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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The paper discusses the development of the agricultural production model for the agricultural sector based on the model suggested by the Libyan Government underlining the country policy in promoting this sector. The development of this agricultural production model is through the method of Cobb Douglas production function. As a step of precaution, the robust partial least squares is incorporated in the Cobb Douglas production model to avoid the effect of multicollinearity and outliers in the data. The new model is known as Robust Partial Least Squares of Cobb Douglas Production Function (RPLS-CDF). This study shows that the RPLSCDF overcomes the problem in Cobb Douglas production model when multicollinearity and the outliers exist in the data as compared to the conventional method namely the log transformation. It is found that the pernicious effect of the outliers have been reduced considerably by the proposed robust estimators. Keywords Cobb Douglas Production Function, Robust Parti.....

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