Qingliang Fan, Zijian Guo, Ziwei Mei
arXiv 30 Apr 2022 · Econometrics · publishedJournal of Business and Economic Statistics (2024) · 3 citations (OpenAlex)
arXiv:2205.00171 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes an overidentifying restriction test for high-dimensional linear instrumental variable models. The novelty of the proposed test is that it allows the number of covariates and instruments to be larger than the sample size. The test is scale-invariant and is robust to heteroskedastic errors. To construct the final test statistic, we first introduce a test based on the maximum norm of multiple parameters that could be high-dimensional. The theoretical power based on the maximum norm is higher than that in the modified Cragg-Donald test (Koles\'{a}r, 2018), the only existing test allowing for large-dimensional covariates. Second, following the principle of power enhancement (Fan et al., 2015), we introduce the power-enhanced test, with an asymptotically zero component used to enhance the power to detect some extreme alternatives with many locally invalid instruments. Finally, an empirical example of the trade and economic growth nexus demonstrates the usefulness of the proposed test.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
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 | Kolesár, M (2018) Minimum distance approach to inference with many instruments | 1.000 | 16 | 4 | 100% |
| 2 | Chernozhukov, V., Chetverikov, D., and Kato, K (2013) Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors | 1.000 | 10 | 3 | 100% |
| 3 | Belloni, A., Chernozhukov, V., and Hansen, C (2014) Inference on treatment effects after selection among high-dimensional controls | 0.928 | 4 | 3 | 100% |
| 4 | Javanmard, A. and Montanari, A (2014) Confidence intervals and hypothesis testing for high-dimensional regression | 0.874 | 5 | 2 | 100% |
| 5 | Chao, J. C., Hausman, J. A., Newey, W. K., Swanson, N. R., and Woute… (2014) Testing overidentifying restrictions with many instruments and heteroskedasticity | 0.843 | 3 | 3 | 100% |
| 6 | Fan, J., Liao, Y., and Yao, J (2015) Power enhancement in high-dimensional cross-sectional tests | 0.843 | 3 | 3 | 100% |
| 7 | Zhang, X. and Cheng, G (2017) Simultaneous inference for high-dimensional linear models | 0.843 | 3 | 3 | 100% |
| 8 | Gold, D., Lederer, J., and Tao, J (2020) Inference for high-dimensional instrumental variables regression | 0.811 | 4 | 2 | 100% |
| 9 | Fan, Q. and Zhong, W (2018) Nonparametric additive instrumental variable estimator: A group shrinkage estimation perspective self | 0.644 | 4 | 1 | 100% |
| 10 | Belloni, A., Hansen, C., and Newey, W (2022) High-dimensional linear models with many endogenous variables | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 49 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 for Nonlinear Endogenous Treatment Effects Accounting for High-Dimensional Covariate Complexity | 0.644 | 2 | 2 |
| 2 | Enhanced power enhancements for testing many moment equalities: Beyond the $2$- and $$-norm | 0.405 | 1 | 1 |