The efficacy of monetary and fiscal policies on economic growth: Evidence from Thailand

Pathairat Pastpipatkul; Htwe Ko Article The efficacy of monetary and fiscal policies on economic growth: Evidence from Thailand Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Pathairat Pastpipatkul; Htwe Ko (2025) : The efficacy of monetary and fiscal policies on economic growth: Evidence from Thailand, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 1, pp. 1-17, https://doi.org/10.3390/economies13010019 This Version is available at: https://hdl.handle.net/10419/329299 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Academic Editor: Gabriela Dobrot˘a Received: 20 December 2024 Revised: 10 January 2025 Accepted: 11 January 2025 Published: 15 January 2025 Citation: Pastpipatkul, P., & Ko, H. (2025). The Efficacy of Monetary and Fiscal Policies on Economic Growth: Evidence from Thailand. Economies, 13(1), 19. https://doi.org/10.3390/ economies13010019 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article The Efficacy of Monetary and Fiscal Policies on Economic Growth: Evidence from Thailand Pathairat Pastpipatkul and Htwe Ko * Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand; [email protected] *Correspondence: [email protected] Abstract: This study empirically explores the dynamic effect of MP and FP on the economic growth of Thailand from Q1:2003 to Q2:2024. In this study, data analysis was conducted using an advanced sequence of the econometric modeling approach to guarantee that the estimated results were more consistent and reliable. First, we used Bayesian additive regression trees (BART) and Bayesian variable selection (BASAD) methods to determine macro factors with the highest probabilities influencing growth, in addition to monetary and fiscal policy tools during the studied periods. Second, we used the time-varying coefficients seemingly unrelated equation (TVSURE) model to examine the economic impact of MP and FP. Last, we also employed the Markov switching regression (MSR) model not only to support the findings from the TVSURE model but also to propose policy recommendations based on regime durations and transitions tempted by MP and FP. The main results from both TVSURE and MSR reveal the following: (1) MP is more consistent with expected growth outcomes while FP is stronger when localized, (2) MP is more effective in sustaining long periods of high growth, (3) FP is significantly stronger in recovering from recessions, and (4) the coordination of MP and FP has a similar performance to MP alone but with shorter transition periods. This study makes an empirical contribution to the ongoing debate on the effectiveness of MP and FP in boosting growth and aiding in the recovery from recessions in the case of Thailand. In addition, this study not only acknowledged certain limitations but also recommended policies to sustain the Thai economy. Keywords: monetary policy; fiscal policy; growth; TVSURE; Markov switching; Thailand 1. Introduction Monetary and fiscal policies are implemented as critical instruments to drive a nation toward economic prosperity. This paper agrees that monetary policy (MP) and fiscal policy (FP) are essential for maintaining sustainable economic growth, moderating inflation, ensuring debt sustainability, and achieving balanced public finances. For many countries, governments are responsible for fiscal tools and central banks, while, on the other hand, handling monetary tools to respond to any external shocks such as global financial and health crises. These kinds of crises are truly detrimental to the economy. For example, following the coronavirus (COVID-19) pandemic, China’s coordinated economic revival produced significant spillover effects globally that affected not only the economic growth but energy demand in upper-middle and also high-income nations (Yuan et al.,2022). In modern days, the combination of MP and FP plus favorable investment conditions is quite important for output growth and social development in line with the long-term growth path. In fact, Behera et al. (2024) argued the need for an effective strategy to address extreme events like the COVID-19 pandemic. Fundamentally, MP and FP share the same Economies 2025,13, 19 https://doi.org/10.3390/economies13010019 Economies 2025,13, 19 2 of 17 objective of stimulating public welfare, similar to other mechanisms of public policies. Moreover, one study found evidence of a substitution association between policies (Afonso et al.,2019). Governments and central banks must collaborate closely to take actions that prevent