Variables In Data Envelopment Analysis Research Articles

Using machine learning to select variables in data envelopment analysis: Simulations and application using electricity distribution data

Agencies that regulate electricity providers often apply nonparametric data envelopment analysis (DEA) to assess the relative efficiency of each firm. The reliability and validity of DEA are contingent upon selecting relevant input variables. In the era of big (wide) data, the assumptions of traditional variable selection techniques are often violated due to challenges related to high-dimensional data and their standard empirical properties. Currently, regulators have access to a large number of potential input variables. Therefore, our aim is to introduce new machine learning methods for regulators of the energy market. We also propose a new two-step analytical approach where, in the first step, the machine learning-based adaptive least absolute shrinkage and selection operator (ALASSO) is used to select variables and, in the second step, selected variables are used in a DEA model. In contrast to previous research, we find, by using a more realistic data-generating process common for production functions (i.e., Cobb–Douglas and Translog), that the performance of different machine learning techniques differs substantially in different empirically relevant situations. Simulations also reveal that the ALASSO is superior to other machine learning and regression-based methods when the collinearity is low or moderate. However, in situations of multicollinearity, the LASSO approach exhibits the best performance. We also use real data from the Swedish electricity distribution market to illustrate the empirical relevance of selecting the most appropriate variable selection method.

Using Machine Learning to Select Variables in Data Envelopment Analysis: Simulations and Application Using Electricity Distribution Data

Using Machine Learning to Select Variables in Data Envelopment Analysis: Simulations and Application Using Electricity Distribution Data

Ranking of Banks’ Risk Reporting Using Data Envelopment Analysis

The present study aimed to rank banks in terms of board of directors report and notifying the users of reports. In addition, we evaluated factors affecting risk disclosure from the perspective of corporate governance. Moreover, we assessed risk disclosure based on linguistic analysis of board report text and capital adequacy ratio. Words were applied as measurement units to measure risk disclosure. The advantage of this technique is the unique analysis of words. According to the theoretical foundations presented in the present study, we first identified risk disclosure words in reports provided to financial information users and divided them into two categories of positive and negative risk disclosure words. Another variable selected for risk disclosure was capital adequacy ratio. Effective variables in corporate governance system in banks included the board independence, duality of CEO duties, and major shareholders as input variables in data envelopment analysis (DEA) model. On the other hand, the BCC model of DEA was selected as output-based nature. The statistical population included all banks listed in Tehran Stock Exchange. In total, 20 year-bank units listed during 2016-2017 were assessed. In the end, seven year-bank units were considered efficient while the rest were inefficient. Moreover, we estimated the amount of shortage in outputs to reach inefficient banks to the desired level of efficiency.

Selection of Variables in Data Envelopment Analysis for Evaluation of Stock Performance

This study deploys data envelopment analysis (DEA) to identify the appropriate variables for the performance valuation of stocks. For this purpose, sixty-nine non-financial stocks of the Nifty 100 index of The National Stock Exchange of India Ltd (NSE) were selected as a sample for this study. We segregated the selected stocks into three groups of inputs and outputs for DEA based on fundamental indicators (financial ratios); technical indicators (momentum indicators); and both, fundamental and technical indicators. The stock performance indicators are sourced from the ACE database from financial year 2014 to 2019. The results of the study suggest that all three sets of stock performance indicators help in the identification of efficient stocks. However, stocks identified under momentum indicators are seen to have been better performing in stock return compared to the other two groups. The outcome of this study may help academicians and investors construct an effective portfolio and analyse/study its performance evaluation

A new stepwise method for selection of input and output variables in data envelopment analysis

Data envelopment analysis (DEA) is one of the widely accepted optimization technique uses to measure the relative efficiency of organizational units where multiple inputs and outputs are present. The significance of DEA results depends on the variables selected for DEA modelling. One of the main challenges in data envelopment analysis modelling is of identify the significant input and output variables for DEA modelling. In this study, we propose an enhanced stepwise method to identify the significant and insignificant input and output variable by reducing the iterations process in stepwise method. The statistical significance of the input and output variables evaluated using the statistical methods: Least significance difference (LSD), and Welch’s statistics. The proposed method applied to the Indian banking sector and the results have shown that the proposed model significantly identified the significant and insignificant input and output variables with least loss of information.

