Estimating treatment effects when program participation is misreported
| Authors |
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|---|---|
| Publication date | 12-2024 |
| Journal | The STATA Journal |
| Volume | Issue number | 24 | 4 |
| Pages (from-to) | 614-629 |
| Organisations |
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| Abstract |
Instrumental variables are commonly used to estimate treatment effects
in cases of imperfect compliance. However, if participation in the
program is misreported, standard techniques can yield severely biased
results. We present a new command, ivreg2m,
that implements the mismeasured robust local average treatment-effect
estimator developed by Calvi, Lewbel, and Tommasi (2022, Journal of Business and Economic Statistics 40: 1701–1717) and Tommasi and Zhang (2024b, Journal of Applied Econometrics, https://doi.org/10.1002/jae.3079), to estimate the heterogeneous treatment effect of a program in the presence of treatment noncompliance and misreporting. The ivreg2m command can be used as the preferred strategy in cases of exogenous (nondifferential) misclassification.
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| Document type | Article |
| Language | English |
| Published at | https://doi.org/10.1177/1536867X241297916 |
| Downloads |
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