with sample size
- Estimation of (near) low-rank matrices with noise and high-dimensional scaling
- A shrinkage principle for heavy-tailed data: high-dimensional robust low-rank matrix recovery
- Efficient estimation of approximate factor models via penalized maximum likelihood
- Principal fitted components for dimension reduction in regression
- Factor models for matrix-valued high-dimensional time series
- Estimating stable latent factor models by indirect inference
- Robust covariance and scatter matrix estimation under Huber's contamination model
- Estimating latent asset-pricing factors
- Regularized estimation of large covariance matrices
- Robust principal component analysis?
- Sufficient dimension reduction and prediction in regression
- An Intertemporal General Equilibrium Model of Asset Prices
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Forecasting Using Principal Components From a Large Number of Predictors
- High-Dimensional Probability
- Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection
- Vast Portfolio Selection With Gross-Exposure Constraints
- Reduced rank ridge regression and its kernel extensions
- Regularization and Variable Selection Via the Elastic Net
- Selective factor extraction in high dimensions
- Robust reduced-rank regression
- EFFICIENT ESTIMATION OF FACTOR MODELS
- Common risk factors in the returns on stocks and bonds
- Large Covariance Estimation by Thresholding Principal Orthogonal Complements
- Prediction by Supervised Principal Components