A new stepwise method for selection of input and output variables in data envelopment analysis

Data envelopment analysis (DEA) is one of the widely accepted optimization technique uses to measure the relative efficiency of organizational units where multiple inputs and outputs are present. The significance of DEA results depends on the variables selected for DEA modelling. One of the main challenges in data envelopment analysis modelling is of identify the significant input and output variables for DEA modelling. In this study, we propose an enhanced stepwise method to identify the significant and insignificant input and output variable by reducing the iterations process in stepwise method. The statistical significance of the input and output variables evaluated using the statistical methods: Least significance difference (LSD), and Welch’s statistics. The proposed method applied to the Indian banking sector and the results have shown that the proposed model significantly identified the significant and insignificant input and output variables with least loss of information.

SPECIFYING WEIGHT RESTRICTION LIMITS IN DATA ENVELOPMENT ANALYSIS WITH THE WONG AND BEASLEY AND CONE RATIO METHODS

ABSTRACT This study presents a new approach for the definition of weight restrictions in Data Envelopment Analysis (DEA) for the one output, multiple inputs case, using the results of a Linear Regression model (LRM) developed with the same DEA variables. Thus, the limits of Wong-Beasley and Cone Ratio methods are chosen without interference from a decision maker, with DEA weight search intervals defined from the estimated standardized coefficients of a linear regression (which represent the statistical importance of the inputs for the definition of the DEA efficiency scores). As an example, weight restrictions for a DEA model (Constant Returns to Scale (CRS)) were obtained through the unrestricted, Wong-Beasley and Cone Ratio methods applied to a dataset consisting of hospital admissions (output), number of beds and number of health professionals (inputs) in the year 2016; and rankings were compared by a Spearman correlation procedure. The regression model had R 2 = 0.89 with coefficients 0.43 (professionals) and 0.54 (beds); and the Spearman correlation among rankings was at least RS2 = 0.84. In conclusion, rankings were consistent and interpretable, and the approach circumvents the need for a subjective intervention by a decision maker when defining weight restrictions in DEA.

Unobservable or omitted production variables in data envelopment analysis through unit-specific production trade-offs

The paper focuses upon situations in which decision-making units carry out their production activities with some inputs or outputs unobservable (or possibly omitted), and when there are a priori known constraints on the relative significance of otherwise observable (or explicitly considered) inputs and outputs. For such settings, the paper proposes a modification that alters traditional construction of the production possibility set and isolates the role of the unobservable (or omitted) variables in production by means of restrictions on virtual inputs and outputs being converted into production trade-offs. In effect, the proposed procedure induces unit-specific production possibility sets that derive from production trade-offs framed for units assessed separately to reflect their specific observed production conditions. The modification is implemented within a weighted slacks-based measure with restricted direction of slacks in order to make technical efficiency measurement more informative and consistent with the operating conditions under which production activities are accomplished. These ideas are illustrated and models implemented in a case study of bank branch performance measurement.

A multiobjective approach for non-discretionary variables in data envelopment analysis

Data Envelopment Analysis is a non-parametrical approach for efficiency evaluation of so-called DMUs (Decision Making Units) and takes into account multiple inputs and outputs. For each inefficient DMU, a target is provided which is constituted by the inputs or outputs levels that are to be attained for the inefficient DMU to become efficient. However, multiobjective models, known as MORO (Multiobjective Model for Ratio Optimization) provide a set of targets for inefficient DMU, which provides alternatives among which the decision-maker can choose. In this paper, we proposed an extension of the MORO models to take into account non-discretionary variables, i.e., variables that cannot be controlled. We present a numerical example to illustrate the proposed multiobjective model. We also discuss the characteristics of this model, as well as the advantages of offering a set of targets for the inefficient DMUs when there are non-discretionary variables in the data set.

