Copula-based Stochastic Frontier Research Articles

Nonlinear Effects in the Asymmetric Copula-Based Stochastic Frontier Model

Several stochastic frontier production analysis problems are not linear regression problems; two error components (inefficiency and noise) may not have symmetric dependence. In nonlinear problems, the data are divided into two or more regimes according to turning points or change points; the relationships between the output and all the inputs are allowed to differ between regimes. In the problem of two-error dependence, the inefficiency component generally plays a more dominant role than the noise component, thereby the output of each firm is mainly influenced by the inefficiency; in such cases, the symmetric dependence is not suitable. It is, therefore, desirable to come up with a new nonlinear asymmetric copula-based stochastic frontier model (SFM). Two nonlinear structures, namely kink and threshold and skew-normal copula, are suggested to SFM to deal with these problems. The Monte Carlo simulations and analysis of a real data set are employed to evaluate the accuracy and performance of proposed models. The results show that the proposed models show a higher performance when compared with the conventional linear SFM with symmetric two-error dependence.

A Copula-Based Stochastic Frontier Analysis of Thai Jasmine Rice Production in the Northeast of Thailand

Thai jasmine rice holds immense economic significance both domestically and as a major export, making it a vital contributor to the Thai economy. Traditional and modern agricultural practices, centered around rice cultivation, play a pivotal role in the culture and economy of the Northeast region. However, concerns have arisen regarding rice production efficiency in recent times due to capital and labor availability challenges. Motivated by these concerns, this study employs a copula-based stochastic frontier modeling (copula-SFM) framework to investigate production efficiency empirically. The research is based on a sample of 384 farmers in the Northeast of Thailand. The Akaike Information Criterion (AIC) is utilized to select the most appropriate model. The results indicate that the Gaussian copula-SFM outperforms other copula models. Notably, the study identifies total area, capital, and labor as critical factors significantly contributing to the positive impact on jasmine rice production. Furthermore, the research ranks provinces based on average technical efficiency (TE) scores, revealing Khon Kaen Province, Yasothon Province, and Roi Et Province as the top-performing regions.

Technical Efficiency of Rice Production in the Upper North of Thailand: Clustering Copula-Based Stochastic Frontier Analysis

This study examines the efficiency of rice production in Thailand, especially major rice, which is the main crop of farmers in all regions of Thailand and is still a pressing issue. Analyzing technical efficiency by using the appropriate analytical tools inevitably brings about determining the correct production efficiency measures. In this study, we applied the K-Means algorithm and copula-based stochastic frontier model to cluster farmer groups in order to find the different factors that impact the group, and to relax the assumption of the two components of random error, which is that they are independent to each other; the correlation of the two components of random error is also represented by the estimation of copula. The findings from the K-Means clustering algorithms applied in this study indicate that the production frontiers can be divided into two frontiers, with the number of farmers under the frontiers of such production differing from the number of farmers collected in each area. The production frontiers were obtained with 591 farmers under the first production frontier and 65 farmers under the second. In addition, the results reflected a correlation between the two error components U and V. This suggests inefficiencies and zero-mean, and that the symmetric error is not independent of each other. The findings from the application of the copula-based stochastic frontier production function models indicate that land, cost of chemicals, and labor inputs have significant positive effects on the mean output of major rice in both groups of farmers. Therefore, the results of this study indicate that the financial services in rural areas should be continuously promoted by governmental policy, particularly via agricultural loans, to rural people since the utilization of inputs affects the quantity of rice produced. Timely loans should be encouraged.

The Impact of Climate Change on Agriculture Sector in ASEAN

This study purposes to estimate climate change effect on agriculture sector in ASEAN by using the copula-based stochastic frontier approach to evaluate the technical efficiency and factors that affect agriculture production. Panel data of land, labour, fertilizer, and temperature in seven countries in ASEAN including Thailand, Vietnam, Myanmar, Philippines, Indonesia, Cambodia, and Malaysia collected from 2002 - 2016 were used for estimating the model. The results presented that the land, labour, and fertilizer consumption according to the agriculture have positive and significant effects on agricultural production. The most interesting point from this study, found that there is a negative effect on agriculture production related by the climate change. Additionally, this study provides the most appropriate tools to analyse climate change impacts on ASEAN agriculture and the potential options for adaptation in the agriculture sector.

Asymmetric dependence in the stochastic frontier model using skew normal copula

In this paper, a new skew normal copula-based stochastic frontier model (SFM) is proposed to investigate the asymmetric dependence among the disturbances U (representing technical inefficiency) and V (representing noise). By employing the skew-normal copula in SFM, the asymmetric joint behavior of U and V can be parameterized, thereby allowing the data to have the opportunity to determine the adequacy of the independence or symmetric copula assumption. The skewness of ordinary least square residuals under copula-based SFM is discussed. The performance of the proposed skew normal copula-based SFM is evaluated through simulation studies and an analysis of a real data set.

Technical efficiency of rice production in Thailand: copula-based stochastic frontier model

The study aims to investigate Thai rice production efficiency with the copula-based stochastic frontier model to draft general statistical data for Thai agriculture development in the future. The empirical results show that land and fertilizer have an indisputably positive and significant effect on rice production. The SFM with Gaussian copula has the lowest value regarding to AIC. The best average TE score belongs to the northern region followed by the central region, the north-eastern region, and the southern region with their average scores of 0.86, 0.819, 0.76 and 0.74, respectively. The interesting point is that, even with highest efficiency score, there is the largest gap between the highest and the lowest scores within central region provinces, followed by north-eastern, central and northern regions with their values of 0.2, 0.18, 0.15 and 0.08, respectively.