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Alternative Estimators in Logit Model in The Presence of Multicollinearity and Heteroscedasticity with A Stochastic Linear Restricted

Saja Mohammad Hussein

Abstract



We propose grafting the maximum likelihood estimator (ML)for logit model into the mixed estimator (ME) and stochastic restricted ridge regression estimator (SRRR) for a linear model. To obtain estimators that can apply to models in which the dependent variable is binary in the presence of multicollinearity problem in the case of the heteroscedasticity of error term when stochastic linear restrictions are assumed to hold. A mean square error (MSE) is used to compare the performance of the proposed estimators through a simulation study and investigate The behavior of these estimators using some Ridge parameters.

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