DEA Window 분석을 이용한 국내 온라인 게임 기업의 운영 효율성 평가

This paper measures the operational efficiency of domestic onli ne game companies and analyze its trends and pat-terns by using data envelopment analysis (DEA). DEA is a non-pa rametric approach to measuring the relative efficiency of decision-making units (DMUs) with multiple inputs and output s. 14 online game companies are selected as DMUs and three inputs (number of employees, capital and asset) and t hree outputs (sales, operating profit and net profit) are selected as DEA variables. First, the output-oriented BCC m odel and super-efficiency model are employed to meas-ure the static operational efficiency of the online game compan ies from 2003 to 2012. We also conduct the dynamic analysis with DEA window model to capture the trends of their o perational efficiency influenced by internal and external environmental changes. The results are expected to provide frui tful implications for strategic decision making of online game companies and policy making for the online game industry.

Chance-constrained DEA models with random fuzzy inputs and outputs

Data Envelopment Analysis (DEA) is a widely used mathematical programming technique for comparing the inputs and outputs of a set of homogenous Decision Making Units (DMUs) by evaluating their relative efficiency. The conventional DEA methods assume deterministic and precise values for the input and output observations. However, the observed values of the input and output data in real-world problems can potentially be both random and fuzzy in nature. We introduce Random Fuzzy (Ra-Fu) variables in DEA where randomness and vagueness coexist in the same problem. In this paper, we propose three DEA models for measuring the radial efficiency of DMUs when the input and output data are Ra-Fu variables with Poisson, uniform and normal distributions. We then extend the formulation of the possibility–probability and the necessity–probability DEA models with Ra-Fu parameters for a production possibility set where the Ra-Fu inputs and outputs have normal distributions with fuzzy means and variances. We finally propose the general possibility–probability and necessity–probability DEA models with fuzzy thresholds. A set of numerical examples and a case study are presented to demonstrate the efficacy of the procedures and algorithms.

Impact of Environmental Factors on Efficiency of Rice Production in Bangladesh

This study uses environment variables in Data Envelopment Analysis (DEA) and examines the influence of environmental factors on efficiency of rice production in Bangladesh.Data was collected from Bangladesh Bureau of Statistics (BBS) and Bangladesh Meteorology Department Efficiency with and without environmental factors of three type of rice production (AUS, AMAN and BORO) are measured using data envelopment analysis.Efficiency of rice production which measured without environmental factors is explained by the environmental factors (Rainfall, Temperature and Humidity) using Tobit regression to weigh up the impact of these factors on rice production.Efficiency of BORO production with environmental factors is almost similar to the efficiency of production without environmental factors. Production efficiency of AUS and AMAN employing environmental factors is higher than that of without environmental factors. Humidity has a positive and significant effect on all types of rice production.Temperature has a negative impact on production efficiency, implying that global warming could be a cause for a decrease in efficiency of rice production.Rainfall has a positive impact only on BORO production.

Data envelopment analysis in the presence of measurement error: case study from the National Database of Nursing Quality Indicators® (NDNQI®)

Data envelopment analysis (DEA) is the most commonly used approach for evaluating healthcare efficiency [B. Hollingsworth, The measurement of efficiency and productivity of health care delivery. Health Economics 17(10) (2008), pp. 1107–1128], but a long-standing concern is that DEA assumes that data are measured without error. This is quite unlikely, and DEA and other efficiency analysis techniques may yield biased efficiency estimates if it is not realized [B.J. Gajewski, R. Lee, M. Bott, U. Piamjariyakul, and R.L. Taunton, On estimating the distribution of data envelopment analysis efficiency scores: an application to nursing homes’ care planning process. Journal of Applied Statistics 36(9) (2009), pp. 933–944; J. Ruggiero, Data envelopment analysis with stochastic data. Journal of the Operational Research Society 55 (2004), pp. 1008–1012]. We propose to address measurement error systematically using a Bayesian method (Bayesian DEA). We will apply Bayesian DEA to data from the National Database of Nursing Quality Indicators® to estimate nursing units’ efficiency. Several external reliability studies inform the posterior distribution of the measurement error on the DEA variables. We will discuss the case of generalizing the approach to situations where an external reliability study is not feasible.

