arXiv 10 Apr 2018 · Econometrics · publishedEconometric Reviews (2018) · 23 citations (OpenAlex)
arXiv:1804.03349 · PDF · DOI · OpenAlex · Extracted main text
We develop point-identification for the local average treatment effect when the binary treatment contains a measurement error. The standard instrumental variable estimator is inconsistent for the parameter since the measurement error is non-classical by construction. We correct the problem by identifying the distribution of the measurement error based on the use of an exogenous variable that can even be a binary covariate. The moment conditions derived from the identification lead to generalized method of moments estimation with asymptotically valid inferences. Monte Carlo simulations and an empirical illustration demonstrate the usefulness of the proposed procedure.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | A. Mahajan (2006) Identification and estimation of regression models with misclassification | 1.000 | 12 | 4 | 100% |
| 2 | A. Lewbel (2007) Estimation of average treatment effects with misclassification | 0.969 | 11 | 6 | 91% |
| 3 | E. Battistin, M. De Nadai, and B. Sianesi (2014) Misreported schooling, multiple measures and returns to educational qualifications | 0.928 | 4 | 3 | 100% |
| 4 | T. Ura (2018) Heterogeneous treatment effects with mismeasured endogenous treatment | 0.928 | 4 | 3 | 100% |
| 5 | D. Card (1993) Using geographic variation in college proximity to estimate the return to schooling | 0.874 | 5 | 2 | 100% |
| 6 | F. DiTraglia and C. Garcia-Jimeno (2017) Mis-classified, binary, endogenous regressors: Identification and inference | 0.874 | 5 | 2 | 100% |
| 7 | C. Caetano and J. C. Escanciano (2018) Identifying multiple marginal effects with a single instrument | 0.811 | 4 | 2 | 100% |
| 8 | R. Calvi, A. Lewbel, and D. Tommasi (2017) Women's empowerment and family health: Estimating late with mismeasured treatment | 0.737 | 3 | 2 | 100% |
| 9 | G. W. Imbens and J. D. Angrist (1994) Identification and estimation of local average treatment effects | 0.737 | 3 | 2 | 100% |
| 10 | D. J. Aigner (1973) Regression with a binary independent variable subject to errors of observation | 0.644 | 2 | 2 | 100% |
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