High-dimensional predictive regression in the presence of cointegration

DOI10.1016/J.JECONOM.2020.03.011zbMath1464.62515OpenAlexW2547402097MaRDI QIDQ2224889

Yanyan Li

Publication date: 4 February 2021

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

Full work available at URL: https://doi.org/10.1016/j.jeconom.2020.03.011

zbMATH Keywords

cointegrationLassoreturn predictabilityhigh-dimensional predictive regression

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (7)

On LASSO for predictive regression ⋮ Forecasting vector autoregressions with mixed roots in the vicinity of unity ⋮ Penetrating sporadic return predictability ⋮ Predictive quantile regression with mixed roots and increasing dimensions: the ALQR approach ⋮ Forward-selected panel data approach for program evaluation ⋮ Semi-parametric single-index predictive regression models with cointegrated regressors ⋮ Editorial. Special issue of the Journal of Econometrics on ``Econometric estimation and testing: essays in honour of Maxwell King

Uses Software

• Glmnet

Cites Work

• Unnamed Item
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• Regularization and Variable Selection Via the Elastic Net
• Common risk factors in the returns on stocks and bonds
• Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models

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