A two-stage Bridge estimator for regression models with endogeneity based on control function method

Fatemeh Bahador, Ayyub Sheikhi () and Alireza Arabpour
Additional contact information
Fatemeh Bahador: Shahid Bahonar University of Kerman
Ayyub Sheikhi: Shahid Bahonar University of Kerman
Alireza Arabpour: Shahid Bahonar University of Kerman

Computational Statistics, 2024, vol. 39, issue 3, No 11, 1370 pages

Abstract: Abstract In this study, we investigate a penalty-based two-stage least square estimator in regression models when the exploratory variables are correlated with the error term. We propose a two-stage Bridge estimator to overcome this endogeneity problem in high-dimensional data. Our proposed estimator enjoys remarkable statistical properties such as consistency and asymptotic normality. As special cases, this method deals some ill-condition situations such as the multicollinearity as well as the sparsity. Performance of the proposed estimators is demonstrated by simulation studies and it is compared to the existing estimators. An application in real data set is presented for illustration.

Keywords: Bridge estimator; Endogeneity; Instrumental variable; Multicollinearity; Sparsity; Control function (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-023-01379-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
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:spr:compst:v:39:y:2024:i:3:d:10.1007_s00180-023-01379-9

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-023-01379-9

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().