Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints

By Timo Kuosmanen, Mika Kortelainen. 2012

348 citations recorded by the source corpus.

The field of productive efficiency analysis is currently divided between two main paradigms: the deterministic, nonparametric Data Envelopment Analysis (DEA) and the parametric Stochastic Frontier Analysis (SFA). This paper examines an encompassing semiparametric frontier model that combines the DEA-type nonparametric frontier, which satisfies monotonicity and concavity, with the SFA-style stochastic homoskedastic composite error term. To estimate this model, a new two-stage method is proposed, referred to as Stochastic Non-smooth Envelopment of Data (StoNED). The first stage of the StoNED method applies convex nonparametric least squares (CNLS) to estimate the shape of the frontier without any assumptions about its functional form or smoothness. In the second stage, the conditional expectations of inefficiency are estimated based on the CNLS residuals, using the method of moments or pseudolikelihood techniques. Although in a cross-sectional setting distinguishing inefficiency from noise in general requires distributional assumptions, we also show how these can be relaxed in our approach if panel data are available. Performance of the StoNED method is examined using Monte Carlo simulations.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/2f3b8e254d22d88d2995e8723eef71ae390cc218

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Citations in the corpus

What is the best practice for benchmark regulation of electricity distribution? Comparison of DEA, SFA and StoNED methods (2013, methodology): “In fact, both DEA and SFA can be obtained as constrained special cases of the more general StoNED-model (Kuosmanen and Johnson, 2010; Kuosmanen and Kortelainen, 2012).”
On estimating shadow prices of undesirable outputs with efficiency models: A literature review (2014, methodology): “Mekaroonreung and Johnson [51] applied the convex nonparametric least squares (CNLS) [60,61] and the stochastic nonparametric envelopment of data (StoNED) method [62], to compute the shadow prices of SO2 and NOx for U.”
One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient, root-n consistent StoNEZD method (2011, methodology): “Our estimator builds on the nonparametric least-squares interpretation of DEA developed in Kuosmanen and Johnson (2010) and the StoNED method of Kuosmanen and Kortelainen (2011) that combines the axiomatic, nonparametric DEA-style frontier with the probabilistic, SFAstyle treatment of inefficiency”