Sufficient forecasting using factor models
From MaRDI portal
Publication:75240
DOI10.1016/j.jeconom.2017.08.009zbMath1377.62185arXiv1505.07414OpenAlexW3121936541WikidataQ55020312 ScholiaQ55020312MaRDI QIDQ75240
Jiawei Yao, Jianqing Fan, Lingzhou Xue, Jiawei Yao, Jianqing Fan
Publication date: December 2017
Published in: Journal of Econometrics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1505.07414
asymptotic propertiesprincipal component analysisforecastingfactor modelsliced inverse regressionnonparametric forecastinglearning indices
Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Factor analysis and principal components; correspondence analysis (62H25)
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Cites Work
- Unnamed Item
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- Estimating the Technology of Cognitive and Noncognitive Skill Formation
- Are more data always better for factor analysis?
- Forecasting economic time series using targeted predictors
- High dimensional covariance matrix estimation using a factor model
- Factor modeling for high-dimensional time series: inference for the number of factors
- Nonparametric regression with nonparametrically generated covariates
- The three-pass regression filter: a new approach to forecasting using many predictors
- Improved penalization for determining the number of factors in approximate factor models
- On almost linearity of low dimensional projections from high dimensional data
- Regularized rank-based estimation of high-dimensional nonparanormal graphical models
- High-dimensional semiparametric Gaussian copula graphical models
- Dynamic factor models with infinite-dimensional factor spaces: one-sided representations
- Principal components estimation and identification of static factors
- Eigenvalue Ratio Test for the Number of Factors
- Efficient Semiparametric Estimation of the Fama-French Model and Extensions
- A general framework for multiple testing dependence
- Learning Deep Architectures for AI
- Sliced Inverse Regression for Dimension Reduction
- Determining the Dimensionality in Sliced Inverse Regression
- Forecasting Using Principal Components From a Large Number of Predictors
- Estimating False Discovery Proportion Under Arbitrary Covariance Dependence
- SEMIPARAMETRIC ESTIMATION OF PARTIALLY LINEAR MODELS FOR DEPENDENT DATA WITH GENERATED REGRESSORS
- Determining the Number of Factors in the General Dynamic Factor Model
- Inferential Theory for Factor Models of Large Dimensions
- Determining the Number of Factors in Approximate Factor Models
- Measure Theory and Probability Theory
- The Rotation of Eigenvectors by a Perturbation. III
- Large Covariance Estimation by Thresholding Principal Orthogonal Complements
- Prediction by Supervised Principal Components
- On Sliced Inverse Regression With High-Dimensional Covariates
- Projected principal component analysis in factor models