Enhancement of equity portfolio performance using data envelopment analysis

This paper examines the applicability of data envelopment analysis (DEA) as a basis of selection criteria for equity portfolios. It is the first DEA application for constructing a combined equity investment strategy that aims to integrate the benefits of both value investing and momentum investing. The 3-quantile portfolios are composed of a comprehensive sample of Finnish non-financial stocks based on their DEA efficiency scores that are calculated using three variants of DEA models (the constant returns-to-scale, the super-efficiency, and the cross-efficiency models). The performance of portfolios is evaluated on the basis of the average return and several risk-adjusted performance metrics throughout the 1994–2010 sample period.The results show the capability of the DEA approach to add value to equity portfolio selection. The outperformance of the top 3-quantile DEA portfolios in contrast to both the comparable bottom portfolio and the stock market average is statistically significant on the basis of all performance measures employed. The outperformance is slightly more significant when the stock price momentum is included in the DEA variables. The methodology employed offers an interesting alternative for detecting the outperforming stocks of the future by capturing both the price momentum and several dimensions of relative value simultaneously. DEA is particularly useful as a multicriteria methodology in cases in which the number of stocks in the sample is large. It therefore also has useful implications to practical portfolio management.

Container selection in the presence of partial dual‐role factors

PurposeThe purpose of this paper is to introduce an innovative data envelopment analysis (DEA) model entitled “Non‐binary Arithmetic Operator Dual‐role” (NAOD) under free disposability assumption for selecting the refrigerated containers in cold chain management (CCM).Design/methodology/approachIn classical DEA models, it is assumed that all of input and output variables play a certain role. However, in some cases, some variables play both input and output roles, simultaneously. These variables in DEA are called “dual‐role” factors. This study introduces a new approach to deal with these factors in the process of evaluating a set of homogeneous refrigerated containers, which is based on Free Disposal Hull (FDH) (one of the DEA models). However, in previous dual‐role models, this variable is considered as a binary variable. In this paper, a partial role for dual‐role factors is considered.FindingsThe main findings of this paper are: for the first time, the NAOD model is developed for the container selection problem in the context of CCM and the dual‐role factors are considered for the container selection problem. The NAOD model determines partial role of dual‐role factors and can consider multiple dual‐role factors. For the first time, the container selection problem is solved by an FDH model. The result of NAOD model is validated by genetic algorithm.Originality/valueThe paper makes a sufficient contribution to the practice of operations research. It is the first study which applies advanced DEA models for selecting the containers in the context of the CCM. To the best of the authors' knowledge, there is no other reference that deals with container selection in the context of CCM in the presence of dual‐role factors.

Categorical and continuous non-discretionary variables in data envelopment analysis: a comparison of two single-stage models

Non-discretionary or environmental variables are regarded as important in the evaluation of efficiency in Data Envelopment Analysis (DEA), but there is no consensus on the correct treatment of these variables. This paper compares the performance of the standard BCC model as a base case with two single-stage models: the Banker and Morey (1986a) model, which incorporates continuous environmental variables and the Banker and Morey (1986b) model, which incorporates categorical environmental variables. Simulation analyses are conducted using a shifted Cobb-Douglas function, with one output, one non-discretionary input, and two discretionary inputs. The production function is constructed to separate environmental impact from managerial inefficiency, while providing measures of both for comparative purposes. Tests are performed to evaluate the accuracy of each model. The distribution of the inputs, the sample size and the number of categories for the categorical model are varied in the simulations to determine their impact on the performance of each model. The results show that the Banker and Morey models should be used in preference to the standard BCC model when the environmental impact is moderate to high. Both the continuous and categorical models perform equally well but the latter may be better suited to some applications with larger sample sizes. Even when the environmental impact is slight, the use of a simple two-way split of the sample data can produce significantly better results under the Categorical model in comparison to the BCC model.

