Applications of Mitscherlich Baule function: a robust regression approach

Abstract

Consistent estimation techniques need to be implemented to obtain robust empirical outcomes which help policymakers formulating public policies. Therefore, Mitscherlich Baule function was implemented using robust regression model based on a growth equation. In Mitscherlich Baule Function two methods were used one least square Method and Iteratively Reweighted Least Square Method. In Least Square method Gauss Newton Method, was used for the study, whereas in Iteratively Reweighted Least Square method Marquardt Method and Gradient Method was used. Secondary data has been used in the study. Classical and robust procedures were employed for the estimation of the parameters. Thus empirical results reveal that the overall fit of the model improves in case of Marquardt technique. Thus, empirical findings exhibit that the results, obtained through Marquardt method are better than LS techniques. The present study focus on when you are dealing with nonlinear production function Mitscherlich Baule function comes out to be best for handling outliers in data sets and also helpful for policy makers for formulating public policies.

Keywords:

• Mitscherlich Baule Function
• Marquardt Method
• gradient method
• iteratively reweighted least square

Disclosure statement

Authors declare that there is no conflict of interest.