Standard parametric models for efficiency and total factor productivity growth measurement either impose strict structures on the time-evolution of efficiency scores or no structure at all. When the data capture a sector in turbulent periods both specifications may be inappropriate. The dynamic stochastic frontier model takes a middle way in terms of the time-structure it imposes on efficiency scores. We apply the dynamic stochastic frontier model to the case of German dairy farms in a period that is characterized by high milk price volatility. The model is able to capture time-specific efficiency and total factor productivity growth shocks that may have been induced by this high volatility. Furthermore, the dynamic stochastic frontier model is favored by the data when compared to a model that imposes a very restrictive time structure on efficiency and two models that do not impose any time structure at all.
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Skevas et al. (2018) studied this question.
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