arXiv 24 Mar 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2303.13795 · PDF · DOI · OpenAlex · Extracted main text
This paper characterizes point identification results of the local average treatment effect (LATE) using two imperfect instruments. The classical approach (Imbens and Angrist (1994)) establishes the identification of LATE via an instrument that satisfies exclusion, monotonicity, and independence. However, it may be challenging to find a single instrument that satisfies all these assumptions simultaneously. My paper uses two instruments but imposes weaker assumptions on both instruments. The first instrument is allowed to violate the exclusion restriction and the second instrument does not need to satisfy monotonicity. Therefore, the first instrument can affect the outcome via both direct effects and a shift in the treatment status. The direct effects can be identified via exogenous variation in the second instrument and therefore the local average treatment effect is identified. An estimator is proposed, and using Monte Carlo simulations, it is shown to perform more robustly than the instrumental variable estimand.
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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 | G. W. Imbens and J. D. Angrist (1994) Identification and estimation of local average treatment effects | 1.000 | 10 | 5 | 100% |
| 2 | J. D. Angrist, G. W. Imbens, and D. B. Rubin (1996) Identification of causal effects using instrumental variables | 0.874 | 6 | 2 | 100% |
| 3 | M. Kolesár, R. Chetty, J. Friedman, E. Glaeser, and G. W. Imbens (2015) Identification and inference with many invalid instruments | 0.737 | 3 | 2 | 100% |
| 4 | R. Chetty, J. N. Friedman, N. Hilger, E. Saez, D. W. Schanzenbach, a… (2011) How does your kindergarten classroom affect your earnings? evidence from project star | 0.511 | 2 | 1 | 100% |
| 5 | K. Hirano, G. W. Imbens, D. B. Rubin, and X.-H. Zhou (2000) Assessing the effect of an influenza vaccine in an encouragement design | 0.511 | 2 | 1 | 100% |
| 6 | M. Huber and G. Mellace (2015) Testing instrument validity for late identification based on inequality moment constraints | 0.511 | 2 | 1 | 100% |
| 7 | I. Mourifié and Y. Wan (2017) Testing local average treatment effect assumptions | 0.511 | 2 | 1 | 100% |
| 8 | J. D. Angrist and W. N. Evans (1998) Children and their parents' labor supply: Evidence from exogenous variation in family size | 0.405 | 1 | 1 | 100% |
| 9 | D. Card (1993) Using geographic variation in college proximity to estimate the return to schooling | 0.405 | 1 | 1 | 100% |
| 10 | D. Card (2001) Estimating the return to schooling: Progress on some persistent econometric problems | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 28 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | IV Regressions without Exclusion Restrictions | 0.405 | 1 | 1 |
| 2 | 1420 Identification with possibly invalid IVs | 0.405 | 1 | 1 |
| 3 | 1820 Don't (fully) exclude me, it's not necessary! Causal inference with semi-IVs | 0.000 | 1 | 1 |