Nonlinear Factor‐Augmented Predictive Regression Models with Functional Coefficients
DOI10.1111/jtsa.12511zbMath1447.62102OpenAlexW2990965122WikidataQ126787337 ScholiaQ126787337MaRDI QIDQ5111851
Wenyang Zhang, Jiraroj Tosasukul, Degui Li
Publication date: 27 May 2020
Published in: Journal of Time Series Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1111/jtsa.12511
factor modelsPCAlocal linear smoothingbootstrap procedurefunctional-coefficient modelsnonlinear forecastvector auto-regression.
Inference from stochastic processes and prediction (62M20) Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Functional data analysis (62R10) Applications of statistics to actuarial sciences and financial mathematics (62P05) General nonlinear regression (62J02)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Trending time-varying coefficient time series models with serially correlated errors
- Forecasting with factor-augmented regression: a frequentist model averaging approach
- Determining the number of factors when the number of factors can increase with sample size
- Statistical methods with varying coefficient models
- Variable selection, estimation and inference for multi-period forecasting problems
- Model selection and structure specification in ultra-high dimensional generalised semi-varying coefficient models
- Simultaneous confidence band and hypothesis test in generalised varying-coefficient models
- Covariance regularization by thresholding
- Estimating the dimension of a model
- Nonparametric statistics for stochastic processes. Estimation and prediction.
- Statistical estimation in varying coefficient models
- New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models
- On the subspaces of \(L^p\) \((p > 2)\) spanned by sequences of independent random variables
- Eigenvalue Ratio Test for the Number of Factors
- Forward Regression for Ultra-High Dimensional Variable Screening
- LOCAL LINEAR FITTING UNDER NEAR EPOCH DEPENDENCE: UNIFORM CONSISTENCY WITH CONVERGENCE RATES
- Extended Bayesian information criteria for model selection with large model spaces
- Multivariate functional-coefficient regression models for nonlinear vector time series data
- Confidence Intervals for Diffusion Index Forecasts and Inference for Factor-Augmented Regressions
- Efficient estimation for semivarying-coefficient models
- UNIFORM CONVERGENCE RATES FOR KERNEL ESTIMATION WITH DEPENDENT DATA
- Testing Hypotheses About the Number of Factors in Large Factor Models
- Forecasting Using Principal Components From a Large Number of Predictors
- Functional-Coefficient Regression Models for Nonlinear Time Series
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Functional-Coefficient Autoregressive Models
- On a Principal Varying Coefficient Model
- Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series
- Feature Selection for Varying Coefficient Models With Ultrahigh-Dimensional Covariates
- Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models
- Shrinkage Estimation of the Varying Coefficient Model
- Determining the Number of Factors in Approximate Factor Models
- Large Covariance Estimation by Thresholding Principal Orthogonal Complements
- Varying Coefficient Regression Models: A Review and New Developments