Integrating dual-role variables in data envelopment analysis

Integrating dual-role variables in data envelopment analysis

Integrating categorical variables in Data Envelopment Analysis models: A simple solution technique

Abstract This paper introduces a novel method to incorporate categorical non-discretionary variables in Data Envelopment Analysis (DEA) models. While solutions to this problem have been introduced before, they have rarely been employed in applied work. We surmise that existing solution concepts pose problems for applied researchers and develop a simple and straightforward alternative based on indicator variables. We thereby provide a flexible tool for models with categorical variables that–unlike the approaches mentioned above–can be solved with standard DEA software irrespective of scale assumptions even if no option for non-discretionary variables is available. Furthermore, there is no need to split the data and run multiple DEA, one for each data set generated. The model is extensible to categorical discretionary variables and in addition to non-hierarchical data.

Avaliação sistêmica da introdução de tecnologias na pecuária de gado de corte do Pantanal por meio de modelos de análise envoltória de dados (DEA)

Este trabalho foi realizado com o objetivo de avaliar, por meio de diferentes modelos de Análise Envoltória de Dados (DEA), a eficiência da introdução e adaptação de tecnologias no sistema de produção de cria extensiva do Pantanal. Utilizaram-se dados econômicos registrados durante oito anos (1995 a 2002). Duas metodologias de escolha das variáveis foram utilizadas para a realização da análise DEA: a primeira é uma variante do método multicritério de seleção de variáveis, que combina os objetivos conflitantes de melhor ordenação das unidades de decisão e maior eficiência média, e a segunda inclui técnicas de estatística multivariada. Nos dois modelos resultantes (com seis inputs e um output; e com três inputs e um output, respectivamente), foram analisados os resultados de eficiência em relação às fronteiras DEA clássica e invertida, além do valor do índice composto que agrega ambos os resultados. O melhor modelo, avaliado pela análise de tendência linear, foi o obtido com a variante proposta do método multicritério de seleção de variáveis, cujo índice composto resultou em estimativas significativas dos coeficientes de regressão (0,0455) e de determinação (0,70).

Efficiency and regulation in Spanish hypermarket retail trade

PurposeThis study aims to present an approach for analyzing hypermarkets efficiency data envelopment analysis (DEA) in Spanish retailing. In particular, the influence of the Retail Trade Act of 1996, by means of which the Spanish state transferred authority to concede licenses for opening commercial establishments to the regions, is to be studied.Design/methodology/approachThe analysis is based on a DEA model that allows for the evaluation of categorical variables in DEA in cross‐section data.FindingsThe findings suggest the existence of three different production frontiers in relation to the markets' regulation process where the hypermarkets operate; high, medium and low regulation. In the second place, the effect of the regulatory restrictions carried out by the autonomous communities is corrected in the second stage. This correction (once managerial inefficiencies have been eliminated) allows the hypermarkets operative in areas with low restrictions to be more efficient than those located in areas of greater regulation.Research limitations/implicationsThe generalization of the conclusions of the study to the whole sector should be made with caution, because only one of the players in the distribution channel has been analyzed.Practical implicationsThe implications that can be derived from the analysis carried out could be of great interest to policy makers in Spain with regard to the influence of the Retail Trade Act of 1996.Originality/valueThe paper uses the model mentioned as evaluation of the efficiency in relation to the environment or regulation carried out in the sector. The methodology proposed to analyze the impact of this environment variable is one of the main contributions of the work.