Dennis Lim, Wenjie Wang, Yichong Zhang
arXiv 2 Dec 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2412.01603 · PDF · DOI · OpenAlex · Extracted main text
Weak-identification-robust tests for instrumental variable (IV) regressions are typically developed separately depending on whether the number of IVs is treated as fixed or increasing with the sample size, forcing researchers to make a stance on the asymptotic behavior, which is often ambiguous in practice. This paper proposes a bootstrap-based, dimension-agnostic Anderson-Rubin (AR) test that achieves correct asymptotic size regardless of whether the number of IVs is fixed or diverging, and even accommodates cases where the number of IVs exceeds the sample size. By incorporating ridge regularization, our approach reduces the effective rank of the projection matrix and yields regimes where the limiting distribution of the AR statistic can be a weighted chi-squared, a normal, or a mixture of the two. Strong approximation results ensure that the bootstrap procedure remains uniformly valid across all regimes, while also delivering substantial power gains over existing methods by exploiting rank reduction.
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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 | Dov\`, M.-S., A. B. Kock, and S. Mavroeidis (2024) A ridge-regularized jackknifed anderson-rubin test | 1.000 | 10 | 4 | 100% |
| 2 | Card, D. (2009, May) (2009) Immigration and inequality | 1.000 | 6 | 3 | 100% |
| 3 | Mikusheva, A. and L. Sun (2022) Inference with many weak instruments | 0.976 | 14 | 7 | 93% |
| 4 | Navjeevan, M (2023) An identification and dimensionality robust test for instrumental variables models | 0.961 | 9 | 4 | 89% |
| 5 | Crudu, F., G. Mellace, and Z. Sándor (2021) Inference in instrumental variable models with heteroskedasticity and many instruments | 0.956 | 8 | 5 | 88% |
| 6 | Anatolyev, S. and M. Slvsten (2023) Testing many restrictions under heteroskedasticity | 0.956 | 8 | 4 | 88% |
| 7 | Belloni, A., D. Chen, V. Chernozhukov, and C. Hansen (2012) Sparse models and methods for optimal instruments with an application to eminent domain | 0.941 | 6 | 3 | 83% |
| 8 | Kline, P., R. Saggio, and M. Slvsten (2020) Leave-out estimation of variance components | 0.928 | 4 | 3 | 100% |
| 9 | Carrasco, M. and G. Tchuente (2015) Regularized liml for many instruments | 0.928 | 4 | 3 | 100% |
| 10 | Carrasco, M. and G. Tchuente (2016) Efficient estimation with many weak instruments using regularization techniques | 0.928 | 4 | 3 | 100% |
Showing the top 10 of 100 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Robust Inference with High-Dimensional Instruments | 0.644 | 2 | 2 |
| 2 | An Empirical Comparison of Weak-IV-Robust Procedures in Just-Identified Models | 0.405 | 1 | 1 |
| 3 | Wild Bootstrap Inference for Linear Regressions with Many Covariates | 0.405 | 1 | 1 |
| 4 | An Improved Inference for IV Regressions | 0.405 | 1 | 1 |
| 5 | Cluster-Robust Inference for Quadratic Forms | 0.405 | 1 | 1 |