The Fragility of Sparsity
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911677689102336 |
|---|---|
| author | Kolesár, Michal Müller, Ulrich K. Roelsgaard, Sebastian T. |
| author_facet | Kolesár, Michal Müller, Ulrich K. Roelsgaard, Sebastian T. |
| contents | We show, using three empirical applications, that linear regression estimates predicated on the assumption of sparsity are fragile in two ways. First, we document that different choices of the regressor matrix which do not impact ordinary least squares (OLS) estimates, such as the choice of baseline category with categorical controls, can move sparsity-based estimates by two standard errors or more. Second, we develop two tests of the sparsity assumption by comparing sparsity-based estimators with OLS. The tests tend to reject the sparsity assumption in all three applications. Unless the number of regressors is comparable to or exceeds the sample size, OLS yields more robust inference at little efficiency cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_02299 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The Fragility of Sparsity Kolesár, Michal Müller, Ulrich K. Roelsgaard, Sebastian T. Econometrics Methodology We show, using three empirical applications, that linear regression estimates predicated on the assumption of sparsity are fragile in two ways. First, we document that different choices of the regressor matrix which do not impact ordinary least squares (OLS) estimates, such as the choice of baseline category with categorical controls, can move sparsity-based estimates by two standard errors or more. Second, we develop two tests of the sparsity assumption by comparing sparsity-based estimators with OLS. The tests tend to reject the sparsity assumption in all three applications. Unless the number of regressors is comparable to or exceeds the sample size, OLS yields more robust inference at little efficiency cost. |
| title | The Fragility of Sparsity |
| topic | Econometrics Methodology |
| url | https://arxiv.org/abs/2311.02299 |