The creative economy, a global driver of economic development, plays a significant role in Thailand. Defined by UNCTAD as the marriage of creativity, culture, and technology, it emphasizes intellectual property and cultural goods (UNCTAD in World investment report 2010: investing in a low carbon economy. United Nations, 2010). This economic model not only promotes innovation, job creation, and cultural diversity (Hesmondhalgh in The cultural industries (2nd ed.). SAGE Publications Ltd. 2002) but also contributes significantly to Thailand’s GDP. While Gross Domestic Product (GDP) serves as a key economic indicator guiding policymakers and economists, forecasting the creative economy’s impact on Thailand’s GDP may have limitations that make it challenging to rely solely on historical data. More adaptable forecasting methods are needed to effectively analyse and predict its growth and impact. This study uses the grey model’s ability to handle small and incomplete data sets and capture the dynamic interactions between various factors, which makes it particularly well-suited for forecasting the GDP of the creative economy in Thailand. The results reveal that a diversification of revenue streams underscore the expanding demand within music, performing arts, visual arts, movies, and broadcasting sectors over the specified timeframe. In addition, the potential of these sectors in Thailand’s economy signals opportunities for further development and investment in the Thai food, traditional medicine, and cultural tourism industries. However, the dynamic nature of these industries and the growth rate of the creative economy are driven by technological advancements, changing consumer preferences, and market dynamics. For the overall performance of the creative economy Total creative industries (TCI), which are affected by the other variables, can be seen to increase for 10-year periods. Therefore, the gross domestic product (GDP) of the creative economy in Thailand is indeed crucial for policymakers, investors, and stakeholders since the creative economy has the potential to affect the growth rate of the overall economy.

The banking sector in Myanmar has substantial employee turnover. However, there is still a lack of research on the specific rates and efficacy of the current retention techniques. This study aims to investigate the human capital investment situations of the private banks of Myanmar from employees’ perspectives and examines employee retention intention. This study was survey research where a sample of 410 employees from three private banks in Yangon, Myanmar were collected and analyzed by Cox Proportional Hazard Model to examine the relationships between human capital investment activities and employee retention intention. The research revealed that work-life balance and salary have significant impacts on employee retention intention. However, the relationships between employee retention intention and educational support, training and health benefits are not statistically significant in the current uncertain economic situation. Moreover, private banks’ investment in health benefits of the employees is the lowest from the perspective of employees, and the average year that employees are willing to stay more with their current organizations is 3.5 years. Furthermore, the sector employees are working in and employees’ years of service influence on employee retention intention.

For the success of efficient socioeconomic development, it is crucial that budget allocation in higher education is effectively managed, with a clear focus on targeting SDG 4 (Quality Education), which is vital for every country and should be prioritized globally. This research article attempts to assess the socio-economic impact of Chiang Mai University based on the impact of both its expenditure and teaching and training programs on the Northern Thailand economy. Moreover, it also aims to develop the best model to predict the SROI for academic projects before investing the budget for efficient financial management. All the data utilized in this research article come from official organizations such as Chiang Mai University, the Office of the National Economic and Social Development Council (NESDC), and the Provincial Comptroller’s Office of each province in Northern Thailand, with the data collection covering the study period from 2023 to 2025. The key finding is that Chiang Mai University played a significant role in creating a socioeconomic impact on Northern Thailand’s economy, both in the industry sector and the service sector, totaling more than an average of THB 3 billion per year for direct and indirect effects. In addition, every THB 1 million that this university spends can create more than 703 jobs in the agribusiness sector, and, for the same budget spending, it can create 241 jobs in the service sector and 113 jobs in the industry sector, respectively. Technically, for the prediction model to predict the SROI value, it was found that the best model is the Decision Tree model. If the findings of this research can be applied to other universities in Thailand or globally, it would represent a significant initiative in optimizing budget allocation, with a particular emphasis on supporting SDG 4 (Quality Education) as a priority.

The aim of the study was to examine the sustainability (environmental, social and economic impacts) in Cambodia, Laos, Myanmar, and Vietnam (CLMV countries) and Thailand. We evaluated sustainable development through three perspectives such as environmental indicators assessed include carbon emissions, social indicators include inequality, and the economic indicator is the growth rate of real GDP. Our theoretical model introduced Bayesian kink regression model. From the results of the study, it was found that economic development is correlated with the level of inequality. While economic development does not affect the amount of carbon emissions. This demonstrates that economic development has a greater effect on inequality than environmental problems. Economists and policymakers can use the results of empirical studies to come up with guidelines or policies that can be implemented for finding ways to develop the economy further, taking into account the impact of creating more inequality from economic development.

The Bio-Circular-Green economy (BCG) concept was originally started by the Thai government to promote national development and post-pandemic recovery in 2021. This study concentrates on the ways to increase the effectiveness of Thailand's natural rubber exports. The primary goal is to evaluate Thailand's natural rubber exports to ASEAN nations, in terms of their technical efficiency rankings. In order to achieve the main objectives based on the BCG concept (BCG policy measures in number 10: "investing in infrastructure"), the spatial dataset is applied with a stochastic frontier analysis model, which is called the panel spatial stochastic frontier analysis model estimation. The empirical results of this study to improve the technical efficiency of Thailand's rubber export found that the infrastructure, especially the logistic system requirements of CLMV countries, needs to be addressed first. This is because the mixed spatial matrix (mixed-wij) represents significantly the level of logistics system development, which plays an important role in sustainably improving the technical efficiency. Therefore, the government and private sectors can use these empirical findings to promote policy recommendations in agricultural economics, especially the investment in logistic systems' aspects of low carbon emissions and using renewable energy, which is the BCG concept.

