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Inference on LATEs with covariates
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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.
Forward citations
Cited by 2 Pith papers
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Wild Bootstrap Inference for Linear Regressions with Many Covariates
A scaled wild bootstrap is proven asymptotically valid for inference on a regression coefficient when the number of covariates is of the same order as the sample size and errors are heteroskedastic.
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Robust Inference with High-Dimensional Instruments
A self-normalized, random-matrix-based test for the structural parameter in IV regressions with K proportional to N and general error dependence, with the central proof deferred to the authors' prior unpublished work.
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