EconPapers    
Economics at your fingertips  
 

Sharp Threshold Detection Based on Sup-Norm Error Rates in High-Dimensional Models

Laurent Callot (), Mehmet Caner (), Anders Kock and Juan Andres Riquelme

Journal of Business & Economic Statistics, 2017, vol. 35, issue 2, 250-264

Abstract: We propose a new estimator, the thresholded scaled Lasso, in high-dimensional threshold regressions. First, we establish an upper bound on the ℓ∞ estimation error of the scaled Lasso estimator of Lee, Seo, and Shin. This is a nontrivial task as the literature on high-dimensional models has focused almost exclusively on ℓ1 and ℓ2 estimation errors. We show that this sup-norm bound can be used to distinguish between zero and nonzero coefficients at a much finer scale than would have been possible using classical oracle inequalities. Thus, our sup-norm bound is tailored to consistent variable selection via thresholding. Our simulations show that thresholding the scaled Lasso yields substantial improvements in terms of variable selection. Finally, we use our estimator to shed further empirical light on the long-running debate on the relationship between the level of debt (public and private) and GDP growth. Supplementary materials for this article are available online.

Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2015.1052461 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Sharp Threshold Detection Based on Sup-norm Error rates in High-dimensional Models (2015) Downloads
Working Paper: Sharp Threshold Detection based on Sup-Norm Error Rates in High-dimensional Models (2015) 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:taf:jnlbes:v:35:y:2017:i:2:p:250-264

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1080/07350015.2015.1052461

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2022-01-18
Handle: RePEc:taf:jnlbes:v:35:y:2017:i:2:p:250-264