Essays on A Stochastic Frontier Cost Function Approach Assignment

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The paper "A Stochastic Frontier Cost Function Approach" is a wonderful example of an assignment on macro and microeconomics. The Maximum-Likelihood ML method helps in testing for the estimation of the parameters in the frontier production function. The ML requires the numerical maximization of the likelihood function. The individual mathematical expectation (technical efficiency)TE = exp (-ui) of the variables could only be evaluated/ predicted under the distributional assumptions of the underlying technical inefficiency. When the UIS is I . i.d and the assumption is that they are half-normal random variables, the ML Estimator to find the mean technical efficiency is by substituting ML estimators for the parameters in the equation.

This is because the sample’ s technical efficiencies may be predicted. e) Re-run the model using the efficiency effects model and including the five efficiency variables outlined above. Interpret results for the efficiency variables, reporting any management implications from the technical efficiency estimates obtained. Maximum 300 words. In order to make an estimation of the frontier production function estimate, the data is created in the Frontier form referred to as ‘ Model 2’ . The variables included1.

Age of the household head2. Education of the household head3. Household size4. Number of adults in the household5. Percentage of the area classified as upland fieldsThe translog had 14 x variables with 3 z variables. He values in the instruction file are self-explanatory. The log-likelihood function for the model was calculated to -17.988 while the value for the OLS fit is -38.999. This is less than the value in the full frontier model. Therefore, the generalized likelihood-ratio to test the absence of a technical inefficiency effect in the frontier may be calculated as followsLR = -2{-38.999- (-17.988) }= 42.02This is the LR test of a one-sided error.

Given that this value exceeds 10.371, it is significant. For the degrees of freedom that equal five, this is the critical value, therefore, we reject the null hypothesis. The ML estimate for Y is 0.9999999 given an estimated 0.0035 standard error. The results in the Frontier are consistent with the fact that the y-value is greater than zero. Despite this, they estimate is not different from one (significantly). Therefore, the y-estimate may not be significantly different from one.


Puig-Junoy Jaume, Ortun Vicente, “Cost Efficiency in Primary Care Contracting: A Stochastic Frontier Cost Function Approach,” University of Pompeu Fabra Sept 2013 Web. July 2002
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