The stochastic distance function model is extended to allow for the inefficiency component of the error term to be autocorrelated, as implied by a dynamic model of firm behavior. The autocorrelation parameter can then be interpreted as a measure of the persistence of inefficiency. The model is viewed from a state-space perspective, and Kalman filtering techniques are proposed for estimation. The model is applied to two panels of dairy farms from Germany and the Netherlands. The results suggest a very high degree of persistence of inefficiency through time.

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Stochastic frontier models with autocorrelated inefficiency have been proposed in the past as a way of addressing the issue of temporal variation in firm-level efficiency scores. They are justified using an underlying model of dynamic firm behavior. In this paper we argue that these models could have radically different implications for the expected long-run efficiency scores in the presence of unobserved heterogeneity. The possibility of accounting for unobserved heterogeneity is explored. Random-and correlated random-effects dynamic stochastic frontier models are proposed and applied to a panel of US electric utilities.

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