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 (Forthcoming, 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.
Baum, C.F., Tommasi, D., Zhang, L. (2024). Estimating treatment effects when program participation is misreported. THE STATA JOURNAL, 24(4), 614-629 [10.1177/1536867X241297916].
Estimating treatment effects when program participation is misreported
Denni Tommasi
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2024
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 (Forthcoming, 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.| File | Dimensione | Formato | |
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ivreg2m_merged.pdf
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