Tom Boot, Johannes W. Ligtenberg
arXiv 14 Mar 2023 · Econometrics · publishedJournal of Econometrics (2025) · 1 citations (OpenAlex)
arXiv:2303.07822 · PDF · DOI · OpenAlex · Extracted main text
Identification-robust hypothesis tests are commonly based on the continuous updating GMM objective function. When the number of moment conditions grows proportionally with the sample size, the large-dimensional weighting matrix prohibits the use of conventional asymptotic approximations and the behavior of these tests remains unknown. We show that the structure of the weighting matrix opens up an alternative route to asymptotic results when, under the null hypothesis, the distribution of the moment conditions satisfies a symmetry condition known as reflection invariance. We provide several examples in which the invariance follows from standard assumptions. Our results show that existing tests will be asymptotically conservative, and we propose an adjustment to attain nominal size in large samples. We illustrate our findings through simulations for various linear and nonlinear models, and an empirical application on the effect of the concentration of financial activities in banks on systemic risk.
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| Reference | Intensity | Mentions | Sections | Main text | |
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| 1 | Honoré, B. E (1992) Trimmed LAD and least squares estimation of truncated and censored regression models with fixed effects | 1.000 | 24 | 4 | 100% |
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| 3 | Hausman, J. A., Newey, W. K., Woutersen, T., Chao, J. C., and Swanso… (2012) Instrumental variable estimation with heteroskedasticity and many instruments | 0.909 | 8 | 4 | 75% |
| 4 | Chernozhukov, V., Hansen, C., and Jansson, M (2009) Finite sample inference for quantile regression models | 0.885 | 13 | 5 | 69% |
| 5 | Langfield, S. and Pagano, M (2016) Bank bias in Europe: effects on systemic risk and growth | 0.874 | 9 | 2 | 100% |
| 6 | Chao, J. C., Swanson, N. R., Hausman, J. A., Newey, W. K., and Woute… (2012) Asymptotic distribution of JIVE in a heteroskedastic IV regression with many instruments | 0.860 | 11 | 3 | 64% |
| 7 | Kleibergen, F (2005) Testing parameters in GMM without assuming that they are identified | 0.855 | 8 | 3 | 62% |
| 8 | Mikusheva, A. and Sun, L (2022) Inference with many weak instruments | 0.843 | 5 | 3 | 60% |
| 9 | Crudu, F., Mellace, G., and Sándor, Z (2021) Inference in instrumental variable models with heteroskedasticity and many instruments | 0.737 | 3 | 3 | 67% |
| 10 | Ligtenberg, J. W (2023) Inference in IV models with clustered dependence, many instruments and weak identification self | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 45 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Inference in clustered IV models with many and weak instruments | 0.843 | 4 | 3 |