Anders Kock
Biographic Data
| ID | 9107586 |
|---|---|
| NAME | Anders Kock |
| GIVEN NAMES | Anders |
| FAMILY NAME | Kock |
| SIGNATURE | KOCK A |
| AFFILIATIONS | University of Oxford |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
A Ridge-Regularized Jackknifed Anderson-Rubin Test
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-regularized version of the jackknifed Anderson and Rubin (henceforth AR) test controls asymptotic size in the presence of heteroscedasticity, and when the instruments may be arbitrarily weak. Asymptotic size control is established under weaker assu…
Sharp Threshold Detection Based on Sup-Norm Error Rates in High-Dimensional Models
We propose a new estimator, the thresholded scaled Lasso, in high-dimensional threshold regressions. First, we establish an upper bound on the l∞ estimation error of the scaled Lasso estimator of Lee, Seo, and Shin. This is a nontrivial task as the literature on high-dimensional models has focused almost exclusively on l1 and l2 estimation errors. We show that this sup-norm bound can be used to distinguish between zero and nonzero coefficients at…
No prominent works on this page.
Sharp Threshold Detection Based on Sup-Norm Error Rates in High-Dimensional Models
We propose a new estimator, the thresholded scaled Lasso, in high-dimensional threshold regressions. First, we establish an upper bound on the l∞ estimation error of the scaled Lasso estimator of Lee, Seo, and Shin. This is a nontrivial task as the literature on high-dimensional models has focused almost exclusively on l1 and l2 estimation errors. We show that this sup-norm bound can be used to distinguish between zero and nonzero coefficients at…
A Ridge-Regularized Jackknifed Anderson-Rubin Test
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-regularized version of the jackknifed Anderson and Rubin (henceforth AR) test controls asymptotic size in the presence of heteroscedasticity, and when the instruments may be arbitrarily weak. Asymptotic size control is established under weaker assu…
Advanced Statistical Methods and Models (2 works) · Econometrics (2 works) · Mathematics (2 works) · Statistical Methods and Inference (2 works) · Statistics (2 works) · Cartography (1 work) · Computer Science (1 work) · Covariate (1 work) · Elastic net regularization (1 work) · Geography (1 work)