Multivariate Skew Normal-Based Stochastic Frontier Models

Zhu, Xiaonan; Wei, Zheng; Wang, Tonghui

doi:10.1007/s42519-022-00249-9

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…To answer these questions, we performed a simulation experiment. Following the simulation experiments of Wei et al [44], Liu et al [45] and Zhu et al [46], we randomly drew datasets from a copula-based stochastic frontier model with heterogeneity. The simulated process of this experiment can be illustrated as follows:…”

Section: Simulation Studymentioning

confidence: 99%

Consequences of Ignoring Dependent Error Components and Heterogeneity in a Stochastic Frontier Model: An Application to Rice Producers in Northern Thailand

Liu

Xue

et al. 2022

Agriculture

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The traditional Stochastic Frontier Model (SFM) suffers from a very restrictive assumption of independence of its error components and also limited ability to address heterogeneity (inefficiency effects) satisfactorily, thereby leading to potential biases in the estimation of model parameters, identification of inefficiency effect variables influencing efficiency and, ultimately, efficiency scores. This paper aims to investigate the consequences of ignoring any dependency in error components and heterogeneity in the stochastic frontier model, and proposes a copula-based SFM with heterogeneity to resolve such weaknesses based on a simulation study to prove its superiority over the traditional SFM, followed by an empirical application on a sample of rice producers from northern Thailand. We demonstrate that the proposed model, i.e., copula-based SFM with dependent error components and heterogeneity, is unbiased and robust. The simulation experiments show that the traditional SFM can cause biases in parameter estimation and severe overestimation of technical efficiency. The traditional SFM with heterogeneity also has similar consequences. However, just ignoring heterogeneity does not have a great impact on parameter estimation and technical efficiency compared to the consequence of ignoring dependency in error components. The empirical application of the proposed model results show that land, labor and material inputs are all significant drivers of rice production in our copula-based SFM with dependent error components and heterogeneity, whereas in the traditional SFM model only the land variable seems to be a significant driver of rice production. The mean technical efficiency (MTE) score was also overestimated by two points in the traditional SFM, i.e., MTE = 0.88 versus 0.86. Finally, results of the copula-based SFM with dependent error components and heterogeneity reveals that both subsistence pressure and the use of hired labor are significantly associated with technical inefficiency, whereas the traditional SFM could identify the effect of hired labor use only. Therefore, caution is necessary when interpreting results from the conventional SFM as the results may be biased, incomplete and/or inadequate.

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Section: Simulation Studymentioning

confidence: 99%

Consequences of Ignoring Dependent Error Components and Heterogeneity in a Stochastic Frontier Model: An Application to Rice Producers in Northern Thailand

Liu

Xue

et al. 2022

Agriculture

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show abstract

“…Recently, Badunenko and Henderson (2023) introduced an SFM with skew-normal noise and exponentially distributed inefficiency (SN-Exp SFM), while Wei et al (2021) proposed an SFM with skew-normal noise and half-normally distributed inefficiency (SN-HN SFM). The latter encompasses the classical SFM in Aigner et al (1977) and Meeusen and van Den Broeck (1977) as a special case and is further extended to a multivariate setting by Zhu et al (2022).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Stochastic Frontier Models under the Skew Normal Settings

Wei,

Choy,

Wang

et al. 2024

Preprint

Self Cite

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Recently, a skew normal-based stochastic frontier model has emerged as a promising tool for efficiency analysis. In this paper, a Bayesian framework for statistical inference is presented, incorporating both informative and non-informative prior knowledge. The efficacy of the Bayesian approach is evaluated through rigorous examination using both simulation data and real data from a manufacturing productivity study. A comprehensive comparison with the conventional maximum likelihood approach is conducted. Results from both simulated and empirical investigations unequivocally demonstrate the superior performance of the Bayesian methodology.

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An alternative corrected ordinary least squares estimator for the stochastic frontier model

Parmeter

Zhao

2023

Empir Econ

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Multivariate Skew Normal-Based Stochastic Frontier Models

Cited by 7 publications

References 23 publications

Consequences of Ignoring Dependent Error Components and Heterogeneity in a Stochastic Frontier Model: An Application to Rice Producers in Northern Thailand

Consequences of Ignoring Dependent Error Components and Heterogeneity in a Stochastic Frontier Model: An Application to Rice Producers in Northern Thailand

Bayesian Stochastic Frontier Models under the Skew Normal Settings

An alternative corrected ordinary least squares estimator for the stochastic frontier model

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