Inference for high-dimensional instrumental variables regression
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Publication:2190211
DOI10.1016/j.jeconom.2019.09.009zbMath1456.62149arXiv1708.05499OpenAlexW2998216423WikidataQ126466948 ScholiaQ126466948MaRDI QIDQ2190211
Publication date: 18 June 2020
Published in: Journal of Econometrics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1708.05499
Asymptotic properties of parametric estimators (62F12) Applications of statistics to economics (62P20) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)
Related Items (7)
Mathematical foundations of machine learning. Abstracts from the workshop held March 21--27, 2021 (hybrid meeting) ⋮ Doubly debiased Lasso: high-dimensional inference under hidden confounding ⋮ Estimating causal effects with hidden confounding using instrumental variables and environments ⋮ -Penalized Pairwise Difference Estimation for a High-Dimensional Censored Regression Model ⋮ Culling the Herd of Moments with Penalized Empirical Likelihood ⋮ Unnamed Item ⋮ High-dimensional linear models with many endogenous variables
Uses Software
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