Stochastic DEA Models: Estimating Production Frontiers with Composed Error Models

Samah Jradi and John Ruggiero

Data Envelopment Analysis Journal, 2021, vol. 5, issue 2, 395-411

Abstract: 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.

Keywords: Stochastic data envelopment analysis; econometric models; optimal quantile; Kolmogorov Smirnov test (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:now:jnldea:103.00000042

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