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Stochastic DEA Models: Estimating Production Frontiers with Composed Error Models

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  • Samah Jradi
  • John Ruggiero

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.

Suggested Citation

  • Samah Jradi & John Ruggiero, 2021. "Stochastic DEA Models: Estimating Production Frontiers with Composed Error Models," Data Envelopment Analysis Journal, now publishers, vol. 5(2), pages 395-411, August.
    • Handle: RePEc:now:jnldea:103.00000042
    DOI: 10.1561/103.00000042
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