Philipp Bach, Victor Chernozhukov, Martin Spindler
arXiv 13 Sep 2018 · Econometrics · 23 citations (OpenAlex)
arXiv:1809.04951 · PDF · DOI · OpenAlex · Extracted main text
Due to the increasing availability of high-dimensional empirical applications in many research disciplines, valid simultaneous inference becomes more and more important. For instance, high-dimensional settings might arise in economic studies due to very rich data sets with many potential covariates or in the analysis of treatment heterogeneities. Also the evaluation of potentially more complicated (non-linear) functional forms of the regression relationship leads to many potential variables for which simultaneous inferential statements might be of interest. Here we provide a review of classical and modern methods for simultaneous inference in (high-dimensional) settings and illustrate their use by a case study using the R package hdm. The R package hdm implements valid joint powerful and efficient hypothesis tests for a potentially large number of coeffcients as well as the construction of simultaneous confidence intervals and, therefore, provides useful methods to perform valid post-selection inference based on the LASSO.
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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 | Belloni, Chernozhukov, and Kato (2015) Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems | 1.000 | 7 | 4 | 100% |
| 2 | Chernozhukov, Chetverikov, and Kato (2013) Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors | 1.000 | 6 | 3 | 100% |
| 3 | Romano and Wolf (2005) Stepwise Multiple Testing as Formalized Data Snooping | 0.843 | 3 | 3 | 100% |
| 4 | Chernozhukov, Hansen, and Spindler (2016) hdm: High-Dimensional Metrics self | 0.737 | 3 | 3 | 67% |
| 5 | Belloni, Chernozhukov, and Hansen (2014) Inference on Treatment Effects After Selection Amongst High-Dimensional Controls | 0.737 | 3 | 2 | 100% |
| 6 | Benjamini and Hochberg (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing | 0.644 | 2 | 2 | 100% |
| 7 | Romano and Wolf (2005) Exact and Approximate Stepdown Methods for Multiple Hypothesis Testing | 0.644 | 2 | 2 | 100% |
| 8 | Romano and Wolf (2016) Efficient computation of adjusted p-values for resampling-based stepdown multiple testing | 0.511 | 2 | 1 | 100% |
| 9 | Belloni, Chernozhukov, Chetverikov, and Wei Uniformly valid post-regularization confidence regions for many functional parameters in z-estimation framework | 0.405 | 1 | 1 | 100% |
| 10 | Bach, Chernozhukov, and Spindler (2018) An econometric (re-)analysis of the gender wage gap in a high-dimensional setting self | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 12 scored citations.
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| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Big Data meets Causal Survey Research: Understanding Nonresponse in the Recruitment of a Mixed-mode Online Panel | 0.405 | 1 | 1 |