Data Envelopment Analysis Journal > Vol 5 > Issue 2

Stochastic DEA Models: Estimating Production Frontiers with Composed Error Models

Samah Jradi, Paris School of Business, France, , John Ruggiero, University of Dayton, USA,
Suggested Citation
Samah Jradi and John Ruggiero (2021), "Stochastic DEA Models: Estimating Production Frontiers with Composed Error Models", Data Envelopment Analysis Journal: Vol. 5: No. 2, pp 395-411.

Publication Date: 17 Aug 2021
© 2021 S. Jradi and J. Ruggiero
Econometric models
Stochastic data envelopment analysiseconometric modelsoptimal quantileKolmogorov Smirnov test


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In this article:
1 Introduction 
2 Stochastic DEA 
3 Normal/Half-Normal and Normal Exponential Stochastic DEA 
4 Application to Hildreth Data 
5 Conclusions 


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.