Inference in stochastic frontier analysis with dependent error terms
نویسندگان
چکیده
Stochastic frontier analysis (SFA) is often used to estimate technical efficiency of entities such as firms, countries or municipalities. A potential dependence between the two components of the error term can be taken into account by a copula function. While estimation of the model is straightforward using the Corrected Ordinary Least Squares (COLS) and Maximum Likelihood (ML) methods, an open issue concerns the inference of the technical efficiencies. We propose a parametric bootstrap algorithm which is an extension of an algorithm proposed by Simar and Wilson [18] to the dependence case. This allows us to estimate the efficiency percentile confidence intervals. We apply the model to the estimation of technical efficiencies of moroccan municipalities.
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ورودعنوان ژورنال:
- Mathematics and Computers in Simulation
دوره 102 شماره
صفحات -
تاریخ انتشار 2014