Estimation of Efficiency with the Stochastic Frontier Cost Function and Heteroscedasticity: A Monte Carlo Study
The objective of this article is to address heteroscedasticity in the stochastic frontier cost function using aggregated data and verify it using a Monte Carlo study. We find that when the translog form of a stochastic frontier cost function with aggregated data is estimated, all explanatory variables can inversely affect the variation of error terms. Our Monte Carlo study shows that heteroscedasticity is only significant in the random effect and the unexplained error term not in the inefficiency error term. Also, it does not cause biases, which is quite opposite of previous research. These are because our model is approximately defined by first order Taylor series around zero inefficiency area. But, disregarding heteroscedasticity causes the average inefficiency to be overestimated when the variation of inefficiency term dominates the other error terms.
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