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Despite the important role that production function has played in growth literature, few attempts have been made to change the methodology to estimate it. The Cobb-Douglas functions are among the best known production functions utilized in applied production analysis. This paper describes development of a new model based on Cobb-Douglas production function with the used of robust method and partial least squares path modeling for parameter estimation. The new model attempted to solve two main problems in modeling namely the issue of multicollinearity and outliers. Each issue was handled separately but using the same method of least square for parameter estimations. This paper goes on to provide an overview of the measurements and structural criteria needed for model development and, also to introduce a robust partial least squares-path modeling for the Cobb-Douglas production function (RPLS-PM-CD). The researcher hypothesizes that utilization of the minimum covariance determinant (MCD) provides an estimate by the measurement model and expresses the structural relationships between the latent variables through the partial least squares-path modeling (PLS-PM). The inputs and outputs of the RPLS-PM-CD were based on agricultural wheat production data pertaining to AlKufra Agricultural Production Project. This paper is more theoretical and should be seen as a new way to estimate Cobb-Douglas production function.

Model development for wheat production: Outliers and multicollinearity<br>problem in Cobb-Douglas production function