Efficient estimate transcendental logarithmic (Translog) model: Comparing the estimator using Monte Carlo

Transcendental logarithmic (Translog) model suffers from the multicollinearity problem since the squares are added to the model and cross products of variables. Since classical estimators can not be used under multicollinearity, biased estimators can be used to overcome the problem. In this study, ridge, restricted ridge, generalized maximum entropy, restricted generalized maximum entropy, ordinary least squares, restricted ordinary least squares estimators are compared according to the mean squared error criteria. In the application section, Monte Carlo simulation is used to obtain mean squared error values. In conclusion, restricted generalized maximum entropy is decided as the most efficient estimator.