Two-parameter estimator for the Tobit regression model

Abstract

The ridge estimator has been shown to be an effective shrinkage method for reducing the impacts of multicollinearity on a number of occasions. When there is just little information about the dependent variable for some observations, the Tobit regression model is a well-known model. However, it is well known that in the presence of multicollinearity, the variance of the maximum likelihood estimator (MLE) of the Tobit regression model coefficients can be negatively affected. In this research, a new two-parameter estimator is proposed to solve the Tobit regression model's multicollinearity problem. In terms of MSE, our Monte Carlo simulation results show that the proposed estimate outperforms the MLE and ridge estimators.