A double-copula stochastic frontier model with dependent error components and correction for sample selection

Sriboonchitta, Songsak; Liu, Jianxu; Wiboonpongse, Aree; Denœux, Thierry

doi:10.1016/j.ijar.2016.08.006

Cited by 20 publications

(20 citation statements)

References 18 publications

(30 reference statements)

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“…To handle the endogeneity issue of

x

, by jointly modelling

x

and

y

, we allow their residuals to be correlated in Bayesian estimation (e.g., by specifying a bivariate normal distribution with a correlation

ρ

). This idea is similar to approaches to modelling the dependence between the problematic predictors and the residuals (e.g., Amsler, Prokhorov, & Schmidt, ; Park & Gupta, ; Sriboonchitta, Liu, Wiboonpongse, & Denoeux, ; Tran & Tsionas, ). Note that the proposed Bayesian estimation method should yield similar results to maximum likelihood estimation when the same likelihood function is used.…”

Section: Discussionmentioning

confidence: 98%

Asymptotic bias of normal‐distribution‐based maximum likelihood estimates of moderation effects with data missing at random

Zhang

Yuan

Wang

2018

Brit J Math & Statis

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Moderation analysis is useful for addressing interesting research questions in social sciences and behavioural research. In practice, moderated multiple regression (MMR) models have been most widely used. However, missing data pose a challenge, mainly because the interaction term is a product of two or more variables and thus is a non-linear function of the involved variables. Normal-distribution-based maximum likelihood (NML) has been proposed and applied for estimating MMR models with incomplete data. When data are missing completely at random, moderation effect estimates are consistent. However, simulation results have found that when data in the predictor are missing at random (MAR), NML can yield inaccurate estimates of moderation effects when the moderation effects are non-null. Simulation studies are subject to the limitation of confounding systematic bias with sampling errors. Thus, the purpose of this paper is to analytically derive asymptotic bias of NML estimates of moderation effects with MAR data. Results show that when the moderation effect is zero, there is no asymptotic bias in moderation effect estimates with either normal or non-normal data. When the moderation effect is non-zero, however, asymptotic bias may exist and is determined by factors such as the moderation effect size, missing-data proportion, and type of missingness dependence. Our analytical results suggest that researchers should apply NML to MMR models with caution when missing data exist. Suggestions are given regarding moderation analysis with missing data. x Ã ¼

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“…To handle the endogeneity issue of

x

, by jointly modelling

x

and

y

, we allow their residuals to be correlated in Bayesian estimation (e.g., by specifying a bivariate normal distribution with a correlation

ρ

Section: Discussionmentioning

confidence: 98%

Asymptotic bias of normal‐distribution‐based maximum likelihood estimates of moderation effects with data missing at random

Zhang

Yuan

Wang

2018

Brit J Math & Statis

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“…where f ε (ε) is the density function of ε, which can be obtained by the following steps [19]: First, the joint density f (w, ε) can be obtained using Equations (3) and (7). Transforming (w, v) to (w, ε), we can get the density function of (w, ε) [16]:…”

Section: Copula-based Stochastic Frontier Modelmentioning

confidence: 99%

“…Following that, many scholars have applied the copula-based SFM to assess efficiency of decision making units in various fields, such as, applications to analyze technical efficiencies of Moroccan municipalities [17] and intercrop coffee production in northern Thailand [18]. Further, Sriboonchitta et al [19] proposed a double-copula SFM with sample selection, extending the standard SFM with sample selection by modelling dependent error components using copula functions. Thus, it is important to allow for the dependence not only between composite errors across multiple equations but also between the statistical noise (random error) and inefficiency components in the simultaneous SFM framework.…”

Section: Introductionmentioning

confidence: 99%

A Simultaneous Stochastic Frontier Model with Dependent Error Components and Dependent Composite Errors: An Application to Chinese Banking Industry

Liu

Rahman

et al. 2020

Mathematics

Self Cite

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The paper develops a simultaneous equations stochastic frontier model (SFM) with dependent random noise and inefficiency components of individual equations as well as allowing dependence across all equations of the model using copula functions. First, feasibility of our developed model was verified via two simulation studies. Then the model was applied to assess cost efficiency and market power of the banking industry of China using a panel data of 37 banks covering the period 2013–2018. Results confirmed that our simultaneous SFM with dependent random noise and inefficiency components outperformed its predecessor, which is a simultaneous SFM with dependent composite errors but with independent random noise and inefficiency components of individual SFMs as well as the conventional single-equation SFM. Apart from the statistical and computational superiority of our developed model, we also see that Chinese banks in general have a high level of cost efficiency and that competition in the banking industry of China mainly exists in state-owned banks and joint stock banks. Presence of economies of scales as well as diseconomies of scales were found in different banks. Also, the state-owned banks embraced most sophisticated technologies thereby allowing them to operate with the highest level of cost efficiency.

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“…Therefore, based on Table 7, Product copula can be said as a relatively good model for the dataset since it has the highest correlation with the CCR model. The alternative approach for determining the best model to estimate efficiency scores is by checking the information criterion (AIC or BIC) [13], [14]. This approach will be conducted in the next study.…”

Section: Notations: F= Family; C = Clayton; G = Gumbel; a = A12; P = mentioning

confidence: 99%

Technical Efficiency of Construction Companies: A Stochastic Frontier Model with Dependent Error

Arsad

Isa²,

Razak

2019

IJRTE

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There are several problems with using the Standard Stochastic Frontier (SF) model to produce a company’s technical efficiencies. One of the problem is the hypothesis of independence between the error components, known as, statistical noise and inefficiency term. Recent studies have used the SFA-copula model where the copula element captures the joint distribution of the two error components thus assuming that the errors are dependent. This research seeks to use the Cobb-Douglas Stochastic Frontier production model (standard model), SFA-copula models and DEA models (DEA-CCR and DEA-BCC) to compare the technical efficiencies of 12 publicly listed construction companies in Malaysia. The latter models are non-parametric frontier models which will be used as a base to determine the best SF model. The four copulas considered in this study are Clayton, Gumbel, A12 and Product copulas. The main findings of this study are the SFA-copula models showed consistency in terms efficiency scores and rankings of companies as compared to DEA models. The results further raise the question of the reliability of standard SF model especially when SFA-product copula was chosen as the best model for measuring efficiency performance. Therefore, it is obvious that it is not possible to ignore the reliance between the noise and inefficiency term

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A double-copula stochastic frontier model with dependent error components and correction for sample selection

Cited by 20 publications

References 18 publications

Asymptotic bias of normal‐distribution‐based maximum likelihood estimates of moderation effects with data missing at random

Asymptotic bias of normal‐distribution‐based maximum likelihood estimates of moderation effects with data missing at random

A Simultaneous Stochastic Frontier Model with Dependent Error Components and Dependent Composite Errors: An Application to Chinese Banking Industry

Technical Efficiency of Construction Companies: A Stochastic Frontier Model with Dependent Error

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