Model averaging based on leave-subject-out cross-validation for vector autoregressions

DOI10.1016/j.jeconom.2018.10.007zbMath1452.62657OpenAlexW2904550086WikidataQ128724999 ScholiaQ128724999MaRDI QIDQ1740272

Xinyu Zhang, Jun Liao, Xianpeng Zong, Guo Hua Zou

Publication date: 30 April 2019

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

Full work available at URL: https://doi.org/10.1016/j.jeconom.2018.10.007

zbMATH Keywords

consistencyvector autoregressionsasymptotic optimalitymodel averagingleave-subject-out cross-validation

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) Linear regression; mixed models (62J05)

Related Items (10)

Model averaging for generalized linear models in fragmentary data prediction ⋮ Mallows model averaging with effective model size in fragmentary data prediction ⋮ AN ASYMPTOTIC THEORY FOR LEAST SQUARES MODEL AVERAGING WITH NESTED MODELS ⋮ Forecasting vector autoregressions with mixed roots in the vicinity of unity ⋮ Penalized time-varying model averaging ⋮ Multifold Cross-Validation Model Averaging for Generalized Additive Partial Linear Models ⋮ Time-varying forecast combination for factor-augmented regressions with smooth structural changes ⋮ Limit of the optimal weight in least squares model averaging with non-nested models ⋮ Corrected Mallows criterion for model averaging ⋮ Optimal model averaging for multivariate regression models

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