Instrumental variables estimation with many weak instruments using regularized JIVE

DOI10.1016/j.jeconom.2014.04.022zbMath1311.62097OpenAlexW2138545569MaRDI QIDQ2511799

Yanyan Li

Publication date: 6 August 2014

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

Full work available at URL: https://doi.org/10.1016/j.jeconom.2014.04.022

zbMATH Keywords

endogeneityridge regressionhigh-dimensional models

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (17)

Econometric estimation with high-dimensional moment equalities ⋮ JIVE FOR PANEL DYNAMIC SIMULTANEOUS EQUATIONS MODELS ⋮ Many IVs estimation of dynamic panel regression models with measurement error ⋮ On a generalization of the test of endogeneity in a two stage least squares estimation ⋮ Adaptive k-class estimation in high-dimensional linear models ⋮ The pitfall of instrumental variables in big data: What the rule of thumb can't give you ⋮ Breaking the winner's curse in Mendelian randomization: rerandomized inverse variance weighted estimator ⋮ Dummy endogenous treatment effect estimation using high‐dimensional instrumental variables ⋮ Inference for high-dimensional instrumental variables regression ⋮ A conditional linear combination test with many weak instruments ⋮ Treatment effects in interactive fixed effects models with a small number of time periods ⋮ Culling the Herd of Moments with Penalized Empirical Likelihood ⋮ Ill-posed estimation in high-dimensional models with instrumental variables ⋮ Regularized LIML for many instruments ⋮ Robust estimation with many instruments ⋮ Generalized high-dimensional trace regression via nuclear norm regularization ⋮ An augmented Anderson–Hsiao estimator for dynamic short-T panels^†

Cites Work

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• A regularization approach to the many instruments problem
• Instrumental variable estimation in the presence of many moment conditions
• Asymptotic efficiency in estimation with conditional moment restrictions
• The nonlinear two-stage least-squares estimator
• Simultaneous analysis of Lasso and Dantzig selector
• Factor-GMM estimation with large sets of possibly weak instruments
• Boosting for high-dimensional linear models
• Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain
• INSTRUMENTAL VARIABLE ESTIMATION IN A DATA RICH ENVIRONMENT
• ASYMPTOTIC DISTRIBUTION OF JIVE IN A HETEROSKEDASTIC IV REGRESSION WITH MANY INSTRUMENTS
• Simultaneous Equations Estimation Based on Principal Components of Predetermined Variables
• Efficient Instrumental Variables Estimation of Nonlinear Models
• LASSO-TYPE GMM ESTIMATOR
• The Bias of Instrumental Variable Estimators of Simultaneous Equation Systems
• Some Properties of a Modification of the Limited Information Estimator
• Alternative Approximations to the Distributions of Instrumental Variable Estimators
• Instrumental Variables Regression with Weak Instruments
• Choosing the Number of Instruments
• Selecting Instrumental Variables in a Data Rich Environment
• Consistent Estimation with a Large Number of Weak Instruments
• Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators
• Random Effects Estimators with many Instrumental Variables
• On the Use of Principal Components of Independent Variables in Two-Stage Least-Squares Estimation

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