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The United Nations has promoted and supported the UNCTAD Creative Economy Programme since 2004 to help countries around the world understand how to promote economic development through creativity in industries. This research article aims to determine whether the creative economy will be the major engine to accelerate Thailand's economic development in the coming decade or not, and what the major creative economy sectors are that must be prioritized or initiated and focused on. The data implemented in this research cover 2011-2018, which consist of creative economy sector income, the IO table, and the SAM table. The methodology utilized in this research was the ML model, the GREY model for predicting the growth rate of income from the major creative economy sectors contribute to Thailand's economy between 2019-2025, and the CGE model. The study's empirical findings show that the significant sectoral creative economy consists of fashion, advertising, Thai food, and cultural tourism, which need to be given more stimulus. Furthermore, the economies of Chiang Mai, and Thailand as a whole, would eventually be high-income economies if creative economy sectors were to be promoted and continuously supported by efficient policies. the economic growth of Thailand and Chiang Mai would eventually become high income whenever these economies allow creative economy sectors to be promoted or supported by efficient policies continuously.

With the COVID-19 pandemic’s complexity and inexorable devastation, this research article attempts to forecast Thailand’s economic move forward through gastronomic tourism promotion. The dynamic input-output (I-O) model was the primary method for classifying gastronomic activities in tourism I-O data, which was investigated sector by sector. The Ministry of Tourism and Sports in Bangkok, Thailand, officially gathered the 2017 I-O table. To briefly explain the empirical results, it found that the main sectors of gastronomic tourism that highly impact Thailand’s economy are the processing and preserving of foods, other foods, food and beverage serving activities, and other food services. In terms of forecasting during the period of the COVID-19 pandemic, the Bayesian Structural Time Series (BSTS) based on the dynamic input-output (I-O) model suggests that approximately 1% to 2% of Thailand’s gastronomic tourism will be able to contribute to the GDP of this country substantially. By the way, if this research result is significant, then both the private sector and the government sector need to be concerned and promote those sectors as much as they can.

Sustainable development aims to balance current and future needs across economic, social, and environmental dimensions. This study explores the relationships among tourist numbers (Tr), sustainability (SDG), and globalization, divided into economic (GOE), social (GOS), and political (GOC) dimensions, with COVID-19 (CO) included as a short-term factor. Using annual panel data from ASEAN countries (2001–2022), panel unit root, cointegration, and autoregressive distributed lag (Panel ARDL) models were applied to analyses short- and long-term relationships. The findings reveal that sustainability (SDG) and the political globalization dimension (GOC) significantly influence tourist numbers (Tr) in the long term. COVID-19 (CO) affected tourism in the short term. The results underscore the positive impact of sustainable development on tourism and its integration with globalization’s economic and social dimensions. ASEAN countries should prioritize sustainable tourism policies and strengthen international political cooperation to enhance tourism’s long-term benefits. Plain Language Summary How Sustainability, Globalization, and COVID-19 Affect Tourism in ASEAN Countries Tourism plays an important role in the economy of ASEAN countries. This study looks at how sustainable development, globalization, and COVID-19 have influenced the number of tourists visiting these countries. The results highlight the benefits of sustainable development and international political cooperation for boosting tourism. ASEAN countries should focus on policies that promote sustainable tourism and strengthen global partnerships to ensure long-term growth.

Cryptocurrencies are unique and extra-ordinary currencies which to be econometrically forced into the linear model due to their systematic complexity and extreme movements. This paper was conducted to provide an alternative analysis as a solution for escaping the restrictions of traditional linear assumptions. Five predominant digital currencies such as Bitcoin (BTC), Stellar network (XLM), Litecoin (LTC), Ethereum Classic (ETC), and IOTA were chosen to be employed in the multiple processes based on Bayesian approaches. Market dominance and data regime classifications are the essential components that lead to successfully investigate the dependent structures and co-movements in the digital financial market. The empirical findings could assume that the modern time-series data was meticulously estimated by the flexible modern tool. Bayesian statistics and simulations have the sufficient potency as the suitable solution.

It is reasonable to state that gastronomic tourism is an efficient tool that has the potential to refresh Thailand's macroeconomic viability. With the aim of becoming a hub of tourism in Southeast Asia, Thailand's tourism industry must urgently address and sustainably integrate gastronomic activities to navigate the troubled situation caused by its decline after the COVID-19 pandemic. This has led the authors to conduct a deep study on a regional input-output (I-O) table analysis for Thailand's tourism system, specifically focusing on gastronomic activities and tourism industries. The tourism I-O data used in this study come from the official source provided by the Thailand Ministry of Tourism and Sport. Empirically, the results of the dynamic regional I-O model predict that Bangkok and its surrounding areas are the heart of gastronomic tourism development, driving income into Thailand's economy. The eastern region stands as the second-largest area of gastronomy tourism, generating a positive impact on Thailand's economy. On the other hand, the Northeast of Thailand receives less income from gastronomy tourism despite being the largest area in the country. Ultimately, there should be a greater emphasis on gastronomy tourism policies in order to fully maximize their potential for tourism development, stimulating every part of Thailand during the economic depression caused by COVID-19. Moreover, gastronomy tourism has the potential to play an important role in driving economic growth through the combination of cuisine and tourism development.

