Premium
Instrument Assisted Regression for Errors in Variables Models with Binary Response
Author(s) -
Xu Kun,
Ma Yanyuan,
Wang Liqun
Publication year - 2015
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12097
Subject(s) - mathematics , unobservable , estimator , instrumental variable , binary number , construct (python library) , statistics , errors in variables models , linear regression , generalized linear model , variables , binary data , econometrics , computer science , arithmetic , programming language
We study errors‐in‐variables problems when the response is binary and instrumental variables are available. We construct consistent estimators through taking advantage of the prediction relation between the unobservable variables and the instruments. The asymptotic properties of the new estimator are established and illustrated through simulation studies. We also demonstrate that the method can be readily generalized to generalized linear models and beyond. The usefulness of the method is illustrated through a real data example.