the significant decline in economic activities. Fiscal sustainability is essential in supporting the initiatives of monetary authorities. Central banks, through implementing monetary policies, aim to safeguard price stability and governments through adjusting fiscal policies that regulate more revenue, propriate expenditures, and avoid budget deficits to create satisfactory conditions for economic development. While MP and FP may have different goals, a moderate monetary expansion should be implemented alongside a balanced fiscal policy except in extraordinary economic circumstances (Chugunov et al.,2021). For instance, central banks in advanced economies shifted to unconventional measures due to the zero lower bound, while fiscal tools showed limited consolidation efforts after the 2008 financial crisis (Silva & Vieira,2017). In addition, Arora et al. (2022) supported the use of an optimal combination of MP and FP to achieve sustainable growth, particularly in terms of demand growth and price stability. Their study suggested that FP should take a leading role with monetary and trade policies, acting through adjustments as needed. They argued that this policy coordination can stabilize the economy and growth for the long term based on the quarterly dataset of India from Q1:1996–97 and Q4:2019–2020. While both monetary policy (MP) and fiscal policy (FP) are widely considered as significant contributors towards improving the macroeconomic stability, their efficacy in sustaining growth for the Thai economy remains insufficiently explored. Moreover, given the challenges Thailand faces, such as household debt, aging population, inflation, political uncertainty, and global economic headwinds, the country also pursues opportunities in digital transformation and sustainable development. However, no empirical study has been conducted on the efficacy of MP and FP following these crises in Thailand. Therefore, we use the latest available data to examine the effects of monetary and fiscal policies along with other macro variables on the Thai economy. The empirical findings are obtained through this present study by using TVSURE and MSR models; this study examines the effectiveness of MP in sustaining long-term growth and FP in facilitating a rapid recovery during economic downturns in the case of Thailand. This paper contributes to the existing empirical literature by exploring the macro effects of MP and FP shocks, with a focus on tracking economic growth and the potential influence of a comprehensive set of variables. This paper is structured as follows: it begins with a review of recent empirical studies, followed by a section on the data and methods of study used for data analysis. The results and discussions are then presented. Finally, conclusions and policy recommendations are provided at the end. 2. Recent Empirical Studies on MP and FP in Relation to Growth There has been extensive empirical research on the relationship between monetary policy (MP), fiscal policy (FP), and economic growth across different countries. However, the existing literature presents inconsistencies, with some studies showing positive impacts, while others report negative and natural relationships. As Andini (2024) notes, the nexus between these policies and growth depends on many factors including heterogeneity, sample size, research methods, control variables, and other relative elements. These varying factors may be a cause behind the mixed findings observed in the literature. In this section, we reviewed some of the recent empirical studies in this field to identify the gaps this study seeks to fill. One of the most relevant empirical studies conducted by Tan et al. (2020) found the existence of a negative relationship between economic growth and the money market rate (as a tool of MP) and a positive link with government spending (as a tool of FP) in Thailand Economies 2025,13, 19 3 of 17 from Q1:1990 to Q1:2017. They concluded that FP is more effective in Thailand compared to Malaysia and Singapore. But this conclusion appears biased as it only considered two explanatory factors impacting the real GDP. Additionally, the results showed the outcomes are asymmetric, with MP and FP being mutually dependent. In contrast, Chugunov et al. (2021) empirically investigated the impact of FP and MP on economic growth in 19 emerging countries from 1995 to 2018. They found (1) the general government spending has a negative relationship with the per capital GDP growth, (2) the effect of public spending on economic development depends on three factors like institutions’ quality, expenditure composition, and fiscal architecture, and (3) there is a proven need to maximize the share of productive expenditures. Based on these results, they recommended using adaptive tools in MP to achieve both intermediate and final inflation targets. Similarly, Ozili (2024) used secondary annual data of 22 countries between 2011 and 2018 to investigate the impact of monetary, fiscal, and regulatory policies on sustainable development while controlling for economic growth. His findings showed the following: (1) economic policy improves sustainable development in developing and non-European countries but has a negative effect in developed and European countries, (2) expansionary MP supports SDG6, (3) expansionary fiscal policy boots SDG3: Good Health and Well-Being and SDG7: Affordable and Clean Energy but harms SDG6: Clean Water and Sanitation, (4) regulatory policy that is being designed to improve governance enhances SDG3 and SDG6, and (5) changes to these policies lead to changes in sustainable development. Regarding exchange rate regimes, one study conducted by Ito and Kawai (2024) found that (1) monetary policy is effective in increasing real GDP growth under a flexible exchange rate regime but not under financially open fixed rate regime, (2) MP is most effective in curbing inflation and inflation volatility under a flexible exchange rate regime, and (3) FP positively affects GDP growth under a flexible exchange rate regime, and inflation volatility under a financially closed fixed rate regime helps achieve price stability in financially open economies. These findings were based on the sample of 61 developing and emerging economies from 1971 to 2020. When considering monetary and fiscal policies influencing sustainable development, it varies across countries and regions. Although the qualitative document analysis of Abeysekera (2024) on the role of monetary, fiscal, and public policies on SDGs before and after the COVID-19 pandemic, public policy was seen as a significant driver of sustainable development in Sri Lanka. In contrast, MP and FP were primarily directed toward economic recovery efforts. Dinh et al. (2024) examined the effect of MP and FP on sustainable development from 2005 to 2020 using a panel dataset of 33 developing and 7 developed countries. The study found that MP as measured by the money supply and inflation negatively affects sustainable development, while foreign exchange reserves and financial stability have a positive impact with probabilities of 89.6 percent in developed and 92.5 percent in developing countries. FP as measured by government spending positively supports sustainable development with a probability of 99.7 percent in both developed and developing countries. Meanwhile, tax income increases sustainable development with a 100 percent probability in developed countries but shows a 60.9 percent probability of a negative effect in developing countries. Demirtas (2023) studied the effectiveness of expansionary MP and FP in 55 developed and 55 developing countries from 2007 to 2026 by using an Arellano–Bond GMM model. The results showed that MP is more effective than FP in developed countries and FP in developing countries supports aggregate demand. Moreover, Azad et al. (2021) explored the economic impact of MP and FP for Canada from Q1:1990 to Q4:2020 by using the regime-switching model and structural VAR model. They found that FP has been more active than MP for boosting short-term economic activity, but causes rising interest rates, lower investment, and higher inflation in the long term. Furthermore, Batayneh et al. (2024) Economies 2025,13, 19 4 of 17 empirically investigated MP and FP on US economic growth from Q1:1964 to Q3:2021 by using an unrestricted VAR model. They found that the federal budget deficit (FP) and money supply, federal funds rate, and exchange rate (MP) have no long-term relationship with growth. In the short term, expansionary MP and FP positively affect economic growth, while the exchange rate shows no significant effect. Additionally, the financial crisis and COVID-19 pandemic were found to have a negative impact on the US economy for the studied periods. Furthermore, Nuru (2020) studied the effects of MP and FP shocks in the South African economy by using a structural vector autoregressive (SVAR) model for the periods of Q2:1994 to Q2:2014. His findings showed that MP tightening reduces real economic activities and leads to exchange rate depreciation. For FP, the government spending multiplier increased as the tax multiplier was close to zero