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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.

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.

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 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 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.