How to improve the performances of DEA/FDH estimators in the presence of noise?

In frontier analysis, most nonparametric approaches (DEA, FDH) are based on envelopment ideas which assume that with probability one, all observed units belong to the attainable set. In these “deterministic” frontier models, statistical inference is now possible, by using bootstrap procedures. In the presence of noise, envelopment estimators could behave dramatically since they are very sensitive to extreme observations that might result only from noise. DEA/FDH techniques would provide estimators with an error of the order of the standard deviation of the noise. This paper adapts some recent results on detecting change points [Hall P, Simar L (2002) J Am Stat Assoc 97:523–534] to improve the performances of the classical DEA/FDH estimators in the presence of noise. We show by simulated examples that the procedure works well, and better than the standard DEA/FDH estimators, when the noise is of moderate size in term of signal to noise ratio. It turns out that the procedure is also robust to outliers. The paper can be seen as a first attempt to formalize stochastic DEA/FDH estimators.      

Inference by the <Emphasis Type="Italic">m</Emphasis> out of <Emphasis Type="Italic">n</Emphasis> bootstrap in nonparametric frontier models

It is well-known that the naive bootstrap yields inconsistent inference in the context of data envelopment analysis (DEA) or free disposal hull (FDH) estimators in nonparametric frontier models. For inference about efficiency of a single, fixed point, drawing bootstrap pseudo-samples of size m < n provides consistent inference, although coverages are quite sensitive to the choice of subsample size m. We provide a probabilistic framework in which these methods are shown to valid for statistics comprised of functions of DEA or FDH estimators. We examine a simple, data-based rule for selecting m suggested by Politis et al. (Stat Sin 11:1105–1124, 2001), and provide Monte Carlo evidence on the size and power of our tests. Our methods (i) allow for heterogeneity in the inefficiency process, and unlike previous methods, (ii) do not require multivariate kernel smoothing, and (iii) avoid the need for solutions of intermediate linear programs.     

Estimation of cost inefficiency in panel data models with firm specific and sub-company specific effects

This paper proposes a dual-level inefficiency model for analysing datasets with a sub-company structure, which permits firm inefficiency to be decomposed into two parts: a component that varies across different sub-companies within a firm (internal inefficiency); and a persistent component that applies across all sub-companies in the same firm (external inefficiency). We adapt the models developed by Kumbhakar and Hjalmarsson (J Appl Econom 10:33–47, 1995) and Kumbhakar and Heshmati (Am J Agric Econ 77:660–674, 1995), making the same distinction between persistent and residual inefficiency, but in our case across sub-companies comprising a firm, rather than over time. The proposed model is important in a regulatory context, where datasets with a sub-company structure are commonplace, and regulators are interested in identifying and eliminating both persistent and sub-company varying inefficiency. Further, as regulators often have to work with small cross-sections, the utilisation of sub-company data can be seen as an additional means of expanding cross-sectional datasets for efficiency estimation. Using an international dataset of rail infrastructure managers we demonstrate the possibility of separating firm inefficiency into its persistent and sub-company varying components. The empirical illustration highlights the danger that failure to allow for the dual-level nature of inefficiency may cause overall firm inefficiency to be underestimated.     

Foreign presence and efficiency in transition economies

This paper presents empirical evidence on the role of foreign presence in the performance of domestic manufacturing firms in five Central and Eastern European countries. Data Envelopment Analysis (DEA) was used to estimate a frontier for each sector with similar technology common for five transition countries in the sample − Bulgaria, Estonia, Hungary, Poland and Romania. Following Simar and Wilson (J Econom 136(1):31–64, 2007), this study applies a truncated regression and bootstrap technique in a second stage post-DEA analysis. Some evidence is found to support the hypothesis that foreign presence has an overall positive spillover effects on the performance of domestic firms.     

Estimation of a panel data model with parametric temporal variation in individual effects

This paper is an extension of Ahn et al. (J. Econom. 101 (2001) 219) to allow a parametric function for time-varying coefficients of the individual effects. It provides a fixed-effect treatment of models like those proposed by Kumbhakar (J. Econom. 46 (1990) 201) and Battese and Coelli (J. Prod. Anal. 3 (1992) 153). We present a number of GMM estimators based on different sets of assumptions. Least squares has unusual properties: its consistency requires white noise errors, and given white noise errors it is less efficient than a GMM estimator. We apply this model to the measurement of the cost efficiency of Spanish savings banks.     

