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overall, a stochastic frontier model has two components.
There is no stochastic frontier parameter in the model, and the stochastic frontier model becomes an average production model.
Hence, this study has adopted a parametric approach, specifically stochastic frontier analysis (SFA).
Using the parameters estimated from the stochastic frontier production model, it's capable to decompose TFP growth.
As the analyzing method, the stochastic frontier is used as well as the metafrontier.
The results relevant to the estimated coefficients of stochastic frontier functions are reported in Table 2.
Parametric methods include thick frontier approach (TFA), stochastic frontier approach (SFA), and distribution free approach (DFA).
Stochastic frontier analysis (SFA) is the most commonly used regression based approach to efficiency measurement.
These functions, which are estimated to determine efficiency, are also known as stochastic frontier approach (SFA).
Then, we identify more efficient farmers by means of a stochastic frontier approach.
Subsequently, an alternative methodology, a stochastic frontier production function approach, is considered.
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