1 option
Correcting for Misclassified Binary Regressors Using Instrumental Variables / Steven J. Haider, Melvin Stephens Jr..
- Format:
- Book
- Author/Creator:
- Haider, Steven J.
- Series:
- Working Paper Series (National Bureau of Economic Research) no. w27797.
- NBER working paper series no. w27797
- Language:
- English
- Physical Description:
- 1 online resource: illustrations (black and white);
- Place of Publication:
- Cambridge, Mass. National Bureau of Economic Research 2020.
- Summary:
- 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.
- Notes:
- Print version record
- September 2020.
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.