Arbeitspapier
Correcting for Misclassied Binary Regressors Using Instrumental Variables
Estimators that exploit an instrumental variable to correct for misclassification in a binary regressor typically assume that the misclassification rates are invariant across all values of the instrument. We show that this assumption is invalid in routine empirical settings. We derive a new estimator that is consistent when misclassification rates vary across values of the instrumental variable. In cases where identification is weak, our moments can be combined with bounds to provide a confidence set for the parameter of interest.
- Sprache
-
Englisch
- Erschienen in
-
Series: IZA Discussion Papers ; No. 13593
- Klassifikation
-
Wirtschaft
Methodological Issues: General
Single Equation Models: Single Variables: Instrumental Variables (IV) Estimation
- Thema
-
misclassification
measurement error
instrumental variables
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Haider, Steven J.
Stephens Jr., Melvin
- Ereignis
-
Veröffentlichung
- (wer)
-
Institute of Labor Economics (IZA)
- (wo)
-
Bonn
- (wann)
-
2020
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Haider, Steven J.
- Stephens Jr., Melvin
- Institute of Labor Economics (IZA)
Entstanden
- 2020
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