Predictive quantile regression with mixed roots and increasing dimensions: the ALQR approach

DOI10.1016/j.jeconom.2022.11.006arXiv2101.11568MaRDI QIDQ6090583

Youngki Shin, Rui Fan, Ji Hyung Lee

Publication date: 17 November 2023

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2101.11568

zbMATH Keywords

quantile regressioncointegrationforecastingoracle propertyadaptive LASSO

Mathematics Subject Classification ID

Statistics (62-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Regularized estimation in sparse high-dimensional time series models
• The Adaptive Lasso and Its Oracle Properties
• Robust econometric inference with mixed integrated and mildly explosive regressors
• Quantile cointegrating regression
• Oracle inequalities for high dimensional vector autoregressions
• Lasso-driven inference in time and space
• High-dimensional variable selection
• Globally adaptive quantile regression with ultra-high dimensional data
• \(\ell_1\)-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors
• Asymptotic behavior of M-estimators of p regression parameters when \(p^ 2/n\) is large. I. Consistency
• Asymptotic behavior of M estimators of p regression parameters when \(p^ 2/n\) is large. II: Normal approximation
• Determination of vector error correction models in high dimensions
• Asymptotics for Lasso-type estimators.
• Adaptive penalized quantile regression for high dimensional data
• Lasso guarantees for \(\beta \)-mixing heavy-tailed time series
• High-dimensional predictive regression in the presence of cointegration
• Predictive quantile regressions under persistence and conditional heteroskedasticity
• Jackknife model averaging for quantile regressions
• \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
• Adaptive robust variable selection
• Predictive regression under various degrees of persistence and robust long-horizon regression
• CONSISTENT AND CONSERVATIVE MODEL SELECTION WITH THE ADAPTIVE LASSO IN STATIONARY AND NONSTATIONARY AUTOREGRESSIONS
• Bernstein inequality and moderate deviations under strong mixing conditions
• Shrinkage Tuning Parameter Selection with a Diverging number of Parameters
• Optimal Inference in Cointegrated Systems
• Unified LASSO Estimation by Least Squares Approximation
• Trimmed Least Squares Estimation in the Linear Model
• Conditions for linear processes to be strong-mixing
• Regression Quantiles
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Oracle Estimation of a Change Point in High-Dimensional Quantile Regression
• Alternative Asymptotics for Cointegration Tests in Large VARs
• Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
• Identifying Cointegration by Eigenanalysis
• Regularization Parameter Selections via Generalized Information Criterion
• AUTOMATED ESTIMATION OF VECTOR ERROR CORRECTION MODELS
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• Tuning parameter selectors for the smoothly clipped absolute deviation method
• Tuning Parameter Selection in High Dimensional Penalized Likelihood
• Partially linear additive quantile regression in ultra-high dimension
• Predictive quantile regression with persistent covariates: IVX-QR approach
• Performance bounds for parameter estimates of high-dimensional linear models with correlated errors

This page was built for publication: Predictive quantile regression with mixed roots and increasing dimensions: the ALQR approach