EconPapers    
Economics at your fingertips  
 

Inference after lasso model selection

David Drukker

London Stata Conference 2019 from Stata Users Group

Abstract: The increasing availability of high-dimensional data and increasing interest in more realistic functional forms have sparked a renewed interest in automated methods for selecting the covariates to include in a model. I discuss the promises and perils of model selection and pay special attention to estimators that provide reliable inference after model selection. I will demonstrate how to use Stata 16's new features for double selection, partialing out, and cross-fit partialing out to estimate the effects of variables of interest while using lasso methods to select control variables.

Date: 2019-09-15
New Economics Papers: this item is included in nep-big
References: Add references at CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed

Downloads: (external link)
http://repec.org/usug2019/Drukker_uk19.pdf (application/pdf)

Related works:
Working Paper: Inference after lasso model selection (2019) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:boc:usug19:25

Access Statistics for this paper

More papers in London Stata Conference 2019 from Stata Users Group Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F Baum ().

 
Page updated 2023-03-26
Handle: RePEc:boc:usug19:25