on impact and statistically insignificant. His study emphasized the presence of the important role of MP and FP in influencing economic activities and pollical decision-making. In addition, Adegboyo et al. (2021) found that FP stimulates Nigeria’s economic growth in the long run with government spending but this is not consistent in the short run. Government revenue, however, was not found to affect growth. On the other hand, monetary policies showed that the money supply does not affect growth, but an increase in interest rates positively influence growth between 1985 and 2020. The authors suggested that policymakers use interest rates and fiscal policies to boost short-term economic growth for Nigeria. From 1960 to 2020 in Nigeria, the exchange rate and money supply had a positive and significant link with growth, while the interest rate showed a positive but insignificant relationship and the inflation rate had a negative and insignificant impact (Donald et al.,2024). Moreover, Isaiah et al. (2024) and Dodo et al. (2024) both examined the economic impact of MP and FP for the same country (Nigeria) for the periods between 1991 and 2022 and from 1981 to 2017, respectively. FP had a positive and significant effect on growth while MP negatively affects it (Dodo et al.,2024). Isaiah et al. (2024) noted MP was weak compared to FP in its impact on Nigeria’s economy while resulting in high inflation and exchange rate volatility. Mwale and Mulenga (2024) also found that 1 percent increases in tax revenue have a positive and significant long-term impact on growth by 3.36 percent, while external debt and public expenditure have negative effects by 1.17 percent and 0.003 percent, correspondingly, between 1991 and 2021 in Nigeria. In the short term, it showed 1 percent increases in tax revenue causes a growth decline by 0.003 percent and 6.14 percent, respectively. Last but not least, Andini (2024) also argued that the economic growth–fiscal policy nexus can be varied, showing positive, negative, or neutral results depending on factors such as heterogeneous conditions, intermediate variables, research methods, sample size, and development level of countries studied, and many more. The study found the effect of government spending, taxation, and debt on growth remains unclear, including in reports by Nguyen et al. (2024), whereas they found FP as measured by public expenditure had a larger impact on economic growth than MP as measured by broad money (M2) between 1996 and 2021 in Vietnam. In addition, Aisyah et al. (2024) examined the FP-economic growth nexus in Indonesia and found a close and positive relationship. There was a longrun positive nexus between increases and decreases in government spending and economic growth between 1970 and 2019 in Somalia (Ali et al.,2024). In another empirical study by Kim et al. (2021) on a similar topic for the case of China, they found local spending has a larger impact on output growth than central spending between 1985 and 2016. It also showed a shift from infrastructure investments to R&D-driven growth in recent years. Net taxes and public debt in China were also found to influence the long-run growth. The existing literature reviewed showed the mixed findings of the economic impact of MP and FP for different economies. Moreover, most of them tend to narrowly focus Economies 2025,13, 19 5 of 17 on either MP or FP alone so that they seem to overlook and ignore the consideration of various macroeconomic factors. We believe these findings may be subject to significant biases. The need for the effective and careful coordination of MP and FP in order to reduce threats to macroeconomic stability is clear. Therefore, this study adopts a monetary–fiscal mix approach to more comprehensively examine the effects of these policies on the Thai economy between the first quarter of 2003 to the second quarter of 2024 by using an advanced sequence of econometric modeling methods. 