F-test and z-test for high-dimensional regression models with a factor structure

DOI10.1080/00949655.2022.2062357OpenAlexW4223467213MaRDI QIDQ5040532

Mingjing Chen

Publication date: 17 October 2022

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2022.2062357

zbMATH Keywords

factormultiple testinghigh-dimensionalcommon correlated effect

Mathematics Subject Classification ID

Parametric hypothesis testing (62F03) Statistics (62-XX)

Uses Software

• FAMT

Cites Work

• Factor-Adjusted Regularized Model Selection
• Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control
• Testing a single regression coefficient in high dimensional linear models
• On the role of the rank condition in CCE estimation of factor-augmented panel regressions
• Improved penalization for determining the number of factors in approximate factor models
• Testing covariates in high-dimensional regression
• Testing covariates in high dimension linear regression with latent factors
• Robust determination for the number of common factors in the approximate factor models
• Unbiased CCE estimator for interactive fixed effects panels
• Tests for the explanatory power of latent factors
• Cross-sectional averages versus principal components
• Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors
• Eigenvalue Ratio Test for the Number of Factors
• A Factor Model Approach to Multiple Testing Under Dependence
• Factor profiled sure independence screening
• Testing Against a High Dimensional Alternative
• Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• High Dimensional Variable Selection via Tilting
• A self-reliant projected information criterion for the number of factors
• Tests for High-Dimensional Regression Coefficients With Factorial Designs
• Determining the Number of Factors in Approximate Factor Models
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
• Time varying factor models with possibly strongly correlated noises

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