arXiv 30 Sep 2021 · Econometrics · publishedEconometrics Journal (2021) · 2 citations (OpenAlex)
arXiv:2109.14785 · PDF · DOI · OpenAlex · Extracted main text
This paper extends the identification results in Nevo and Rosen (2012) to nonparametric models. We derive nonparametric bounds on the average treatment effect when an imperfect instrument is available. As in Nevo and Rosen (2012), we assume that the correlation between the imperfect instrument and the unobserved latent variables has the same sign as the correlation between the endogenous variable and the latent variables. We show that the monotone treatment selection and monotone instrumental variable restrictions, introduced by Manski and Pepper (2000, 2009), jointly imply this assumption. Moreover, we show how the monotone treatment response assumption can help tighten the bounds. The identified set can be written in the form of intersection bounds, which is more conducive to inference. We illustrate our methodology using the National Longitudinal Survey of Young Men data to estimate returns to schooling.
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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 | Nevo, A. and A. Rosen (2012) Identification with imperfect instruments | 1.000 | 13 | 6 | 100% |
| 2 | Manski, C. F. and J. Pepper (2000) Monotone instrumental variables: With an application to the returns to schooling | 1.000 | 8 | 5 | 100% |
| 3 | Manski, C. F. and J. Pepper (2009) More on monotone instrumental variables | 1.000 | 6 | 4 | 100% |
| 4 | Chernozhukov, V., S. Lee, and A. M. Rosen (2013) Intersection bounds: Estimation and inference | 0.928 | 5 | 4 | 80% |
| 5 | Chernozhukov, V., W. Kim, S. Lee, and A. M. Rosen (2015) Implementing intersection bounds in stata | 0.928 | 5 | 3 | 80% |
| 6 | Kédagni, D., L. Li, and I. Mourifié (2018) Bounding average returns to schooling using unconditional moment restrictions | 0.843 | 3 | 3 | 100% |
| 7 | Andrews, D. W. K. and X. Shi (2013) Inference based on conditional moment inequalities | 0.644 | 2 | 2 | 100% |
| 8 | Kédagni, D. and I. Mourifié (2020) Generalized instrumental inequalities: Testing the instrumental variable independence assumption | 0.644 | 2 | 2 | 100% |
| Manski | unmatched citation key Manski | 0.585 | 3 | 1 | 100% |
| 10 | Andrews, D. W. K., W. Kim, and X. Shi (2017) Stata commands for testing conditional moment inequalities/equalities | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 65 scored citations. 1 of these could not be matched to a bibliography entry, so only the citation key is shown.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Debiased Machine Learning of Aggregated Intersection Bounds and Other Causal Parameters | 0.405 | 1 | 1 |
| 2 | 1420 Identification with possibly invalid IVs | 0.405 | 1 | 1 |