Testing-Based Forward Model Selection
American Economic Review, 2017, vol. 107, issue 5, 266-69
This paper defines and studies a variable selection procedure called Testing-Based Forward Model Selection. The procedure inductively selects covariates which increase predictive accuracy into a working statistical regression model until a stopping criterion is met. The stopping criteria and selection criteria are defined using statistical hypothesis tests. The paper explicitly describes a testing procedure in the context of high-dimensional linear regression with heteroskedastic disturbances. Finally, a simulation study examines finite sample performance of the proposed procedure and shows that it behaves favorably in high-dimensional sparse settings in terms of prediction error and size of selected model.
JEL-codes: C52 (search for similar items in EconPapers)
Note: DOI: 10.1257/aer.p20171039
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (7) Track citations by RSS feed
Downloads: (external link)
https://www.aeaweb.org/articles/attachments?retrie ... V5KEnRk4w52XBe3NdO-A (application/zip)
https://www.aeaweb.org/articles/attachments?retrie ... EdpOs8lUAOJ2D2pq5uwD (application/zip)
Access to full text is restricted to AEA members and institutional subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:aea:aecrev:v:107:y:2017:i:5:p:266-69
Ordering information: This journal article can be ordered from
Access Statistics for this article
American Economic Review is currently edited by Pinelopi Koujianou Goldberg
More articles in American Economic Review from American Economic Association Contact information at EDIRC.
Series data maintained by Jane Voros ().