Prospective national and regional environmental performance: Boundary estimations using a combined data envelopment – stochastic frontier analysis approach

Energy ◽  
2010 ◽  
Vol 35 (9) ◽  
pp. 3657-3665 ◽  
Author(s):  
Alexander Vaninsky
2017 ◽  
Vol 23 (6) ◽  
pp. 787-795 ◽  
Author(s):  
Joanicjusz NAZARKO ◽  
Ewa CHODAKOWSKA

The primary problems pertaining to productivity or – more precisely – efficiency are: how to define it and how to measure it. This article studies technical efficiency in Stochastic Frontier Analysis (SFA) – the input-oriented frontier model – in the construction industry and compares it with Data Envelopment Analysis (DEA) results. The models ex­plored in this paper were constructed on the basis of two outputs and personnel cost as an input. The research sample consisted of European countries. The aim was to determine whether there are substantial differences in estimation of ef­ficiency derived from those two alternative frontier approaches. The comparison of results according to the models may translate into higher reliability of the undertaken labour efficiency analysis in construction and its conclusions. Although the results are not characterized by high compatibility, the conducted analysis indicated the most attractive countries taking into account labour cost to profit and turnover ratios of enterprises. One of the determinants which should not be ignored when analysing the labour efficiency is the level of development of a country; however, it is not the sole factor affecting the efficiency of the sector.


2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Marcus Vinicius Pereira de Souza ◽  
Madiagne Diallo ◽  
Reinaldo Castro Souza ◽  
Tara Keshar Nanda Baidya

The purpose of this study is to evaluate the efficiency indices for 60 Brazilian electricity distribution utilities. These scores are obtained by DEA (Data Envelopment Analysis) and Bayesian Stochastic Frontier Analysis models, two techniques that can reduce the information asymmetry and improve the regulator's skill to compare the performance of the utilities, a fundamental aspect in incentive regulation schemes. In addition, this paper also addresses the problem of identifying outliers and influential observations in deterministic nonparametric DEA models.


2015 ◽  
Vol 4 (2) ◽  
pp. 51-56
Author(s):  
Orsolya Tóth ◽  
István Takács

Abstract It has long been the subject of empirical researches to examine the technical efficiency on farm (micro) level. Two main methods are most often used in the empirical literature: the non-parametric Data Envelopment Analysis (DEA) based on linear programming, and the Stochastic Frontier Analysis (SFA) introduced by Aigner, Lovell and Schmidt (1977). The present study aimed to investigate the technical efficiency of farms involved in agricultural activities in Hungary using the DEA-method and the data from the Hungarian FADN database. The technical efficiency was examined based on legal forms, farm size categories and the type of farming between 2001 and 2013.


2021 ◽  
Vol 9 (3) ◽  
pp. 41
Author(s):  
Tin H. Ho ◽  
Dat T. Nguyen ◽  
Thanh Ngo ◽  
Tu D. Q. Le

This study explains the differences and variances in the efficiency scores of the Vietnamese banking sector retrieved from 27 studies published in refereed academic journals under the framework of meta-regression analysis. These scores are mainly based on frontier efficiency measurements, which essentially are Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) for Vietnamese banks over the period of 2007–2019. The meta-regression is estimated by using truncated regression to obtain bias-corrected scores. Our findings suggest that only the year of publication is positively correlated with efficiency, whilst the opposite is true for the data type, and sample size.


2020 ◽  
Vol 47 (7) ◽  
pp. 1787-1810
Author(s):  
Kekoura Sakouvogui

PurposeThe consistency of stochastic frontier analysis (SFA) and data envelopment analysis (DEA) cost efficiency measures using a sample of 650 commercial and domestic banks in the United States is investigated based on cluster analysis while accounting for the yearly variation in banks.Design/methodology/approachDue to the importance of efficiency measures for policy and managerial decision-making, the cost efficiency measures of SFA and DEA estimators are examined according to four criteria: levels, rankings, stability over time and stability over clustering groups. In this paper, we present two clustering methods, Gap Statistic and Dindex, that involve SFA and DEA cost efficiency measures. The clustering approach creates homogeneous groups of banks offering a similar mix of efficiency levels. Hence, each evaluated bank knows the cluster to which it belongs. Furthermore, this paper provides nonparametric statistical tests of SFA and DEA cost efficiency measures estimated with and without a clustering approach.FindingsThe results suggest that the clustering approach plays a considerable role in the rankings of US banks. Furthermore, the average SFA and DEA cost efficiency measures over time of the homogeneous US banks are substantially higher than those of the heterogeneous US banks.Originality/valueThis research is the first to provide comparative efficiency measures needed for desirable policy conclusions of heterogeneous and homogeneous US banks.


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