Instrument assisted regression for errors in variables models with binary response

Kun Xu, Yanyuan Ma, Liqun Wang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)104-117
Number of pages14
JournalScandinavian Journal of Statistics
Volume42
Issue number1
DOIs
StatePublished - Mar 1 2015

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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