arXiv 18 Aug 2023 · Econometrics · publishedEconometrics Journal (2024) · 8 citations (OpenAlex)
arXiv:2308.09535 · PDF · DOI · OpenAlex · Extracted main text
Linear instrumental variable regressions are widely used to estimate causal effects. Many instruments arise from the use of “technical” instruments and more recently from the empirical strategy of “judge design”. This paper surveys and summarizes ideas from recent literature on estimation and statistical inferences with many instruments for a single endogenous regressor. We discuss how to assess the strength of the instruments and how to conduct weak identification-robust inference under heteroskedasticity. We establish new results for a jack-knifed version of the Lagrange Multiplier (LM) test statistic. Furthermore, we extend the weak-identification-robust tests to settings with both many exogenous regressors and many instruments. We propose a test that properly partials out many exogenous regressors while preserving the re-centering property of the jack-knife. The proposed tests have correct size and good power properties.
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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 | Angrist, J. and A. Krueger (1991) Does compulsory school attendance affect schooling and earnings? | 1.000 | 16 | 4 | 100% |
| 2 | Mikusheva, A. and L. Sun (2022) Inference with Many Weak Instruments self | 0.865 | 17 | 4 | 65% |
| 3 | Angrist, J. D. and B. Frandsen (2022) Machine labor | 0.843 | 3 | 3 | 100% |
| 4 | Staiger, D. and J. Stock (1997) Instrumental variables regression with weak instruments | 0.811 | 4 | 2 | 100% |
| 5 | Bekker, P. A (1994) Alternative Approximations to the Distributions of Instrumental Variable Estimators | 0.811 | 4 | 2 | 100% |
| 6 | Hausman, J. A., W. K. Newey, T. Woutersen, J. C. Chao, and N. R. Swa… (2012) Instrumental variable estimation with heteroskedasticity and many instruments | 0.811 | 4 | 2 | 100% |
| 7 | Matsushita, Y. and T. Otsu (2022) A jackknife lagrange multiplier test with many weak instruments | 0.811 | 4 | 2 | 100% |
| 8 | Hansen, C., J. Hausman, and W. Newey (2008) Estimation With Many Instrumental Variables | 0.737 | 3 | 2 | 100% |
| 9 | Kolesar, M (2013) Estimation in an instrumental variables model with treatment effect heterogeneity | 0.737 | 3 | 2 | 100% |
| 10 | Chao, J. C., N. R. Swanson, and T. Woutersen (2023) Jackknife Estimation of a Cluster-Sample IV Regression Model with Many Weak Instruments | 0.644 | 4 | 1 | 100% |
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