3. Research Methodology In this study, data analysis was conducted using an advanced sequence of econometric modeling approach to guarantee that the estimated results were more consistent and reliable. We initially used Bayesian additive regression trees (BART) and Bayesian variable selection (BASAD) methods to determine macro factors with the highest probabilities of influencing growth in addition to monetary and fiscal policy tools during the studied periods. While both the BART and BASAD methods are still weak at choosing between relevant and irrelevant variables, the final selection of variables was based on a mix-order approach between the two models. This way, we improve the consistency of both the BART and BASAD models by selecting between relevant and irrelevant variables for the final variable selection based on a mix-order technique between the two models. Secondly, we proceeded the time-varying coefficients seemingly unrelated equation (TVSURE) model to examine the economic impact of MP and FP. Finally, we used the Markov switching regression (MSR) model not only to support the findings from the TVSURE model but also to propose policy recommendations based on regime durations and transitions. In order to support the findings from the time-varying seemingly unrelated regression equations (TVSURE) model and to determine whether MP, FP, or the coordination of MP and FP is more effective in sustaining growth, we developed three empirical equations to further examine the regime-shifting effect of MP and FP on the Thai economy. For this to be achieved, we employed the Markov switching regression (MSR) model. Since TVSURE and MSR are linear models, we applied a log transformation to the final selected variables of the study in order to linearize the relationships between the variables. This way, it helps us to better fit the model by making nonlinear relationships more linear and by stabilizing the variance. Figure 1represents the conceptual framework of the study. Economies2025,13,xFORPEERREVIEW5of17  tomacroeconomicstabilityisclear.Therefore,thisstudyadoptsamonetary–fiscalmix approachtomorecomprehensivelyexaminetheeffectsofthesepoliciesontheThaiecon‐ omybetweenthefirstquarterof2003tothesecondquarterof2024byusinganadvanced sequenceofeconometricmodelingmethods. 3.ResearchMethodology Inthisstudy,dataanalysiswasconductedusinganadvancedsequenceofeconometric modelingapproachtoguaranteethattheestimatedresultsweremoreconsistentandrelia‐ ble.WeinitiallyusedBayesianadditiveregressiontrees(BART)andBayesianvariablese‐ lection(BASAD)methodstodeterminemacrofactorswiththehighestprobabilitiesofinflu‐ encinggrowthinadditiontomonetaryandfiscalpolicytoolsduringthestudiedperiods. WhileboththeBARTandBASADmethodsarestillweakatchoosingbetweenrelevantand irrelevantvariables,thefinalselectionofvariableswasbasedonamix‐orderapproachbe‐ tweenthetwomodels.Thisway,weimprovetheconsistencyofboththeBARTandBASAD modelsbyselectingbetweenrelevantandirrelevantvariablesforthefinalvariableselection basedonamix‐ordertechniquebetweenthetwomodels.Secondly,weproceededthetime‐ varyingcoefficientsseeminglyunrelatedequation(TVSURE)modeltoexaminetheeco‐ nomicimpactofMPandFP.Finally,weusedtheMarkovswitchingregression(MSR)model notonlytosupportthefindingsfromtheTVSUREmodelbutalsotoproposepolicyrecom‐ mendationsbasedonregimedurationsandtransitions.Inordertosupportthefindings fromthetime‐varyingseeminglyunrelatedregressionequations(TVSURE)modelandto determinewhetherMP,FP,orthecoordinationofMPandFPismoreeffectiveinsustaining growth,wedevelopedthreeempiricalequationstofurtherexaminetheregime‐shiftingef‐ fectofMPandFPontheThaieconomy.Forthistobeachieved,weemployedtheMarkov switchingregression(MSR)model.SinceTVSUREandMSRarelinearmodels,weapplied alogtransformationtothefinalselectedvariablesofthestudyinordertolinearizethere‐ lationshipsbetweenthevariables.Thisway,ithelpsustobetterfitthemodelbymaking nonlinearrelationshipsmorelinearandbystabilizingthevariance.Figure1represents theconceptualframeworkofthestudy.  Figure 1. Conceptual framework of this study. Economies 2025,13, 19 6 of 17 3.1. Bayesian Additive Regression Trees (BART) Model BART is a method that is being used to determine potential input predictors influencing the dependent variable of the study based on the posterior inclusion probabilities of variables. This model is a non-parametric one, by incorporating the Bayesian technique, first developed by Chipman et al. (2010) and which is later implemented in R version 4.4.1. by Kapelner and Bleich (2016). The model has both advantages and disadvantages. One of the pros of using this model is being able to handle nonlinear datasets. This model consists of three components: 1. additive trees; 2. prior specification; and 3. a stochastic process for posterior distribution so it is also known as the sum of trees. Variables are ranked based on their average posterior inclusion probabilities. One of the cons is that this model only produces a small fraction of the variation of the overall relationship that can be explained. In this study, we used the “BartMachine” package in RStudio version 4.4.1 in order to apply this method. 