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3 août 2021
- handle: 10670/1.rv19pz
- hal: hal-00591732
- ARXIV: 1105.2454
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info:eu-repo/semantics/altIdentifier/arxiv/1105.2454
Ce document est lié à :
info:eu-repo/grantAgreement/EC/FP7/337665/EU/Parsimony and operator methods for treatment of endogeneity and multiple sources of unobserved heterogeneity/POEMH
info:eu-repo/semantics/OpenAccess
Mots-clés
Bias correction Robustness to identification Partial identification Unknown variance Variable selection Instrumental variables Sparsity Endogeneity Confidence intervals 62J99, 62H12, G2P20 C - Mathematical and Quantitative Methods/C.C1 - Econometric and Statistical Methods and Methodology: General/C.C1.C13 - Estimation: General C - Mathematical and Quantitative Methods/C.C2 - Single Equation Models • Single Variables/C.C2.C21 - Cross-Sectional Models • Spatial Models • Treatment Effect Models • Quantile RegressionsSujets proches
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Eric Gautier et al., « High-dimensional instrumental variables regression and confidence sets », Archive Ouverte d'INRAE, ID : 10670/1.rv19pz
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Résumé
This article considers inference in linear instrumental variables models with many regressors, all of which could be endogenous. We propose the STIV estimator. Identification robust confidence sets are derived by solving linear programs. We present results on rates of convergence, variable selection, confidence sets which adapt to the sparsity, and analyze confidence bands for vectors of linear functions using bias correction. We also provide solutions to some instruments being endogenous. The application is to the EASI demand system.