Three Essays in Double/debiased Machine Learning and High-dimensional Econometrics
| dc.contributor.advisor | Sasaki, Yuya | |
| dc.contributor.committeeChair | Sasaki, Yuya | |
| dc.creator | Ma, Yukun | |
| dc.creator.orcid | 0009-0002-3391-6966 | |
| dc.date.accessioned | 2024-05-15T17:20:50Z | |
| dc.date.created | 2024-05 | |
| dc.date.issued | 2024-03-25 | |
| dc.date.submitted | May 2024 | |
| dc.date.updated | 2024-05-15T17:20:51Z | |
| dc.description.abstract | In today’s big data world, we have witnessed rapidly increasing popularity of machine learning methods in empirical studies, such as random forests, lasso, post-lasso, elastic nets, ridge, deep neural networks, and boosted trees among others. The objective of this paper is motivated by recently increasing demand for Dou- ble/debiased Machine Learning (DML) methods in empirical research. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1803/18965 | |
| dc.language.iso | en | |
| dc.subject | Weak identification, local average treatment effect, double/debiased machine learning, multiway cross fitting, dyadic cross fitting | |
| dc.title | Three Essays in Double/debiased Machine Learning and High-dimensional Econometrics | |
| dc.type | Thesis | |
| dc.type.material | text | |
| local.embargo.lift | 2024-11-01 | |
| local.embargo.terms | 2024-11-01 | |
| thesis.degree.discipline | Economics | |
| thesis.degree.grantor | Vanderbilt University Graduate School | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | PhD |