Asar, YasinKilinc, Kadriye2024-02-232024-02-2320200233-18881029-4910https://doi.org/10.1080/02331888.2020.1775597https://hdl.handle.net/20.500.12452/13027In this study, the effects of multicollinearity on the maximum likelihood estimator are analyzed in the probit regression model. It is known that the near-linear dependencies in the design matrix affect the maximum likelihood estimation negatively, namely, the standard errors become so large so that the estimations are said to be inconsistent. Therefore, a new jackknifed ridge estimator is introduced as an alternative to the maximum likelihood technique and the well-known ridge estimator. The mean squared error properties of the listed estimators are investigated theoretically. In order to evaluate the performance of the estimators, a Monte Carlo simulation study is designed, and simulated mean squared error and squared bias are used as performance criteria. Finally, the benefits of the new estimator are illustrated via a real data application.eninfo:eu-repo/semantics/closedAccessMulticollinearityRidge EstimatorJackknifed Ridge EstimatorProbit ModelMean Squared ErrorArticle5446676852-s2.0-85086648501Q3WOS:000542464800001Q310.1080/02331888.2020.1775597