A One Covariate at a Time, Multiple Testing Approach to Variable Selection in High-Dimensional Linear Regression Models

DOI10.3982/ECTA14176zbMath1401.62107OpenAlexW2553503389WikidataQ129448129 ScholiaQ129448129MaRDI QIDQ4682716

Alexander Chudik, M. Hashem Pesaran, George Kapetanios

Publication date: 19 September 2018

Published in: Econometrica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3982/ecta14176

zbMATH Keywords

model selectionhigh dimensionalitymultiple testingboostingMonte Carlo experimentspenalized regressionsone covariate at a time

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Linear regression; mixed models (62J05) Hypothesis testing in multivariate analysis (62H15) Paired and multiple comparisons; multiple testing (62J15)

Related Items (8)

Detection of units with pervasive effects in large panel data models ⋮ A fast and consistent variable selection method for high-dimensional multivariate linear regression with a large number of explanatory variables ⋮ Threshold Selection in Feature Screening for Error Rate Control ⋮ Testing and signal identification for two-sample high-dimensional covariances via multi-level thresholding ⋮ Econometric analysis of production networks with dominant units ⋮ IPAD: Stable Interpretable Forecasting with Knockoffs Inference ⋮ Exponent of cross-sectional dependence for residuals ⋮ An augmented Anderson–Hsiao estimator for dynamic short-T panels^†

This page was built for publication: A One Covariate at a Time, Multiple Testing Approach to Variable Selection in High-Dimensional Linear Regression Models