AThis research aimed to develop a new model for estimation problems of Cobb-Douglas production function. Specifically, the researcher focused on the problems of outliers and multicollinearity, and on solutions that were proposed to deal with these issues simultaneously. The Cobb-Douglas production function was usually fitted by first linearizing the models through logarithmic transformation and then applying the method of least squares. However the log transformation process did not get rid of all the outliers and multicollinearity problems. These problems appeared to have wide repercussions. Yet, these problems had not been known for their written solutions, and no serious research had been conducted in this field. The researcher attempted to develop the best possible method for providing estimates in the context of the Libyan agricultural sector by using the robust partial least squares path modeling (RPLS­PM). The results from RPLS-PM analysis showed that each block consisted of st.....

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The Cobb-Douglas production function (Cobb and Douglas, 1928) is still today the most ubiquitous form in theoretical and empirical analyses of growth and productivity. The estimation of the parameters of aggregate production functions is central to much of today_s work on growth, technological change, productivity, and labour. Empirical estimates of aggregate production functions are a tool of analysis essential in macroeconomics, and important theoretical constructs, such as potential output, technical change, or the demand for labour, are based on them. It is usually fitted by first linearizing the models through logarithmic transformation and then applying method of least squares (Prajneshu, 2008), but the ordinary least squares (OLS) is not the best estimation method (Kahane , 2001). In statistics and econometrics, more and more attention is paid to techniques that can deal with data containing atypical observations, which can arise from outliers, miscoding, or heterogeneity and no.....

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