Stanislav Anatolyev, Mikkel Sølvsten
arXiv 16 Mar 2020 · Econometrics · publishedJournal of Econometrics (2023) · 5 citations (OpenAlex)
arXiv:2003.07320 · PDF · DOI · OpenAlex · Extracted main text
We propose a hypothesis test that allows for many tested restrictions in a heteroskedastic linear regression model. The test compares the conventional F statistic to a critical value that corrects for many restrictions and conditional heteroskedasticity. This correction uses leave-one-out estimation to correctly center the critical value and leave-three-out estimation to appropriately scale it. The large sample properties of the test are established in an asymptotic framework where the number of tested restrictions may be fixed or may grow with the sample size, and can even be proportional to the number of observations. We show that the test is asymptotically valid and has non-trivial asymptotic power against the same local alternatives as the exact F test when the latter is valid. Simulations corroborate these theoretical findings and suggest excellent size control in moderately small samples, even under strong heteroskedasticity.
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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 | Anatolyev, S (2012) Inference in regression models with many regressors self | 1.000 | 5 | 3 | 100% |
| 2 | Cattaneo, M. D., M. Jansson, and W. K. Newey (2018) Inference in linear regression models with many covariates and heteroscedasticity | 0.965 | 10 | 4 | 90% |
| 3 | Kline, P., R. Saggio, and M. Slvsten (2020) Leave-out estimation of variance components | 0.937 | 17 | 4 | 82% |
| 4 | Calhoun, G (2011) Hypothesis testing in linear regression when $k/n$ is large | 0.928 | 4 | 3 | 100% |
| 5 | MacKinnon, J. G (2013) Thirty years of heteroskedasticity-robust inference | 0.811 | 4 | 2 | 100% |
| 6 | Berndt, E. R. and N. E. Savin (1977) Conflict among criteria for testing hypotheses in the multivariate linear regression model | 0.644 | 2 | 2 | 100% |
| 7 | Chao, J. C., J. A. Hausman, W. K. Newey, N. R. Swanson, and T. Woute… (2014) Testing overidentifying restrictions with many instruments and heteroskedasticity | 0.644 | 2 | 2 | 100% |
| 8 | Richard, P (2019) Residual bootstrap tests in linear models with many regressors | 0.644 | 2 | 2 | 100% |
| 9 | Anatolyev, S. and N. Gospodinov (2011) Specification testing in models with many instruments self | 0.511 | 2 | 1 | 100% |
| 10 | Chao, J. C., N. R. Swanson, J. A. Hausman, W. K. Newey, and T. Woute… (2012) Asymptotic distribution of JIVE in a heteroskedastic IV regression with many instruments | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 51 scored citations.
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| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Cluster-Robust Inference for Quadratic Forms | 1.000 | 6 | 4 |
| 2 | A Dimension-Agnostic Bootstrap Anderson-Rubin Test For Instrumental Variable Regressions | 0.956 | 8 | 4 |
| 3 | Inference with Many Weak Instruments and Heterogeneity | 0.811 | 4 | 2 |
| 4 | Leniency Designs: An Operator's Manual | 0.644 | 2 | 2 |
| 5 | Adjustments with Many Regressors under Covariate-Adaptive Randomizations | 0.405 | 1 | 1 |
| 6 | 2308.09535 | 0.405 | 1 | 1 |
| 7 | The Fragility of Sparsity | 0.405 | 1 | 1 |
| 8 | Robust Inference with High-Dimensional Instruments | 0.405 | 1 | 1 |