Biases in frontier estimation due to heteroscedasticity

Caudill, Steven B.; Ford, Jon M.

doi:10.1016/0165-1765(93)90104-k

Cited by 209 publications

(153 citation statements)

References 3 publications

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“…Para se considerar esse aspecto, variáveis relacionadas ao tamanho das unidades foram incluídas na estimação de cada uma dessas variâncias, da seguinte maneira: e/ ou , em que w n são as referidas variáveis de tamanho. Não levar em conta esse aspecto pode levar à superestimação do intercepto da fronteira e a vieses nos demais coeficientes (Caudill;Ford, 1993).…”

Section: O Estágio -O Modelo Dea-bcc Produto Orientadounclassified

Eficiência das escolas públicas urbanas das regiões nordeste e sudeste do Brasil: uma abordagem em três estágios

Carvalho

2014

Estud. Econ.

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ResumoNesse trabalho foi calculada uma medida de eficiência técnica para as escolas públicas urbanas das regiões Nordeste e Sudeste do Brasil, avaliadas pela Prova Brasil 2007, por meio da metodologia chamada de Análise da Envoltória de Dados (DEA) em três estágios, adotando-se a escola como unidade de análise. Em relação a todas as escolas estudadas, há indícios para afirmar que, mesmo descontando os fatores ambientais e aleatórios, ainda persiste a necessidade de melhorias ligadas à gestão de cada uma delas. Palavras-ChaveAnálise da Envoltória de Dados (DEA), eficiência, educação, Análise das Fronteiras Estocásticas (SFA), Nível Socioeconômico (NSE), Prova Brasil AbstractIn this study, we calculated a measure of technical efficiency for urban public schools in the Northeast and Southeast of Brazil; evaluated by a battery of tests called Prova Brasil; year 2007, using Three-stage Data Envelopment Analysis (DEA) methodology; taking the school as unit of analysis. Based on our results we can say that there are margins for improvements in Brazilian urban public schools in terms of pure managerial ability.

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Section: O Estágio -O Modelo Dea-bcc Produto Orientadounclassified

Eficiência das escolas públicas urbanas das regiões nordeste e sudeste do Brasil: uma abordagem em três estágios

Carvalho

2014

Estud. Econ.

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“…1 In a previous study, Caudill and Ford (1993) also found biased estimates of the frontier parameters.…”

Section: Heterogeneity In Stochastic Frontier Models a A Brief Lmentioning

confidence: 99%

Bayesian estimation of inefficiency heterogeneity in stochastic frontier models

2013

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Abstract_______________________________________________________________Estimation of the one sided error component in stochastic frontier models may erroneously attribute firm characteristics to inefficiency if heterogeneity is unaccounted for. However, it is not clear in general in which component of the error distribution the covariates should be included. In the classical context, some studies include covariates in the scale parameter of the inefficiency with the property of preserving the shape of its distribution. We extend this idea to Bayesian inference for stochastic frontier models capturing both observed and unobserved heterogeneity under half normal, truncated and exponential distributed inefficiencies. We use the WinBugs package to implement our approach throughout. Our findings using two real data sets, illustrate the relevant effects on shrinking and separating individual posterior efficiencies when heterogeneity affects the scale of the inefficiency. We also see that the inclusion of unobserved heterogeneity is still relevant when no observable covariates are available. ABSTRACTEstimation of the one sided error component in stochastic frontier models may erroneously attribute firm characteristics to inefficiency if heterogeneity is unaccounted for. However, it is not clear in general in which component of the error distribution the covariates should be included. In the classical context, some studies include covariates in the scale parameter of the inefficiency with the property of preserving the shape of its distribution. We extend this idea to Bayesian inference for stochastic frontier models capturing both observed and unobserved heterogeneity under half normal, truncated and exponential distributed inefficiencies. We use the WinBugs package to implement our approach throughout. Our findings using two real data sets, illustrate the relevant effects on shrinking and separating individual posterior efficiencies when heterogeneity affects the scale of the inefficiency. We also see that the inclusion of unobserved heterogeneity is still relevant when no observable covariates are available.

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“…This assumption is referred to as homoskedasticity, and it forms one of the maintained assumptions of the classic SFA model by Aigner et al (1977). As Caudill and Ford (1993) and Florens and Simar (2005) demonstrate, violation of the homoskedasticity assumption leads to potentially serious problems in the context of parametric frontier estimation. Clearly, similar problems carry over to the present semiparametric setting as well.…”

Section: Heteroskedasticitymentioning

confidence: 99%

“…Moreover, multiplicative error specification might help to alleviate heteroskedasticity from different scale sizes (cf. Caudill and Ford 1993).…”

Section: Multiplicative Modelmentioning

confidence: 99%

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

2010

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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 twostage method is proposed, referred to as Stochastic Nonsmooth 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.

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Biases in frontier estimation due to heteroscedasticity

Cited by 209 publications

References 3 publications

Eficiência das escolas públicas urbanas das regiões nordeste e sudeste do Brasil: uma abordagem em três estágios

Eficiência das escolas públicas urbanas das regiões nordeste e sudeste do Brasil: uma abordagem em três estágios

Bayesian estimation of inefficiency heterogeneity in stochastic frontier models

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

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