Semiparametric Estimation of Stochastic Production Frontier Models

Fan, Yanqin; Li, Qi; Weersink, Alfons

doi:10.1080/07350015.1996.10524675

Cited by 190 publications

(191 citation statements)

References 25 publications

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“…We consider this relative robustness of MOLS with respect to ML as an attractive property, keeping in mind the present semiparametric setting. As mentioned in the introduction, Fan et al (1996) have earlier explored a parallel two-stage approach in the context of kernel estimation.…”

Section: Two-stage Estimation Strategymentioning

confidence: 99%

“…An alternative way to estimate the standard deviations r u , r v is to apply the pseudolikelihood (PSL) method suggested by Fan et al (1996). Compared to the MM, PSL is potentially more efficient, but is computationally somewhat more demanding.…”

Section: Pseudolikelihood Estimationmentioning

confidence: 99%

“…The emerging literature on semi/ nonparametric stochastic frontier estimation has thus far mainly departed from the SFA side, replacing the parametric frontier function by a nonparametric specification that can be estimated by kernel regression or local maximum likelihood (ML) techniques. Fan et al (1996) and Kneip and Simar (1996) were among the first to apply kernel regression to frontier estimation in the cross-sectional and panel data contexts, respectively. Fan et al (1996) proposed a two-step method where the shape of the frontier is first estimated by kernel regression, and the conditional expected inefficiency is subsequently estimated based on the residuals, imposing the same distributional assumptions as in standard SFA.…”

Section: Introductionmentioning

confidence: 99%

“…Fan et al (1996) and Kneip and Simar (1996) were among the first to apply kernel regression to frontier estimation in the cross-sectional and panel data contexts, respectively. Fan et al (1996) proposed a two-step method where the shape of the frontier is first estimated by kernel regression, and the conditional expected inefficiency is subsequently estimated based on the residuals, imposing the same distributional assumptions as in standard SFA. Kneip and Simar (1996) similarly use kernel regression for estimating the frontier, but they make use of panel data to avoid the distributional assumptions.…”

Section: Introductionmentioning

confidence: 99%

“…The noise term is assumed to be symmetric, so the skewness of the regression residuals is attributed to the inefficiency term. Given the parametric distributional assumptions of the inefficiency and the noise terms, we can estimate the variance parameters by using the method of moments (Aigner et al 1977) or pseudolikelihood (Fan et al 1996) techniques. The conditional expected value of the inefficiency term can obtained by using the results of Jondrow et al (1982).…”

Section: Introductionmentioning

confidence: 99%

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

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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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Section: Two-stage Estimation Strategymentioning

confidence: 99%

Section: Pseudolikelihood Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2010

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Stochastic Dea

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2017

Advances in DEA Theory and Applications

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New Growth Accounting

Gong¹

2020

American J Agri Economics

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This article opens the black box of total factor productivity by decomposing this “all‐in‐one” index into various input‐embedded and input‐free productivities in a new growth accounting framework. The new method identifies different channels through which growth drivers affect economic growth and finds the most effective way to boost the economy, which is unidentified in standard method. This new approach uses a varying coefficient stochastic frontier model, which integrates the standpoints of the endogenous growth theory and the induced innovation theory into a reduced‐form productivity analysis. The new growth accounting is then applied to study the impacts and contributions of R&D investments, international trade, and structural transformation to world agricultural growth during the period of 1962 to 2014. The empirical results provide new evidence to support the endogenous growth theory and the induced innovation theory, indicating the necessity of using the new growth accounting method.

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Semiparametric Estimation of Stochastic Production Frontier Models

Cited by 190 publications

References 25 publications

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

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

Stochastic Dea

New Growth Accounting

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