EconPapers    
Economics at your fingertips  
 

RIDGE ESTIMATORS FOR PROBIT REGRESSION: WITH AN APPLICATION TO LABOUR MARKET DATA

Håkan Locking (), Kristofer Månsson () and Ghazi Shukur

Bulletin of Economic Research, 2014, vol. 66, issue S1, S92-S103

Abstract: type="main">

In this paper we propose ridge regression estimators for probit models since the commonly applied maximum likelihood (ML) method is sensitive to multicollinearity. An extensive Monte Carlo study is conducted where the performance of the ML method and the probit ridge regression (PRR) is investigated when the data are collinear. In the simulation study we evaluate a number of methods of estimating the ridge parameter k that have recently been developed for use in linear regression analysis. The results from the simulation study show that there is at least one group of the estimators of k that regularly has a lower mean squared error than the ML method for all different situations that have been evaluated. Finally, we show the benefit of the new method using the classical Dehejia and Wahba dataset which is based on a labour market experiment.

Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://hdl.handle.net/10.1111/boer.12015 (text/html)
Access to full text is restricted to subscribers.

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:bla:buecrs:v:66:y:2014:i:s1:p:s92-s103

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0307-3378

Access Statistics for this article

More articles in Bulletin of Economic Research from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().

 
Page updated 2021-04-03
Handle: RePEc:bla:buecrs:v:66:y:2014:i:s1:p:s92-s103