arXiv 20 Feb 2024 · Econometrics
arXiv:2402.12607 · PDF · DOI · OpenAlex · Extracted main text
In theory, two-stage least squares (TSLS) identifies a weighted average of covariate-specific local average treatment effects (LATEs) from a saturated specification, without making parametric assumptions on how available covariates enter the model. In practice, TSLS is severely biased as saturation leads to a large number of control dummies and an equally large number of, arguably weak, instruments. This paper derives asymptotically valid tests and confidence intervals for the weighted average of LATEs that is targeted, yet missed by saturated TSLS. The proposed inference procedure is robust to unobserved treatment effect heterogeneity, covariates with rich support, and weak identification. We find LATEs statistically significantly different from zero in applications in criminology, finance, health, and education.
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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 | Blandhol, C., J. Bonney, M. Mogstad, and A. Torgovitsky (2022) When is TSLS actually LATE? | 1.000 | 10 | 4 | 100% |
| 2 | Chao, J. C., N. R. Swanson, and T. Woutersen (2023) Jackknife estimation of a cluster-sample IV regression model with many weak instruments | 1.000 | 6 | 4 | 100% |
| 3 | Evdokimov, K. S. and M. Kolesár (2018) Inference in instrumental variables analysis with heterogeneous treatment effects | 1.000 | 6 | 4 | 100% |
| 4 | Card, D (1995) Using geographic variation in college proximity to estimate the return to schooling | 1.000 | 6 | 3 | 100% |
| 5 | Lee, S (2018) A consistent variance estimator for 2SLS when instruments identify different LATEs | 1.000 | 6 | 3 | 100% |
| 6 | Mikusheva, A. and L. Sun (2022) Inference with many weak instruments | 1.000 | 5 | 3 | 100% |
| 7 | Bekker, P. A (1994) Alternative approximations to the distributions of instrumental variable estimators | 0.928 | 4 | 3 | 100% |
| 8 | Crudu, F., G. Mellace, and Z. Sándor (2021) Inference in instrumental variable models with heteroskedasticity and many instruments | 0.843 | 3 | 3 | 100% |
| 9 | Imbens, G. W. and J. D. Angrist (1994) Identification and estimation of local average treatment effects | 0.843 | 3 | 3 | 100% |
| 10 | Kolesár, M (2013) Estimation in an instrumental variables model with treatment effect heterogeneity | 0.843 | 3 | 3 | 100% |
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