Hiroyuki Kasahara, Katsumi Shimotsu
arXiv 25 Apr 2019 · Econometrics · publishedEconometric Theory (2021) · 2 citations (OpenAlex)
arXiv:1904.11143 · PDF · DOI · OpenAlex · Extracted main text
We study identification in nonparametric regression models with a misclassified and endogenous binary regressor when an instrument is correlated with misclassification error. We show that the regression function is nonparametrically identified if one binary instrument variable and one binary covariate satisfy the following conditions. The instrumental variable corrects endogeneity; the instrumental variable must be correlated with the unobserved true underlying binary variable, must be uncorrelated with the error term in the outcome equation, but is allowed to be correlated with the misclassification error. The covariate corrects misclassification; this variable can be one of the regressors in the outcome equation, must be correlated with the unobserved true underlying binary variable, and must be uncorrelated with the misclassification error. We also propose a mixture-based framework for modeling unobserved heterogeneous treatment effects with a misclassified and endogenous binary regressor and show that treatment effects can be identified if the true treatment effect is related to an observed regressor and another observable variable.
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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 | Mahajan, A (2006) Identification and Estimation of Regression Models with Misclassification | 1.000 | 12 | 3 | 100% |
| 2 | DiTraglia, F. J. and Garcá-Jimeno, C (2019) Identifyng the Effect of a Mis-Classified, Binary, Endogenous Regressor | 0.874 | 13 | 2 | 100% |
| 3 | Heckman, J. J., Urzua, S., and Vytlacil, E (2006) Understanding Instrumental Variables in Models with Essential Heterogeneity | 0.693 | 5 | 1 | 100% |
| 4 | Nguimkeu, P., Denteh, A., and Tchernis, R (2019) On the Estimation of Treatment Effects with Endogenous Misreporting | 0.693 | 5 | 1 | 100% |
| 5 | Battistin, E., De Nadai, M., and Sianesi, B (2014) Misreported Schooling, Multiple Measures and Returns to Educational Qualifications | 0.644 | 4 | 1 | 100% |
| 6 | Black, D. A., Berger, M. C., and Scott, F. A (2000) Bounding Parameter Estimates with Nonclassical Measurement Error | 0.644 | 4 | 1 | 100% |
| 7 | Kane, T. J., Rouse, C. E., and Staiger, D (1999) Estimating Returns to Schooling when Schooling is Misreported, Technical report, National Bureau of Economic Research | 0.644 | 4 | 1 | 100% |
| 8 | Hu, Y (2008) Identification and Estimation of Nonlinear Models with Misclassification Error Using Instrumental Variables: A General Solution | 0.644 | 2 | 2 | 100% |
| 9 | Deb, P. and Gregory, C. A (2018) Heterogeneous impacts of the Supplemental Nutrition Assistance Program on food insecurity | 0.585 | 3 | 1 | 100% |
| 10 | Bierens, H (1987) Kernel Estimators of Regression Functions, in | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 42 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Local Average and Marginal Treatment Effects with a Misclassified Treatment | 0.405 | 1 | 1 |
| 2 | Identification of Latent Group Effects under Conditional Calibration | 0.405 | 1 | 1 |