Statistical Inference in Nonparametric Frontier Models: The State of the Art

Efficiency scores of firms are measured by their distance to an estimated production frontier. The economic literature proposes several nonparametric frontier estimators based on the idea of enveloping the data (FDH and DEA-type estimators). Many have claimed that FDH and DEA techniques are non-statistical, as opposed to econometric approaches where particular parametric expressions are posited to model the frontier. We can now define a statistical model allowing determination of the statistical properties of the nonparametric estimators in the multi-output and multi-input case. New results provide the asymptotic sampling distribution of the FDH estimator in a multivariate setting and of the DEA estimator in the bivariate case. Sampling distributions may also be approximated by bootstrap distributions in very general situations. Consequently, statistical inference based on DEA/FDH-type estimators is now possible. These techniques allow correction for the bias of the efficiency estimators and estimation of confidence intervals for the efficiency measures. This paper summarizes the results which are now available, and provides a brief guide to the existing literature. Emphasizing the role of hypotheses and inference, we show how the results can be used or adapted for practical purposes.     

Measuring technical and allocative efficiencies for banks in the transition countries using the Fourier flexible cost function

The transition economies are known to have quite different market structures from the market economies. State-owned banks accounts for a major part of the financial sector in East European countries before the transition period. Since the input prices of the sector are frequently under the control of those governments, the misallocated resources may incur the loss of economic efficiency. This paper attempts to gauge the technical and allocative efficiency using unbalanced panel data of 340 banks from 14 transition countries under the framework of the Fourier flexible shadow cost function. Accommodating technical and allocative efficiencies simultaneously, as suggested by Atkinson and Cornwell (Int Econ Rev 35:231–243, 1994a) and Kumbhakar and Wang (J Econom 134:317–340, 2006a), avoids potential specification errors and leads to consistent parameter estimates. The average total cost savings resulting from greater technical and allocative efficiency are around 28.31 and 7.13%, respectively. Foreign-owned banks are found to be the most economically efficient. The enforcement of financial liberalization does gradually improve upon the sample banks’ technical efficiency. The allocative inefficiency arises from over capitalization and excess funds. Scale diseconomies appear to prevail in the sample states with a few exceptions.     

Matching in local labor markets: a stochastic frontier approach

This paper investigates the technical efficiency of labor market matching from a stochastic frontier approach. The true fixed-effects model (Greene J Prod Anal 23:7–32, 2005a; J Econom 126:269–303, 2005b) is utilised in order to separate cross-sectional heterogeneity from inefficiency, and inefficiency terms are modelled following Battese and Coelli (Empir Econ 20:325–332, 1995). The data set consists of almost 17,000 observations from Local Labor Offices (LLOs) in Finland. According to the results, there are notable differences in matching efficiency between regions, and these differences contribute significantly to the number of filled vacancies. If all regions were as efficient as the most efficient one, the number of total matches per month would increase by over 23%. The heterogeneity of the job-seeker stock is an important determinant of matching efficiency: the weight of the composition of the job-seeker stock in the inefficiency terms is on average 85%.

Detecting Outliers in Frontier Models: A Simple Approach

In frontier analysis, most of the nonparametric approaches (DEA, FDH) are based on envelopment ideas which suppose that with probability one, all the observed units belong to the attainable set. In these deterministic frontier models, statistical theory is now mostly available (Simar and Wilson, 2000a). In the presence of super-efficient outliers, envelopment estimators could behave dramatically since they are very sensitive to extreme observations. Some recent results from Cazals et al. (2002) on robust nonparametric frontier estimators may be used in order to detect outliers by defining a new DEA/FDH deterministic type estimator which does not envelop all the data points and so is more robust to extreme data points. In this paper, we summarize the main results of Cazals et al. (2002) and we show how this tool can be used for detecting outliers when using the classical DEA/FDH estimators or any parametric techniques. We propose a methodology implementing the tool and we illustrate through some numerical examples with simulated and real data. The method should be used in a first step, as an exploratory data analysis, before using any frontier estimation.     

Non-parametric, unconditional quantile estimation for efficiency analysis with an application to Federal Reserve check processing operations

This paper examines the technical efficiency of US Federal Reserve check processing offices over 1980–2003. We extend results from Park et al. [Park, B., Simar, L., Weiner, C., 2000. FDH efficiency scores from a stochastic point of view. Econometric Theory 16, 855–877] and Daouia and Simar [Daouia, A., Simar, L., 2007. Nonparametric efficiency analysis: a multivariate conditional quantile approach. Journal of Econometrics 140, 375–400] to develop an unconditional, hyperbolic, α-quantile estimator of efficiency. Our new estimator is fully non-parametric and robust with respect to outliers; when used to estimate distance to quantiles lying close to the full frontier, it is strongly consistent and converges at rate root-n, thus avoiding the curse of dimensionality that plagues data envelopment analysis (DEA) estimators. Our methods could be used by policymakers to compare inefficiency levels across offices or by managers of individual offices to identify peer offices.     

