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| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Targeted undersmoothing: sensitivity analysis for sparse estimators |
| Organization Unit | |
| Authors |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Journal Title | The Review of Economics and Statistics |
| Publisher | MIT Press |
| Geographical Reach | international |
| ISSN | 0034-6535 |
| Volume | 105 |
| Number | 1 |
| Page Range | 101 - 112 |
| Date | 2023 |
| Abstract Text | This paper proposes a procedure for assessing sensitivity of inferential conclusions for functionals of sparse high-dimensional models following model selection. The proposed procedure is called targeted undersmoothing. Functionals considered include dense functionals that may depend on many or all elements of the highdimensional parameter vector. The sensitivity analysis is based on systematic enlargements of an initially selected model. By varying the enlargements, one can conduct sensitivity analysis about the strength of empirical conclusions to model selection mistakes. We illustrate the procedure's performance through simulation experiments and two empirical examples. |
| Free access at | DOI |
| Digital Object Identifier | 10.1162/rest_a_01017 |
| Other Identification Number | merlin-id:22118 |
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| Keywords | Economics and econometrics, social sciences (miscellaneous), misspecification, model selection, sparsity, dense functionals, hypothesis testing |