Yiqi Lin, Frank Windmeijer, Xinyuan Song, Qingliang Fan
arXiv 7 Jul 2022 · Statistics — Methodology · publishedJournal of the Royal Statistical Society Series B (Statistical Methodology) (2024) · 9 citations (OpenAlex)
arXiv:2207.03035 · PDF · DOI · OpenAlex · Extracted main text
We discuss the fundamental issue of identification in linear instrumental variable (IV) models with unknown IV validity. With the assumption of the "sparsest rule", which is equivalent to the plurality rule but becomes operational in computation algorithms, we investigate and prove the advantages of non-convex penalized approaches over other IV estimators based on two-step selections, in terms of selection consistency and accommodation for individually weak IVs. Furthermore, we propose a surrogate sparsest penalty that aligns with the identification condition and provides oracle sparse structure simultaneously. Desirable theoretical properties are derived for the proposed estimator with weaker IV strength conditions compared to the previous literature. Finite sample properties are demonstrated using simulations and the selection and estimation method is applied to an empirical study concerning the effect of BMI on diastolic blood pressure.
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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 | Windmeijer, F., Liang, X., Hartwig, F. P. and Bowden, J. (2021) The… J. R. Statist. Soc. B, 83, 752–776 self | 1.000 | 17 | 5 | 100% |
| 2 | Kang, H., Zhang, A., Cai, T. T. and Small, D. S. (2016) Instrumental… J. Am. Statist. Ass., 111, 132–144 | 1.000 | 16 | 3 | 100% |
| 3 | Guo, Z., Kang, H., Tony Cai, T. and Small, D. S. (2018) Confidence i… J. R. Statist. Soc. B, 80, 793–815 | 1.000 | 15 | 3 | 100% |
| 4 | Chao, J. C. and Swanson, N. R. (2005) Consistent estimation with a l… Econometrica, 73, 1673–1692 | 1.000 | 6 | 3 | 100% |
| 5 | Hansen, C., Hausman, J. and Newey, W. (2008) Estimation with many in… J. Bus. Econ. Statist., 26, 398–422 | 1.000 | 5 | 3 | 100% |
| 6 | Kolesár, M. (2018) Minimum distance approach to inference with many… J. Econometrics, 204, 86–100 | 0.956 | 8 | 4 | 88% |
| 7 | Windmeijer, F., Farbmacher, H., Davies, N. and Davey Smith, G. (2019… (2019) J. Am. Statist. Ass., 114, 1339–1350 self | 0.941 | 18 | 6 | 83% |
| 8 | Bekker, P. A. (1994) Alternative approximations to the distributions… Econometrica, 657–681 | 0.928 | 5 | 3 | 80% |
| 9 | Kolesár, M., Chetty, R., Friedman, J., Glaeser, E. and Imbens, G. W.… (2015) J. Bus. Econ. Statist., 33, 474–484 | 0.874 | 6 | 4 | 67% |
| 10 | Staiger, D. and Stock, J. H. (1997) Instrumental variables regressio… Econometrica, 557–586 | 0.874 | 6 | 2 | 100% |
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