Identification and estimation using heteroscedasticity without instruments: the binary endogenous regressor case

DOI10.1016/j.econlet.2018.01.003zbMath1401.62110OpenAlexW2621358206MaRDI QIDQ1787418

Arthur Lewbel

Publication date: 5 October 2018

Published in: Economics Letters (Search for Journal in Brave)

Full work available at URL: http://fmwww.bc.edu/EC-P/wp927.pdf

zbMATH Keywords

identificationendogeneityheteroscedasticitybinary regressorslinear regressionssimultaneous systems

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Linear regression; mixed models (62J05)

Related Items (1)

The GENIUS approach to robust Mendelian randomization inference

Uses Software

• IVREG2H

Cites Work

• Large Sample Properties of Generalized Method of Moments Estimators
• A note on the closed-form identification of regression models with a mismeasured binary regressor
• Endogenous regressor binary choice models without instruments, with an application to migration
• Nonparametric identification of regression models containing a misclassified dichotomous regressor without instruments
• Estimating a class of triangular simultaneous equations models without exclusion restrictions
• The Estimation of Economic Relationships using Instrumental Variables
• Constructing Instruments for Regressions With Measurement Error When no Additional Data are Available, with An Application to Patents and R&D
• Identification and estimation of semiparametric two-step models
• TWO-STEP GMM ESTIMATION OF THE ERRORS-IN-VARIABLES MODEL USING HIGH-ORDER MOMENTS

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