Stochastic DEA Models: Estimating Production Frontiers with Composed Error Models

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. http://dx.doi.org/10.1561/103.00000042
Publication Date: 17 Aug 2021
© 2021 S. Jradi and J. Ruggiero
 
Subjects
Econometric models
 
Keywords
Stochastic data envelopment analysis, econometric models, optimal quantile, Kolmogorov Smirnov test
 

JOURNALS

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

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.

DOI:10.1561/103.00000042

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