3.2. Bayesian Variable Selection (BASAD) Model As proposed, additionally, we used the “basad” package in RStudio to select the variables with higher posterior inclusion probabilities, influencing the dependent variables of this study. This method was first introduced by Narisetty and He (2014), which was later implemented in R programming by Xiang and Narisetty (2022). It also results in the same performance as the BART model by combining adaptive sampling with a Bayesian framework. This method can be computationally intensive when the model space is large and sensitive to the choice of priors. Nevertheless, it is a useful tool with which to determine the most relevant variables where there is uncertainty about the model structure or parameters. Since both BART and BASAD have their own weaknesses and strengths, this study will adopt a mix-order selection approach utilizing these two methods in choosing the most influential input variables, in addition to MP and FP tools. 3.3. Time-Varying Coefficients Seemingly Unrelated Equation (TVSURE) Model Seemingly unrelated equations (SURE) models developed by Zellner (1962) are extensions of linear regressions to a multi-equation framework. In this study, a TVSURE with two equations model was employed following Casas and Fernandez-Casal (2022). This model wraps time-varying ordinary least squares (tvOLS) and time-varying generalized least squares (tvGLS) to estimate the coefficients of TVSURE. One of the advantages of this model is that it can estimate each equation independently, assuming no cross-equation error correlations. In our SURE model, the following two equations were included: lgdprt=α10 +α11lm2t+α12lpirst+α13lin f t+α14lusdtobahtt+α15lunempt+ε1t(1) lgdprt=α20 +α21lgovrevt+α22lgovexpt+α23lgovdebtt+α24lunempt+ε2t(2) where lgdpr = the logarithm of the GDP growth rate, lm2 = the logarithm of the money supply (m2), lpirs = the logarithm of the policy interest rates, linf = the logarithm of the consumer price inflation, lusdtobaht = the logarithm of the exchange rate (US dollar against Thai baht), and lunemp = the logarithm of the unemployment rate (variable selected by the mix-order variable selection approach). In this model we applied, the time-varying variance–covariance matrix of two or more series is estimated nonparametrically using the ‘tvReg’ package. E( γit,γi′t′)=σii′t when Economies 2025,13, 19 7 of 17 t = t’, and zero otherwise. This allows the variance–covariance matrix to be time-varying with the following expression at any given time t (quarter): Σt=       σ11t σN1t . . . σN1t σ12t σ22t ... σN2t · · · · · · · · · σ1Nt σ2Nt . . . σNNt       (3) Since the matrix is assumed to be locally stationary, its linear estimator is defined by the following: vech∼ Στ= T ∑ t=1 vech(γτ tγt)Kh(t−τ)s2−s1(τ−t) s0s2−s2 1 (4) where sj=∑T t=1τ−t)jKb(τ−t) for j = 0, 1, 2; Kb(·) is a symmetric kernel function that assigns a higher weight to values close to the focal point ( τ = t/T); and b is the bandwidth parameter. A single bandwidth is used across all co-movements, ensuring that ∼ Στ remains positive-definite. 3.4. Markov Switching Regression (MSR) Model In order to support the findings from the TVSURE model, we further conducted a regime-switching analysis that was first proposed by Goldfeld and Quandt (1973) and Hamilton (1989), and further found in Perlin (2015). Following Sanchez-Espigares and Lopez-Moreno (2022), we used the ‘MSwM’ package in RStudio to estimate the regime shifting relationships between policies and growth. Let the Markov switching regression (MSR) model with two regimes be expressed as follows: γ1,t=β1,1rt∗x1,t+β1,2rt∗x2,t+· · · +β1,irt∗xi,t+ϵ1,t(5) γ2,t=β2,1rt∗x1,t+β2,2rt∗x2,t+· · · +β2,irt∗xi,t+ϵ2,t(6) with ϵ1,t∼N0, σ2 1,r1(7) ϵ2,t∼N0, σ2 2,r2(8) Covariance(ϵ1,t,ϵ2,t)=0 (9) where γ1,tand γ2,t represent dependent variables, xi,t represents all input predictors or independent variables, βi represents the mean of the parameters to be estimated, σ1 , σ2 are residuals of regime 1 and regime 2, and ϵ1,t,ϵ2,tare the error terms. 