arXiv 4 Nov 2022 · Econometrics · 1 citations (OpenAlex)
arXiv:2211.02249 · PDF · DOI · OpenAlex · Extracted main text
We propose and implement an approach to inference in linear instrumental variables models which is simultaneously robust and computationally tractable. Inference is based on self-normalization of sample moment conditions, and allows for (but does not require) many (relative to the sample size), weak, potentially invalid or potentially endogenous instruments, as well as for many regressors and conditional heteroskedasticity. Our coverage results are uniform and can deliver a small sample guarantee. We develop a new computational approach based on semidefinite programming, which we show can equally be applied to rapidly invert existing tests (e.g,. AR, LM, CLR, etc.).
appendix boundary found by appendix_titled_section at “Appendix” · 91% of the source is main text. Read the extracted text to check this.
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 | Gautier, E. and C. Rose, High-dimensional instrumental variables reg… (2021) self | 0.874 | 7 | 2 | 100% |
| 2 | Guggenberger, P., F. Kleibergen, S. Mavroeidis and L. Chen, On the a… (2012) 2649–2666 | 0.811 | 4 | 2 | 100% |
| 3 | Guggenberger, P., F. Kleibergen and S. Mavroeidis, A more powerful s… (2019) 487–526 | 0.811 | 4 | 2 | 100% |
| 4 | Guggenberger, P., F. Kleibergen and S. Mavroeidis, A more powerful s… (2021) | 0.811 | 4 | 2 | 100% |
| 5 | Kang, H., A. Zhang, T. T. Cai and D. S. Small, Instrumental variable… (2016) 132–144 | 0.811 | 4 | 2 | 100% |
| 6 | Mikusheva, A., Robust confidence sets in the presence of weak instru… (2010) 236–247 | 0.811 | 4 | 2 | 100% |
| 7 | Andrews, I., Conditional linear combination tests for weakly identif… (2016) 2155–2182 | 0.737 | 3 | 2 | 100% |
| 8 | Andrews, I., J. H. Stock and L. Sun, Weak instruments in instrumenta… (2019) 727–753 | 0.737 | 3 | 2 | 100% |
| 9 | Belloni, A., D. Chen, V. Chernozhukov and C. Hansen, Sparse models a… (2012) 2369–2429 | 0.737 | 3 | 2 | 100% |
| 10 | Kleibergen, F., Pivotal statistics for testing structural parameters… (2002) 1781–1803 | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 57 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates | 0.405 | 1 | 1 |