This study verifies the primal and dual approaches in presence of stochastic errors in output and input demands, and policy implications when such errors are not taken into account. A data set based on a representative agent behavior is created through Monte Carlo simulation and used to estimate econometrically the primal and dual functions associated to the technology chosen. Results show that both formulations are unbiased, consistent, and efficient. No consideration of these errors can lead to wrong policy recommendations in a productive sector. Any kind of policy created to improve the total production of a particular sector has to consider the issues so far discussed to avoid such bias problems and inefficiency before be applied to the real world.


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