We evaluated the movement in the daily number of COVID-19 cases in response to the real GDP during the COVID-19 pandemic in Thailand from Q1 2020 to Q1 2021. The aim of the study was to find the number of COVID-19 cases that could maintain circulation of the country's economy. This is the question that most of the world's economies have been facing and trying to figure out. Our theoretical model introduced dynamic stochastic general equilibrium (DSGE) models with a special emphasis on Bayesian inference. From the results of the study, it was found that the most reasonable number of COVID-19 cases that still maintains circulation of the country's economy is about 3000 per month or about 9000 per quarter. This demonstrates that the daily number of COVID-19 cases significantly affects the growth of Thailand's real GDP. Economists and policymakers can use the results of empirical studies to come up with guidelines or policies that can be implemented to reduce the number of infections to satisfactory levels in order to avoid Thailand lockdown. Although the COVID-19 outbreak can be suppressed through lockdown, the country cannot be locked down all the time.

Examining the impact of revenues from the tourism sector on poverty reduction in selected members of the Association of Southeast Asian Nations (ASEAN), including Thailand, Malaysia, and Singapore, is the primary objective of this study. The Bayesian Structural Time Series (BSTS) model and the regression kink design (RKD or RK design) were employed to identify the causal effects between tourism revenues and the poverty headcount ratio during 2009–2019. The main conclusion from the analysis is that the relationship between tourism revenue and poverty reduction (in the form of the poverty headcount ratio) in Malaysia and Singapore have similar patterns. In contrast, the pattern of the impact of tourism revenues on the poverty headcount ratio in Thailand is different from the other two countries. For Singapore and Malaysia, increasing tourism revenue can reduce the poverty headcount ratio in the early stages. Nevertheless, after a while, increasing tourism revenue in later stages will not reduce the poverty headcount ratio. On the other hand, Thailand’s tourism revenue can reduce the poverty headcount ratio with time lags. However, in the early stages, Thailand’s tourism revenue could not reduce the poverty headcount ratio.

This paper examines the agricultural productivity efficiency in four countries consists Cambodia, Laos, Myanmar, and Vietnam (CLMV). The Bayesian Stochastic Frontier analysis is used to estimate in this study, this method has several advantages over the traditional method called Stochastic frontier analysis (SFA). The Bayesian method provide more information to be estimation under the uncertainty of parameters. The data consider the period 1991-2019 which comprises 4 countries for 29 years, with 116 observations. The results show that most of the average elasticity variables of agricultural input have a positive association with the agricultural output, this implies that the production frontier is well behave and increase in inputs. It can be concluded that the agricultural outputs of Cambodia, Laos, Myanmar and Vietnam (CLMV) countries in this sample were sensitive to changes in agricultural land followed by changes in agricultural fertilizer and labor. Therefore, the recommendation policy for these countries is governments should focus on enhance the productivity by increasing the technology or innovation in the CLMV countries.

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.

This study investigates the dynamic empirical link between tourism demand (tourist arrivals, tourism revenues and tourism expenditures) and economic growth in the case of Thailand using a quarterly time-series data set from 2013q1 to 2018q4. The combination of Bayesian approach and Markov Chain Monte Carlo (MCMC) simulations can be applied and employed to estimate the parameters of tourism demand and economic growth. Stationary and correlative trends of variables datasets were examined by using Bayesian ADF unit-root testing (BADF), Bayesian seasonal unit-root testing (BHEGY) and Bayesian Auto Regressive Distributed Lag (BARDL) model respectively. BADF is applied in order to probe the stationary of the time-series data set. Moreover, BHEGY is utilized in order to examine the seasonally of the time-series data set. Furthermore, BARDL technique is used and implemented in order to analyse the long-run and short-run relationship between tourism demand and economic growth. Our empirical findings provide important policy implications for further study on Thailand tourism.

This study undertook the investigation on the technical efficiency of tourism and logistics sectors which obtained from two concepts, namely the Bootstrapping Data Envelopment Analysis (Bootstrapping DEA) method and the Stochastic Frontier Analysis (SFA) method based on the assumption regarding the error in the production process. The results of two concepts can be compared by using copula model. The three top destinations in ASEAN (Thailand, Singapore and Malaysia) between 2006 and 2016 is selected. The main conclusion from the analysis is that there is asymmetric distribution. It is determined that the efficiency scores from both methods should not be compare like previous papers we reviewed because this study proved that both methods have asymmetric comparison. The result suggests that each method have particular strengths and weaknesses and potentially measures different aspects of efficiency. This research endorses approach depending on the practical outcome.

This paper is proposed to study on the sections of computational econometric estimations of Thailand’s Business Tourism (MICE) sectors. The objective is to examine the relationship among GDP, demands and revenues of Business Tourism (MICE) industry in Thailand during the period 2010–2016, based on Bayesian Analysis. Bayesian Analysis is applied to estimated Business Tourism (MICE) parameters, combining with Markov Chain Monte Carlo (MCMC) simulations. Stationary and correlative trends of variable sets were checked by employing Bayesian Augmented Dickey-Fuller (ADF) unit-root test and Bayesian Autoregressive Distributed Lag (ARDL) model respectively. Moreover, dependent structure was scrutinized by using canonical (C-) vine Copula method. Empirically, the results imply that revenues contribute most to long-run as well as short-run GDP growth. However, in the structure of the Business Tourism (MICE) industry, the number of tourists is also a significant variable.