Max-Sebastian Dovì, Anders Bredahl Kock, Sophocles Mavroeidis
arXiv 7 Sep 2022 · Econometrics · publishedJournal of Business and Economic Statistics (2023) · 6 citations (OpenAlex)
arXiv:2209.03259 · PDF · DOI · OpenAlex · Extracted main text
We consider hypothesis testing in instrumental variable regression models with few included exogenous covariates but many instruments -- possibly more than the number of observations. We show that a ridge-regularised version of the jackknifed Anderson Rubin (1949, henceforth AR) test controls asymptotic size in the presence of heteroskedasticity, and when the instruments may be arbitrarily weak. Asymptotic size control is established under weaker assumptions than those imposed for recently proposed jackknifed AR tests in the literature. Furthermore, ridge-regularisation extends the scope of jackknifed AR tests to situations in which there are more instruments than observations. Monte-Carlo simulations indicate that our method has favourable finite-sample size and power properties compared to recently proposed alternative approaches in the literature. An empirical application on the elasticity of substitution between immigrants and natives in the US illustrates the usefulness of the proposed method for practitioners.
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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 | Hansen, C. and D. Kozbur (2014) Instrumental variables estimation with many weak instruments using regularized JIVE | 1.000 | 5 | 4 | 100% |
| 2 | Card, D (2009) Immigration and Inequality | 0.874 | 10 | 2 | 100% |
| 3 | Chao, J., N. Swanson, J. Hausmann, W. Newey, and T. Woutersen (2012) Asymptotic Distribution of JIVE in a Heteroskedastic IV Regression with Many Instruments | 0.843 | 3 | 3 | 100% |
| 4 | Anderson, T. and H. Rubin (1949) Estimation of the Parameters of a Single Equation in a Complete System of Stochastic Equations | 0.737 | 3 | 2 | 100% |
| 5 | Bai, Z. and J. Silverstein (2010) Spectral analysis of large dimensional random matrices, Volume 20 | 0.693 | 6 | 1 | 100% |
| 6 | Anatolyev, S. and N. Gospodinov (2011) Specification Testing in Models with Many Instruments | 0.644 | 4 | 1 | 100% |
| 7 | Blandhol, C., J. Bonney, M. Mogstad, and A. Torgovitsky (2022) When is TSLS Actually LATE? | 0.644 | 2 | 2 | 100% |
| 8 | Crudu, F., G. Mellace, and Z. Sándor (2020) Inference in Instrumental Variable Models with Heteroskedasticity and Many Instruments | 0.585 | 3 | 1 | 100% |
| 9 | Belloni, A., D. Chen, V. Chernozhukov, and C. Hansen (2012) Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain | 0.511 | 2 | 1 | 100% |
| 10 | Bun, M., H. Farbmacher, and R. Poldermans (2020) Finite sample properties of the GMM Anderson-Rubin test | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 33 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Enhanced power enhancements for testing many moment equalities: Beyond the $2$- and $$-norm | 0.644 | 2 | 2 |
| 2 | A Dimension-Agnostic Bootstrap Anderson-Rubin Test For Instrumental Variable Regressions | 0.000 | 1 | 1 |