Hybrid generalized empirical likelihood estimators: instrument selection with adaptive lasso

DOI10.1016/j.jeconom.2015.01.007zbMath1337.62167OpenAlexW2155279231WikidataQ57437086 ScholiaQ57437086MaRDI QIDQ494397

Qingliang Fan, Mehmet Caner

Publication date: 1 September 2015

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

Full work available at URL: https://doi.org/10.1016/j.jeconom.2015.01.007

zbMATH Keywords

model selectionshrinkage estimatorsnear minimax risk bound

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Applications of statistics to economics (62P20) Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic distribution theory in statistics (62E20)

Related Items (11)

Adaptive k-class estimation in high-dimensional linear models ⋮ Dummy endogenous treatment effect estimation using high‐dimensional instrumental variables ⋮ Inference for high-dimensional instrumental variables regression ⋮ UNIFORM ASYMPTOTICS AND CONFIDENCE REGIONS BASED ON THE ADAPTIVE LASSO WITH PARTIALLY CONSISTENT TUNING ⋮ Culling the Herd of Moments with Penalized Empirical Likelihood ⋮ Efficient estimation for longitudinal data by combining large-dimensional moment conditions ⋮ Endogeneity in high dimensions ⋮ Variable selection for structural equation with endogeneity ⋮ Inference in partially identified models with many moment inequalities using Lasso ⋮ Generalized high-dimensional trace regression via nuclear norm regularization ⋮ Estimation of Sparse Structural Parameters with Many Endogenous Variables

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