Ludgero Glorias, Federico Martellosio, J. M. C. Santos Silva
arXiv 27 May 2025 · Econometrics
arXiv:2505.21213 · PDF · DOI · OpenAlex · Extracted main text
We consider two nonparametric approaches to ensure that linear instrumental variables estimators satisfy the rich-covariates condition emphasized by Blandhol et al. (2025), even when the instrument is not unconditionally randomly assigned and the model is not saturated. Both approaches start with a nonparametric estimate of the expectation of the instrument conditional on the covariates, and ensure that the rich-covariates condition is satisfied either by using as the instrument the difference between the original instrument and its estimated conditional expectation, or by adding the estimated conditional expectation to the set of regressors. We derive asymptotic properties when the first step uses kernel regression, and assess finite-sample performance in simulations where we also use neural networks in the first step. Finally, we present an empirical illustration that highlights some significant advantages of the proposed methods.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Blandhol, C., J. Bonney, M. Mogstad, and A. Torgovitsky (2025) When is TSLS Actually LATE? | 1.000 | 27 | 7 | 100% |
| 2 | Lee, M.-J (2021) Instrument Residual Estimator for any Response Variable with Endogenous Binary Treatment | 1.000 | 15 | 5 | 100% |
| 3 | Kim, B. and M.-J. Lee (2024) Instrument-Residual Estimator for Multi-valued Instruments Under Full Monotonicity | 1.000 | 13 | 4 | 100% |
| 4 | Borusyak, K. and P. Hull (2023) Nonrandom Exposure to Exogenous Shocks | 1.000 | 7 | 3 | 100% |
| 5 | Kolesár, M (2013) Estimation in an Instrumental Variables Model With Treatment Effect Heterogeneity | 1.000 | 7 | 3 | 100% |
| 6 | Mogstad, M. and A. Torgovitsky (2024) Instrumental Variables with Unobserved Heterogeneity in Treatment Effects, in | 1.000 | 6 | 4 | 100% |
| 7 | Lee, M.-J. and C. Han (2024) Ordinary Least Squares and Instrumental-Variables Estimators for any Outcome and Heterogeneity | 1.000 | 6 | 3 | 100% |
| 8 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/Debiased Machine Learning for Treatment and Structural Parameters | 0.985 | 22 | 5 | 95% |
| 9 | Soczyński, T (2024) When Should We (Not) Interpret Linear IV Estimands as LATE? | 0.928 | 4 | 3 | 100% |
| 10 | Abadie, A (2003) Semiparametric Instrumental Variable Estimation of Treatment Response Models | 0.874 | 7 | 2 | 100% |
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