Stochastic Nonparametric Approach to Efficiency Analysis: A Unified Framework

Kuosmanen, Timo; Johnson, Andrew L.; Saastamoinen, A.

doi:10.1007/978-1-4899-7553-9_7

Cited by 69 publications

(101 citation statements)

References 82 publications

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“…The next step was the evaluation of economic efficiency of the companies to be studied. To meet said objective we employed the DEA method, which is the most frequent and popular technique used in the literature [47,48]. Proposed by Charnes, Cooper and Rhodes in 1978, this methodology has become widespread and applied to different scenarios and areas, which is an indication of its flexibility [49].…”

Section: Methodsmentioning

confidence: 99%

“…With this it was hoped to determine the organizational variables associated with higher levels of efficiency. For this procedure, common in studies using the DEA method [47], the use of regression models is frequent [48]. However, in this study a fuzzy set QCA (fsQCA) was undertaken, which we consider adequate and useful for testing jointly technological and organizational variables which may explain greater levels of efficiency.…”

Section: Methodsmentioning

confidence: 99%

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Determining Factors for Economic Efficiency in the Organic Olive Oil Sector

et al. 2017

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Abstract:Spain looms large worldwide in organic olive oil production. However, this productive potential contrasts with the low internal consumption of the product. This situation makes Spain a world leader in its export. Companies in this sector have clear deficiencies, which must be corrected to ensure their survival over time. In this context, the aim of this study is to analyse the level of efficiency, in economic terms, of organic olive oil producers and to identify the factors explaining the best organizational practices. To do so, Data Envelopment Analysis (DEA) and Qualitative Comparative Analysis (QCA) have been used. The results reveal low levels of economic efficiency and the variables determining said efficiency.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Determining Factors for Economic Efficiency in the Organic Olive Oil Sector

et al. 2017

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“…The recent contributions by Hall and Simar (2002) and Simar (2007), summarized above, allow for a fully nonparametric estimation of κ. Kuosmanen et al (2014) point out that the CNLS estimated residuals ε CNLS j , ∀ j are consistent estimators of ε + j = ε CNLS j − E(u). Using the residuals from a CNLS regression to get an estimator of the density for ε + denoted f ε + (x), the authors argue that a robust nonparametric estimator of the expected inefficiency E(u) is…”

Section: Recent Contributions For Disentangling Noise and Inefficiencmentioning

confidence: 98%

“…However, some collections of GAMS-code and Matlab-code are currently available. For recent introductions with explicit links to such collections in relation to CNLS and Stoned, see Kuosmanen, Johnson, and Saastamoinen (2015) and Johnson and Kuosmanen (2015). Online support material to Kuosmanen, Saastamoinen, and Sipiläinen (2013) includes a Gams setup of the reported simulation.…”

Section: Introductionmentioning

confidence: 98%

Stochastic Data Envelopment Analysis—A review

Olesen

Petersen

2016

European Journal of Operational Research

189

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“…Further, the conditional version is slower by a factor n −4/4+r with r being the number of z-variables (Kneip et al, 1998;Jeong et al, 2010). For StoNEZD, Johnson and Kuosmanen (2011) show that δ converges with the standard parametric rate, n −1/2 , while results for the non-parametric CNLS estimation remains open (Kuosmanen et al, 2015). However, following Stone (1980Stone ( , 1982, Johnson and Kuosmanen (2011) suggest an upper limit n −2d/(2d+m) with d being the degree of differentiability and m as the number of inputs.…”

Section: Comparison Of the Estimatorsmentioning

confidence: 99%

Operational Conditions in Regulatory Benchmarking Models: A Monte Carlo Analysis

Nieswand

Seifert

2016

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract Benchmarking methods are widely used in the regulation of firms in network industries working under heterogeneous exogenous environments. In this paper we compare three recently developed estimators, namely conditional DEA Simar, 2005, 2007b), latent class SFA (Orea and Kumbhakar, 2004;Greene, 2005), and the StoNEZD approach (Johnson and Kuosmanen, 2011) by means of Monte Carlo simulation focusing on their ability to identify production frontiers in the presence of environmental factors. Data generation replicates regulatory data from the energy sector in terms of sample size, sample dispersion and distribution, and correlations of variables. Although results show strengths of each of the three estimators in particular settings, latent class SFA perform best in nearly all simulations. Further, results indicate that the accuracy of the estimators is less sensitive against different distributions of environmental factors, their correlations with inputs, and their impact on the production process, but performance of all approaches deteriorates with increasing noise. For regulators this study provides orientation to adopt new benchmarking methods given industry characteristics. JEL-Codes: L50, Q50, C63 Terms of use: Documents in

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Stochastic Nonparametric Approach to Efficiency Analysis: A Unified Framework

Cited by 69 publications

References 82 publications

Determining Factors for Economic Efficiency in the Organic Olive Oil Sector

Determining Factors for Economic Efficiency in the Organic Olive Oil Sector

Stochastic Data Envelopment Analysis—A review

Operational Conditions in Regulatory Benchmarking Models: A Monte Carlo Analysis

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