4. Data and Empirical Results 4.1. Data Descriptive The study used the secondary quarterly time series data as obtained from the CEIC database through the Chiang Mai University Network for the periods of Q1:2003 to Q2:2024. The analysis focused on economic growth measured as GDP growth as the dependent variable and included a range of independent variables arranged by the policy domain. Broad money, the policy interest rate, the inflation rate, and the exchange rate were considered as MP tools while government revenue, expenditure, and debt were taken as FP tools. Intermediate factors were also considered as they have potential indirect and direct effects on the economic growth of Thailand; these are consumer confidence in the present and future, gold price, diesel price, gasoline 95 price, imports, exports, household debt, unemployment Economies 2025,13, 19 8 of 17 rate, foreign direct investment, electricity generation, and a dummy variable to account for the economic impact of the 2008 financial crisis and the recent COVID-19 pandemic. The selection of policy variables in this study is driven by the existing literature emphasizing their theoretical relevance and empirical significance in macroeconomic research. Variables such as the consumer confidence index (present and future) were included to account for household and business expectations while the gold price serves as a proxy for financial stability and inflationary trends. Imports and exports measure trade openness and external demand, while diesel and gasoline prices represent input costs influencing production and inflation. Variables such as household debt, unemployment, FDI, and electricity generation were included as they reflect domestic credit conditions, labor market dynamics, investment growth, and industrial activities, respectively. A dummy variable for external shocks accounts for major disruptions including the 2008 financial crisis and COVID-19 pandemic. However, the final input predictors in addition to the core MP and FP variables were determined by using a mix-order variable selection approach based on Bayesian variable selection methods. A brief summary of each variable covering its data source, symbol, and unit of measurement is provided in Table A1 (Appendix A). Table 1provides summary statistics of variables. Most key variables are normally distributed random numbers from the Jarque–Bera test. Table 1. Summary statistics of variables. Variable Mean Median Maximum Std. Dev. Skewness Kurtosis Jarque-Bera Probability GDPR 3.104535 3.330000 15.37000 3.7072711 − 0.664509 6.889547 60.53995 0.000000 M2 461,919.9 494,137.5 764,771.3 190,260 − 0.069521 1.713010 6.004507 0.050204 PIRS 2.037442 1.710000 5.00000 1.086429 0.853642 3.342133 10.86422 10.86422 INF 94.06686 95.18500 104.7100 6.854254 − 0.254482 1.812263 6.983317 0.050205 USDTOBAHT 34.25605 33.37000 42.75000 29.81000 0.863062 2.896293 10.71510 0.004712 GOVREV 5171.011 5424.320 8502.670 1758.804 − 0.186334 2.107033 3.354971 0.186843 GOVEXP 15,342.19 16,721.17 24,405.23 5861.325 − 0.376450 1.894641 6.409423 0.040571 GOVDEBT 129,198.2 119,179.3 278,724.1 73,986.53 0.691077 2.350258 8.358168 0.015313 HHDEBT 70.62814 79.98000 95.51000 18.49546 − 0.335635 1.524223 9.418872 0.009010 CCIP 59.44593 59.03500 98.7000 17.41705 0.069541 2.604494 0.6294494 0.729848 CCIF 79.66221 81.75000 113.37000 13.57932 − 0.485427 3.424758 4.02400 0.133721 GOLDPR 580.4464 600.4900 1106.380 235.4623 − 0.170788 2.172168 2.873760 0.237668 DIESELPR 0.782558 0.845000 1.120000 0.195918 − 0.929205 3.070649 12.39361 0.002036 GASOLINEPR 1.073256 1.120000 1.610000 0.321780 − 0.480058 2.534044 4.081194 0.129951 IMPORT 60.63372 61.62500 76.82000 7.265874 − 0.077723 2.603427 0.650138 0.722478 EXPORT 65.69779 66.69500 78.38000 5.293297 − 0.973120 5.157833 30.25801 0.00000 UNEMP 1.271512 1.125000 2.87000 0.523930 1.105516 3.854745 20.13565 0.000042 FDI 2.422791 2.840000 6.4700000 2.245742 − 1.733698 8.868026 166.4693 0.00000 ECG 44,515.54 45,333.56 63,721.96 8407.443 − 0.098914 2.018411 3.592838 0.165892 EXTS 0.220930 0.00000 1.00000 0.417307 1.34524 2.809898 26.07137 0.000002 Source: authors’ own estimation. 4.2. Variables Selection by Using BART, BASAD, and Mix-Order Approaches According to the literature, there are many factors directly and indirectly affecting economic growth, but there is uncertainty about factors at significant levels for specific time periods. We, therefore, first used the Bayesian addictive regression trees (BART) method to find which of the input control variables most significantly influenced growth in Thailand during the studied periods. Secondly, we applied the Bayesian variable selection with adaptive method (BASAD) to enhance the variable selection, and, finally, conducted a mixorder selection utilizing BART and BASAD. Both methods are based on posterior inclusion probabilities of input predictors that explain the dependent variable of the study. In this way, we can assure a good variable selection approach before proceeding with the final estimation. In Table 2, the variables are shown in the order of their inclusion probabilities, Economies 2025,13, 19 15 of 17 Appendix A Table A1. Source of data, symbol, and unit of