This study examines how monetary and fiscal policies affect economic growth in China under global economic uncertainty. We estimate a Markov Switching Regression (MSR) model using quarterly data from 1996: Q1 to 2024: Q4. We also apply Bayesian Model Averaging (BMA) to choose the relevant control variables. During expansions, higher policy rates, government revenue, moderate inflation, FDI inflows, and export growth support growth. Government expenditure can crowd out private investment. During recessions, higher policy rates reduce growth. Government expenditure has limited impact, but revenue collection remains growth-supportive. Global uncertainty steadily reduces growth. Government expenditure shows negative effects, which indicates possible crowding out. The findings support that monetary and fiscal policies coordination may sustain long-term growth in China and strengthen the resilience amid global uncertainty. The Impulse response functions (IRFs) from Bayesian Vector Autoregression (BVAR) confirm the persistence and dynamics of policy shocks under global uncertainty. This study adds to the empirical literature on the role of macroeconomic policies in shaping economic growth in the case of China.

This article identifies the main determinants of China's outward foreign direct investment (OFDI) activities with Greater Mekong Subregion (GMS) countries, namely, Cambodia, Lao, Myanmar, Vietnam, and Thailand during the period between 2007 and 2016. We established the Bayesian panel data approach combination. The results of this study show that a higher economic growth rate, gross domestic product, and political stability tend to increase the likelihood of receiving Chinese outward foreign direct investment. On the other hand, higher foreign direct investment performance, inflation rates, rule of law, and business freedom tends to decrease the probability of being a recipient of Chinese outward foreign direct investment. Compared with previous studies that only assessed economic variables, the innovation of this study lies in its inclusion of socio-political variables.

The advantage of quantum mechanics to shift up the ability to econometrically understand extreme tail losses in financial data has become more desirable, especially in cases of Value at Risk (VaR) and Expected Shortfall (ES) predictions. Behind the non-novel quantum mechanism, it does interestingly connect with the distributional signals of humans' brainstorms. The highlighted purpose of this article is to devise a quantum-wave distribution methodically to analyze better risks and returns for stock markets in The Association of Southeast Asian Nations (ASEAN) countries, including Thailand (SET), Singapore (STI), Malaysia (FTSE), Philippines (PSEI), and Indonesia (PCI). Data samples were observed as quarterly trends between 1994 and 2019. Bayesian statistics and simulations were applied to present estimations' outputs. Empirically, quantum distributions are remarkable for providing \"real distributions\", which computationally conform to Bayesian inferences and crucially contribute to the higher level of extreme data analyses in financial economics.

The panic surrounding the COVID-19 pandemic has collapsed world economic systems. Thailand is no exception. Evidently, domestic-industrial activities in many countries have nearly stopped, and international sectors have abruptly disintegrated. Unemployment has become critical, and business viability has run into trouble. This article illustrates the potential solutions to these issues linked to Thailand’s domestic and international-trade economies by econometrically applying sectoral forecasting analyses and simulations because the pandemic has not only caused physical damage, but also a negative chain in worldwide economic systems. Methodologically, dynamic input-output (I-O) analysis is circumspectly employed to obtain foresight on some predominantly industrial sectors that could potentially rescue the Thai domestic economy from the depression of 2020–2022. The I-O tables are sourced from official data of the Asian Development Bank (ADB). For the international-trade scenario, the agent-based model and simulations are used to forecast the future trends of macroeconomic responses. The two methods, dynamic I-O models and agent-based simulations, are the current means capable of sensibly predicting macroeconomic responses for monitoring the upcoming years. The empirical outcomes can clearly predict upcoming events that will be beneficial for policy implementations.

This study aims to accomplish three main tasks. Firstly, it seeks to determine the more appropriate choice between classical and Bayesian methods in estimating a pooled panel kink regression model under the condition of a known but bounded policy variable choice that serves as a kink point. Secondly, as a product of the first target, the study seeks to provide empirical evidence for the economic growth-economic freedom nexus in five top-performing economies in Sub-Saharan Africa. Using index explanatory variables, which are bounded between 0 and 100, and using both numerical and graphical methods, the findings show that the use of the Bayesian method is more appropriate in characterizing the data than the classical OLS framework, as the former better accounts for randomness via the use of posterior distributions. Finally, the study further employed both threshold and Bayesian pooled panel kink regressions, with mixed results. The Bai-Perron test confirmed that the economic freedom index has a single threshold value of 56.70. Whereas the threshold estimates show a negative impact of economic freedom on growth in both low and high regimes, the Bayesian estimates reveal that economic freedom has a negative impact on growth in a low regime but a positive impact in a high regime. Our novel findings show that there exists a nonlinear impact of economic freedom on growth. This provides some guidance and caution in charting policy paths that seek to achieve economic growth.