Stochastic DEA Models: Estimating Production Frontiers with Composed Error Models
In this article:
2 Stochastic DEA
3 Normal/Half-Normal and Normal Exponential Stochastic DEA
4 Application to Hildreth Data
In this paper we discuss the Stochastic DEA (SDEA) model introduced in Banker (1988). The linear programming model can be considered a nonparametric quantile regression model where the user chooses a priori the percentage of points below the frontier. Rather than imposing a functional form for production, the SDEA model incorporates the celebrated Afriat conditions to enforce a convex production possibilities set. Recent work on the stochastic frontier models shows how additional assumptions can be placed on the SDEA model to allow a composed error model within the SDEA framework. In this paper, we illustrate these models using a simulated data set. We also apply our SDEA models to the Hildreth (1954) data on corn production.