Multicollinearity Problem in Cobb-Douglas Production Function

The Cobb-Douglas Production Functions (CDPF) are among the best known production functions utilized in applied production analysis. The estimation of production functions in general and CDPF in particular, presents many additional problems. Multicollinearity arising in least squares estimation of the CDPF is not new. It is a problem that emerged with the model itself. In this study an estimation method for CDPF parameters by partial least squares path modeling (PLS-PM) is developed. It solves the attendant multicollinearity problem. The newly developed method is then applied to agricultural production data obtained from Al- Kufra Agricultural Production Project, Libya. The results from the model strongly suggest that the measures like composite reliability and goodness-of-fit represent their respective latent constructs well. Consequently, a further investigation of the model is pursued and an analysis on PLS-PM is performed.