Two-stage DEA: <Emphasis Type="Italic">caveat emptor</Emphasis>

This paper examines the wide-spread practice where data envelopment analysis (DEA) efficiency estimates are regressed on some environmental variables in a second-stage analysis. In the literature, only two statistical models have been proposed in which second-stage regressions are well-defined and meaningful. In the model considered by Simar and Wilson (J Prod Anal 13:49–78, 2007), truncated regression provides consistent estimation in the second stage, where as in the model proposed by Banker and Natarajan (Oper Res 56: 48–58, 2008a), ordinary least squares (OLS) provides consistent estimation. This paper examines, compares, and contrasts the very different assumptions underlying these two models, and makes clear that second-stage OLS estimation is consistent only under very peculiar and unusual assumptions on the data-generating process that limit its applicability. In addition, we show that in either case, bootstrap methods provide the only feasible means for inference in the second stage. We also comment on ad hoc specifications of second-stage regression equations that ignore the part of the data-generating process that yields data used to obtain the initial DEA estimates.     

The efficiency of Spanish arable crop organic farms,a local maximum likelihood approach

The methodologies that have been used in existing research to assess the efficiency with which organic farms are operating are generally based either on the stochastic frontier methodology or on a deterministic non-parametric approach. Recently, Kumbhakar et al. (J Econom 137:1–27, 2007) proposed a new nonparametric, stochastic method based on the local maximum likelihood principle. We use this methodology to compare the efficiency ratings of organic and conventional arable crop farms in the Spanish region of Andalucía. Nonparametrically encompassing the stochastic frontier model is especially useful when comparing the performance of two groups that are likely to be characterized by different production technologies.

Assessing bankruptcy prediction models via information content of technical inefficiency

We use a stochastic frontier model with firm-specific technical inefficiency effects in a panel framework (Battese and Coelli in Empir Econ 20:325–332, 1995) to assess two popular probability of bankruptcy (PB) measures based on Merton model (Merton in J Financ 29:449–470, 1974) and discrete-time hazard model (DHM; Shumway in J Bus 74:101–124, 2001). Three important results based on our empirical studies are obtained. First, a firm with a higher PB generally has less technical efficiency. Second, for an ex-post bankrupt firm, its PB tends to increase and its technical efficiency of production tends to decrease, as the time to its bankruptcy draws near. Finally, the information content about firm’s technical inefficiency provided by PB based on DHM is significantly more than that based on Merton model. By the last result and the fact that economic-based efficiency measures are reasonable indicators of the long-term health and prospects of firms (Baek and Pagán in Q J Bus Econ 41:27–41, 2002), we conclude that PB based on DHM is a better credit risk proxy of firms.     

Likelihood ratio tests for model selection of stochastic frontier models

An important issue when conducting stochastic frontier analysis is how to choose a proper parametric model, which includes choices of the functional form of the frontier function, distributions of the composite errors, and also the exogenous variables. In this paper, we extend the likelihood ratio test of Vuong, Econometrica 57(2):307–333, (1989) and Takeuchi’s, Suri-Kagaku (Math Sci) 153:12–18, (1976) model selection criterion to the stochastic frontier models. The most attractive feature of this test is that it can not only be used for testing a non-nested model, but also still be applicable even when the general model is misspecified. Finally, we also demonstrate how to apply this test to the Indian farm data used by Battese and Coelli, J Prod Anal 3:153–169, (1992), Empir Econ 20(2):325–332, (1995) and Alvarez et al., J Prod Anal 25:201–212, (2006).     

Identification-commitment inventory (ICI model): confirmatory factor analysis and construct validity

The aim of this study is to confirm the factorial structure of the Identification-Commitment Inventory (ICI) developed within the frame of the Human System Audit (HSA) (Quijano et al. in Revist Psicol Soc Apl 10(2):27–61, 2000; Pap Psicól Revist Col Of Psicó 29:92–106, 2008). Commitment and identification are understood by the HSA at an individual level as part of the quality of human processes and resources in an organization; and therefore as antecedents of important organizational outcomes, such as personnel turnover intentions, organizational citizenship behavior, etc. (Meyer et al. in J Org Behav 27:665–683, 2006). The theoretical integrative model which underlies ICI Quijano et al. (2000) was tested in a sample (N = 625) of workers in a Spanish public hospital. Confirmatory factor analysis through structural equation modeling was performed. Elliptical least square solution was chosen as estimator procedure on account of non-normal distribution of the variables. The results confirm the goodness of fit of an integrative model, which underlies the relation between Commitment and Identification, although each one is operatively different.     

Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach

The explanation of productivity differentials is very important to identify the economic conditions that create inefficiency and to improve managerial performance. In the literature two main approaches have been developed: one-stage approaches and two-stage approaches. Daraio and Simar (2005, J Prod Anal 24(1):93–121) propose a fully nonparametric methodology based on conditional FDH and conditional order-m frontiers without any convexity assumption on the technology. However, convexity has always been assumed in mainstream production theory and general equilibrium. In addition, in many empirical applications, the convexity assumption can be reasonable and sometimes natural. Lead by these considerations, in this paper we propose a unifying approach to introduce external-environmental variables in nonparametric frontier models for convex and nonconvex technologies. Extending earlier contributions by Daraio and Simar (2005, J Prod Anal 24(1):93–121) as well as Cazals et al. (2002, J Econometrics 106:1–25), we introduce a conditional DEA estimator, i.e., an estimator of production frontier of DEA type conditioned to some external-environmental variables which are neither inputs nor outputs under the control of the producer. A robust version of this conditional estimator is proposed too. These various measures of efficiency provide also indicators of convexity which we illustrate using simulated and real data. Cinzia Daraio received Research support from the Italian Ministry of Education Research on Innovation Systems Project (iRis) “The reorganization of the public system of research for the technological transfer: governance, tools and interventions” and from the Italian Ministry of Educational Research Project (MIUR 40% 2004) “System spillovers on the competitiveness of Italian economy: quantitative analysis for sectoral policies” which are acknowledged. Léopold Simar received Research support from the “Interuniversity Attraction Pole”, Phase V (No. P5/24) from the Belgian Government (Belgian Science Policy) is acknowledged.     

The examination of pseudo-allocative and pseudo-overall efficiencies in DEA using shadow prices

The original Data Envelopment Analysis (DEA) models developed by Charnes et al. (Eur J Oper Res 2:429–444, 1978), Banker et al. (Manag Sci 30:1078–1092, 1984) were both radial models. These models and their varied extensions have remained the most popular DEA models in terms of utilization. The benchmark targets they determined for inefficient units are primarily based on the notion of maintaining the same input and output mixes originally employed by the evaluated unit (i.e. disregarding allocative considerations). This paper presents a methodology to investigate allocative and overall efficiency in the absence of defined input and output prices. The benchmarks determined from models based on this methodology will consider all possible input and/or output mixes. Application of this methodology is illustrated on a model of the financial intermediary function of a bank branch network.     

Cost efficiency of Slovenian water distribution utilities: an application of stochastic frontier methods

This study estimates cost inefficiency and economies of scale of Slovenian water distribution utilities over the 1997–2003 period by employing several different stochastic frontier methods. The results indicate that significant cost inefficiencies are present in the utilities. An introduction of incentive-based price regulation scheme might help resolve this problem. However, the inefficiency scores obtained from different cost frontier models are not found to be robust. The levels of inefficiency estimates as well as the rankings depend on the econometric specification of the model. The established lack of robustness can be at least partly explained by different ability of the models to separate unobserved heterogeneity from inefficiency. Newly proposed true fixed effects model (Greene, J Econom 126:269–303, 2005; J Prod Anal 23(1):7–32, 2005) appears to perform better than the conventional panel data models with respect to distinguishing between unobserved heterogeneity and inefficiency. On the other hand, different models produce fairly robust results with respect to estimates of economies of output density, customer density and economies of scale. The optimal size of a company is found to closely corresponds to the sample median. Economies of scale are found in small-sized utilities, while large companies exhibit diseconomies of scale.

On the inefficiency of European socialist economies

This paper aims at comparing macroeconomic performance of three European socialist economies (Hungary, Poland, Yugoslavia) with developing and developed countries during the 1970s and the 1980s. Using panel data for 89 countries, we measure macroeconomic performance with two panel data production frontier models: the WITHIN model proposed by (Cornwell et al J Econom 46:185–200, 1990), and the firm effects model developed by (Battese and Coelli J Prod Anal 3:153–169, 1992). We conclude in favor of the underperformance of socialist countries in relation to developed countries but also to developing countries in most cases, which may be explained by the features of the socialist economic system.

Asymptotics of SIMEX-based variance estimation

In this paper, we study the asymptotic properties of simulation extrapolation (SIMEX) based variance estimation that was proposed by Wang et al. (J R Stat Soc Series B 71:425–445, 2009). We first investigate the asymptotic normality of the parameter estimator in general parametric variance function and the local linear estimator for nonparametric variance function when permutation SIMEX (PSIMEX) is used. The asymptotic optimal bandwidth selection with respect to approximate mean integrated squared error (AMISE) for nonparametric estimator is also studied. We finally discuss constructing confidence intervals/bands of the parameter/function of interest. Other than applying the asymptotic results so that normal approximation can be used, we recommend a nonparametric Monte Carlo algorithm to avoid estimating the asymptotic variance of estimator. Simulation studies are carried out for illustration.