arXiv 14 Jun 2023 · Econometrics · 1 citations (OpenAlex)
arXiv:2306.08559 · PDF · DOI · OpenAlex · Extracted main text
Data clustering reduces the effective sample size from the number of observations towards the number of clusters. For instrumental variable models this reduced effective sample size makes the instruments more likely to be weak, in the sense that they contain little information about the endogenous regressor, and many, in the sense that their number is large compared to the sample size. Consequently, weak and many instrument problems for estimators and tests in instrumental variable models are also more likely. None of the previously developed many and weak instrument robust tests, however, can be applied to clustered data as they all require independent observations. Therefore, I adapt the many and weak instrument robust jackknife Anderson--Rubin and jackknife score tests to clustered data by removing clusters rather than individual observations from the statistics. Simulations and a revisitation of a study on the effect of queenly reign on war show the empirical relevance of the new tests.
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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 | Mikusheva, A. and L. Sun (2022) Inference with many weak instruments | 1.000 | 13 | 4 | 100% |
| 2 | Bekker, P. A. and F. Crudu (2015) Jackknife instrumental variable estimation with heteroskedasticity | 1.000 | 8 | 3 | 100% |
| 3 | Matsushita, Y. and T. Otsu (2022) Jackknife Lagrange multiplier test with many weak instruments | 1.000 | 6 | 3 | 100% |
| 4 | Crudu, F., G. Mellace, and Z. Sándor (2021) Inference in instrumental variable models with heteroskedasticity and many instruments | 0.971 | 12 | 4 | 92% |
| 5 | Dube, O. and S. P. Harish (2020) Queens | 0.928 | 15 | 4 | 80% |
| 6 | Boot, T. and J. W. Ligtenberg (2025) Identification-and many moment-robust inference via invariant moment conditions | 0.843 | 4 | 3 | 75% |
| 7 | Lim, D., W. Wang, and Y. Zhang (2024) A conditional linear combination test with many weak instruments | 0.843 | 10 | 3 | 60% |
| 8 | Kleibergen, F (2002) Pivotal statistics for testing structural parameters in instrumental variables regression | 0.843 | 3 | 3 | 100% |
| 9 | Chao, J. C., N. R. Swanson, and T. Woutersen (2023) Jackknife estimation of a cluster-sample IV regression model with many weak instruments | 0.737 | 3 | 2 | 100% |
| 10 | Andrews, I (2016) Conditional linear combination tests for weakly identified models | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 36 scored citations.