Stochastic Frontier Models with Bounded Inefficiency

Almanidis, Pavlos; Qian, Junhui; Sickles, Robin C.

doi:10.1007/978-1-4899-8008-3_3

Cited by 40 publications

(47 citation statements)

References 49 publications

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“…This is achieved when U has a half-normal distribution mirrored at zero, shifted to the right by B = a 0 |γ| to have the same mean as the corresponding half-normal distribution with positive γ, and truncated at zero to ensure that inefficiency can only be positive. This is in fact a special case of the doublytruncated normal distribution of Almanidis and Sickles (2011) and Almanidis et al (2014) with location parameter B, truncation at zero and B, and scale parameter given by |γ|. In this case the moments of U can be found analytically.…”

Section: The Extended Normal-halfnormal Distributionmentioning

confidence: 99%

“…4 The expressions for the case γ > 0 were given in (8)- (10) with σ u = γ. where from the first row of Table 1 in Almanidis et al (2014) we can recover…”

Section: The Extended Normal-halfnormal Distributionmentioning

confidence: 99%

“…Note that this corresponds to a reflection around its center of the truncated exponential distribution in Table 1 of Almanidis et al (2014). Hence, ε is replaced by −w − B = −w + a 0 γ and σ u by −γ.…”

Section: The Extended Exponential Distributionmentioning

confidence: 99%

“…All of these onesided distributions have a positive skewness, so Li (1996) considers the case of a uniform distribution and Carree (2002) a negative binomial allowing negative skewness. In the same spirit Almanidis and Sickles (2011) and Almanidis et al (2014) consider a doubly truncated normal distribution for the inefficiencies. The latter three approaches assume that the inefficiency term is bounded above and below.…”

Section: Introductionmentioning

confidence: 99%

“…Examples are the aforementioned negative binomial model of Carree (2002) and the double truncated normal of Almanidis and Sickles (2011), Almanidis et al (2014). While these approaches have their merits, there are potential drawbacks.…”

mentioning

confidence: 99%

See 4 more Smart Citations

The “wrong skewness” problem in stochastic frontier models: A new approach

Hafner

Manner

Simar

2016

Econometric Reviews

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Stochastic frontier models are widely used to measure, e.g., technical efficiencies of firms. The classical stochastic frontier model often suffers from the empirical artefact that the residuals of the production function may have a positive skewness, whereas a negative one is expected under the model, which leads to estimated full efficiencies of all firms. We propose a new approach to the problem by generalizing the distribution used for the inefficiency variable. This generalized stochastic frontier model allows the sample data to have the wrong skewness while estimating well-defined and non-degenerate efficiency measures. We discuss the statistical properties of the model and we discuss a test for the symmetry of the error term (no inefficiency). We provide a simulation study to show that our model delivers estimators of efficiency with smaller bias than those of the classical model even if the population skewness has the correct sign. Finally, we apply the model to data of the U.S. textile industry for 1958-2005, and show that for a number of years our model suggests technical efficiencies well below the frontier, while the classical one estimates no inefficiency in those years.

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Section: The Extended Normal-halfnormal Distributionmentioning

confidence: 99%

“…4 The expressions for the case γ > 0 were given in (8)- (10) with σ u = γ. where from the first row of Table 1 in Almanidis et al (2014) we can recover…”

Section: The Extended Normal-halfnormal Distributionmentioning

confidence: 99%

Section: The Extended Exponential Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

The “wrong skewness” problem in stochastic frontier models: A new approach

Hafner

Manner

Simar

2016

Econometric Reviews

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Nonparametric tests of tail behavior in stochastic frontier models

Horrace

Wang

2022

J of Applied Econometrics

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Summary This article studies tail behavior for the error components in the stochastic frontier model, where one component has bounded support on one side, and the other has unbounded support on both sides. Under weak assumptions on the error components, we derive nonparametric tests for thin‐tailed distributional assumptions imposed on these two components. The tests are useful diagnostic tools for stochastic frontier analysis and kernel deconvolution density estimation. A simulation study and applications to four previously studied datasets are provided. In two of these applications, the new tests reject the thin‐tailed distributional assumptions such as normal or Laplace, which are commonly imposed in the existing literature.

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Penalized sieve estimation of zero‐inefficiency stochastic frontiers

Cai,

Horrace,

Parmeter

2023

J of Applied Econometrics

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SummaryStochastic frontier models for cross‐sectional data typically assume that the one‐sided distribution of firm‐level inefficiency is continuous. However, it may be reasonable to hypothesize that inefficiency is continuous except for a discrete mass at zero capturing fully efficient firms (zero‐inefficiency). We propose a sieve‐type density estimator for such a mixture distribution in a nonparametric stochastic frontier setting under a unimodality‐at‐zero assumption. Consistency, rates of convergence and asymptotic normality of the estimators are established, as well as a test of the zero‐inefficiency hypothesis. Simulations and two applications are provided to demonstrate the practicality of the method.

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Stochastic Frontier Models with Bounded Inefficiency

Cited by 40 publications

References 49 publications

The “wrong skewness” problem in stochastic frontier models: A new approach

The “wrong skewness” problem in stochastic frontier models: A new approach

Nonparametric tests of tail behavior in stochastic frontier models

Penalized sieve estimation of zero‐inefficiency stochastic frontiers

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