measurement of variables used in this study. Variables Symbol Unit Source Real Gross Domestic Product Growth GDPR % CEIC, https://www.ceicdata.com (accessed on 2 December 2024) Money Supply, M2 (Broad Money) M2 US dollar million CEIC, https://www.ceicdata.com Policy Interest Rate PIRS % CEIC, https://www.ceicdata.com Inflation, Consumer Price Index INF 2019 = 100 CEIC, https://www.ceicdata.com Exchange Rate (1 US dollar to Baht) USDTOBAHT Baht per USD CEIC, https://www.ceicdata.com Government Revenue GOVREV US dollar million CEIC, https://www.ceicdata.com Government Expenditure GOVEXP US dollar million CEIC, https://www.ceicdata.com Government Debt GOVDEBT US dollar million CEIC, https://www.ceicdata.com Consumer Confidence Index: Present CCIP Point CEIC, https://www.ceicdata.com Consumer Confidence Index: Future CCIF Point CEIC, https://www.ceicdata.com Gold Price, 99.99% pure standard GOLDPR US dollar CEIC, https://www.ceicdata.com Diesel Price, base wholesale value DIESELPR US dollar CEIC, https://www.ceicdata.com Gasoline 95 price, base retail value GASOLINEPR US dollar CEIC, https://www.ceicdata.com Imports % of Nominal GDP IMPORT % CEIC, https://www.ceicdata.com Exports % of Nominal GDP EXPORT % CEIC, https://www.ceicdata.com Household Debt % of Nominal GDP HHDEBT % CEIC, https://www.ceicdata.com Unemployment Rate UNEMP % CEIC, https://www.ceicdata.com FDI % of Nominal GDP FDI % CEIC, https://www.ceicdata.com Electricity Generation total Gwh ECG Gwh CEIC, https://www.ceicdata.com External Shocks Dummy Variable aEXTS 0 and 1 CEIC, https://www.ceicdata.com Note: a indicates the presence and absence of external shock events, whereas 0 indicates absent periods with no financial crisis and COVID-19 pandemic, and 1 indicates present periods of financial crisis which occurred in Q1:2007 and Q4:2008 worldwide and COVID-19 impact between Q1:2020 and Q4:2021. Table A2. Regime-switching relationships between fiscal policy and economic growth in Thailand. Variable High-Growth Regime Slow-Growth Regime Estimate Std. Error T-Value p-Value Estimate Std. Error T-Value p-Value Intercept 7.1710 4.9724 1.4422 0.1492 3.4056 1.1901 2.8616 0.004 ** Log (Govrev) 0.4867 1.0815 0.4500 0.6527 −0.4190 0.1972 −2.1247 0.033 * Log (Govexp) −1.0422 2.6324 −0.3959 0.6922 1.3432 0.2723 4.9328 0.00 *** Log (Govdebt) −0.0134 1.5557 −0.0086 0.9931 −0.9901 0.1422 −6.9627 0.00 *** Log (Unemp) −0.0228 0.7916 −0.0288 0.9770 0.8976 0.1568 5.7245 0.00 *** Residual 0.82 0.17 Notes: * statistically significant; ** strongly significant; and *** highly significant. Table A3. Regime-switching relationships between monetary policy and economic growth in Thailand. Variable High-Growth Regime Slow-Growth Regime Estimate Std. Error T-Value p-Value Estimate Std. Error T-Value p-Value Intercept 49.3401 32.0146 1.5412 0.1233 32.3281 1.3251 24.3967 0.00 *** Log (M2) −0.9626 3.0892 −0.3116 0.7553 1.7602 0.0823 21.3876 0.00 *** Log (Pirs) −0.8359 0.8221 −1.0168 0.3092 −0.2371 0.0799 −2.9675 0.003 ** Log (Inf) −5.9509 16.3619 −0.3637 0.7161 −12.9511 0.2360 −54.8775 0.00 *** Log (Usdtobaht) −2.3175 5.7257 −0.3637 0.7161 1.4545 0.3478 4.1820 0.00 *** Log (Unemp) −0.4771 1.1489 −0.4153 0.6779 0.3199 0.1571 2.0363 0.041 * Residual 0.86 0.22 Notes: * statistically significant; ** strongly significant; and *** highly significant. Economies 2025,13, 19 16 of 17 Table A4. Regime-switching relationships between monetary and fiscal policies and economic growth in Thailand. Variable High-Growth Regime Slow-Growth Regime Estimate Std. Error T-Value p-Value Estimate Std. Error T-Value p-Value Intercept 153.2249 19.1336 8.0082 0.00 *** 12.4480 1.6729 7.4410 0.00 *** Log (Govrev) 0.6956 1.2370 0.5623 0.5739 0.0718 0.1951 0.3680 0.7128 Log (Govexp) −0.4243 3.3246 −0.1276 0.8984 −0.0622 0.2800 −0.2221 0.8242 Log (Govdebt) 7.5714 2.1502 3.5213 0.00 *** −1.3462 0.1217 −11.0616 0.00 *** Log (M2) −6.7573 3.0856 −2.1899 0.028 * 3.0902 0.1029 30.0311 0.00 *** Log (Pirs) −0.0336 0.7302 −0.0460 0.9633 −0.1281 0.0703 −1.8222 0.0684 Log (Inf) −29.6392 10.1052 −2.9331 0.003 ** −9.6450 0.2771 −34.8069 0.00 *** Log (Usdtobaht) −5.6265 1.8111 −3.1067 0.001 ** 2.3378 0.3594 6.5047 0.00 *** Log (Unemp) −1.0373 1.1267 −0.9207 0.3572 0.4934 0.1594 3.0954 0.001 ** Residual 0.75 0.17 Notes: * statistically significant; ** strongly significant; and *** highly significant. References Abeysekera, I. (2024). The influence of fiscal, monetary, and public policies on sustainable development in Sri Lanka. Sustainability, 16, 580. [CrossRef] Adegboyo, O. S., Keji, S. 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