A new robust ridge estimator for linear regression model with non normal, heteroscedastic and autocorrelated errors

Sohail Chand and Maha Shabbir

Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 23, 7651-7667

Abstract: The ridge regression models the dependent variable as a function of explanatory variables when collinearity exists in the data set. In this study, a new robust estimator is introduced to determine the optimal value of the ridge parameter when a multicollinearity problem emerges with complex behavior of error term. The suggested ridge estimator is a combination of the number of explanatory variables, the standard error of the regression model, and the maximum eigenvalue of the correlation matrix of the explanatory variables. The effectiveness of the suggested new estimator is assessed using extensive Monte Carlo simulations with different distributions of error terms. The simulation findings reveal that the proposed estimator outperforms the ordinary least square (OLS) and popular and closely related ridge estimators regarding minimum mean squared error (MSE). The application of a new estimator is illustrated on two real-life data sets. The results show that the suggested estimator efficiently handles multicollinearity when the error terms are normally distributed, non normally distributed (positively skewed, exponentially decreasing, symmetrical, and heavy-tailed structure), heteroscedastic, and/or autocorrelated.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2025.2479640 (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:taf:lstaxx:v:54:y:2025:i:23:p:7651-7667

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2025.2479640

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().