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

Liu, Jianxu; Ma, Ji; Rahman, Sanzidur; Sriboonchitta, Songsak

doi:10.3390/math8020238

Cited by 6 publications

(5 citation statements)

References 54 publications

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“…Moreover, the issues regarding random or measurement errors are especially prominent for productivity analysis at the macro level [44]. Instead, SFA allows the separation of inefficiency from random shocks or measurement errors, thus providing a more accurate estimate of efficiency [18,45]. Given this advantage, SFA has been applied in various studies to analyze agricultural productivity and its components, such as by Benedetti et al [46], Adetutu and Ajayi [18], and Gong [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Agricultural Productivity Growth and Its Determinants in South and Southeast Asian Countries

et al. 2020

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Improving agricultural productivity is a priority concern in promoting the sustainable development of agriculture in developing countries. In this study, we first apply stochastic frontier analysis (SFA) to analyze the growth of agricultural total factor productivity (TFP) and its three components (technical change—TC, technical efficiency change—TEC and scale change—SC) in 15 south and southeast Asian countries covering the period 2002 to 2016. Then, the determinants of agricultural TFP growth are identified using dynamic panel data models. The results reveal that the south and southeast Asian countries witnessed an overall decline in agricultural productivity during the sample period, thereby creating concerns over sustaining future agricultural growth. Technical progress was the major source of TFP growth, but its contribution has slowed in recent years. On the other hand, declining scale change and technical efficiency change resulted in the deterioration of productivity over time. Variable levels of productivity performances were observed for individual countries, mainly driven by technological progress. Overall, southeast Asia achieved a more stable and sustained agricultural growth as compared to south Asia. Among the determinants, human capital, level of urbanization, and development flow to agriculture positively influenced agricultural TFP growth, while the level of economic development and agricultural import were negatively associated with TFP growth. Policy recommendations include the suggestions that south and southeast Asian countries should increase investment in human capital, focus on technological innovation and make use of financial assistance and development flow to agriculture to increase and sustain agricultural productivity. In addition, frontier countries of the two regions (e.g., India and Indonesia) should take the lead on regional agricultural development ventures by enhancing cooperation with neighboring countries on technological innovations, and countries facing diseconomies of scale (i.e., Afghanistan and Iran) should consider the rational reallocation of agricultural inputs.

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Section: Literature Reviewmentioning

confidence: 99%

Agricultural Productivity Growth and Its Determinants in South and Southeast Asian Countries

et al. 2020

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“…Corbae and D'erasmo (2013) identified numerous crucial variables, such as interest rates, loan default rates, and bank capital adequacy ratios, that substantially affect the business's behavior and outcomes. In a study, the author applied a stochastic frontier model to a sample of Chinese banks and found evidence of substantial technical and allocative inefficiencies and substantial variability and reliance between banks (Liu et al, 2020). In another study on Chine's banking industry, the dynamic modeling approach was used to evaluate the influence of external factors such as macroeconomic conditions, regulatory policies, market competition, and technological innovation, and their feedback loops over time (Li et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Service Time Reduction Through the Development of a Simulation Model in a Selected Bank

Rashed

Nahar

Pritom

2023

AJMS

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Service time is an important element of the banking service. The time needed to wait in the queue makes the service receiver dissatisfied. To minimize the waiting time and improve the utilization rate, simulation-based analysis is a well-established technique. The objective of the current research is to investigate the current scenario of the service mechanism of the selected bank and suggest the best possible configuration for improving the service level. The research is based on a case study in which the operations of a specific bank were observed. A conceptual model of the studied bank has been developed first. It helps to realize how the entities move through the system. Then, an arena model was built according to the conceptual model following the collection and distribution of field data (arrival time, delay time at the aisle, and service time) via an input analyzer. The suggested model shows progressregardingthe average server utilization rate and waiting time customers spend in the queue. The proposed model shows a 30% improvement in waiting time and a 40% improvement in service time, or value-added time.

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

Cited by 6 publications

References 54 publications

Agricultural Productivity Growth and Its Determinants in South and Southeast Asian Countries

Agricultural Productivity Growth and Its Determinants in South and Southeast Asian Countries

Service Time Reduction Through the Development of a Simulation Model in a Selected Bank

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

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