IDEAS home Printed from https://ideas.repec.org/a/gam/jecomi/v13y2025i1p19-d1567185.html
   My bibliography  Save this article

The Efficacy of Monetary and Fiscal Policies on Economic Growth: Evidence from Thailand

Author

Listed:
  • Pathairat Pastpipatkul

    (Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Htwe Ko

    (Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand)

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.

Suggested Citation

  • Pathairat Pastpipatkul & Htwe Ko, 2025. "The Efficacy of Monetary and Fiscal Policies on Economic Growth: Evidence from Thailand," Economies, MDPI, vol. 13(1), pages 1-17, January.
    • Handle: RePEc:gam:jecomi:v:13:y:2025:i:1:p:19-:d:1567185
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7099/13/1/19/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7099/13/1/19/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abdullahi Osman Ali & Jama Mohamed & Mohamed Osman Mohamed, 2024. "Asymmetric modeling of the fiscal policy–economic growth nexus in Somalia," Cogent Economics & Finance, Taylor & Francis Journals, vol. 12(1), pages 2312372-231, December.
    2. Indra Abeysekera, 2024. "The Influence of Fiscal, Monetary, and Public Policies on Sustainable Development in Sri Lanka," Sustainability, MDPI, vol. 16(2), pages 1-28, January.
    3. Naser Yenus Nuru, 2020. "Monetary and fiscal policy effects in South African economy," African Journal of Economic and Management Studies, Emerald Group Publishing Limited, vol. 11(4), pages 625-638, May.
    4. Behera, Chinmaya & Rath, Badri Narayan & Mishra, Pramod Kumar, 2024. "The impact of monetary and fiscal stimulus on stock returns during the COVID-19 Pandemic," Journal of Asian Economics, Elsevier, vol. 90(C).
    5. Baihua Yuan & Wang Leiling & Hayot Berk Saydaliev & Vishal Dagar & Ángel Acevedo-Duque, 2022. "Testing the impact of fiscal policies for economic recovery: does monetary policy act as catalytic tool for economic Survival," Economic Change and Restructuring, Springer, vol. 55(4), pages 2215-2235, November.
    6. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xi, Xiaojing & Mamon, Rogemar, 2011. "Parameter estimation of an asset price model driven by a weak hidden Markov chain," Economic Modelling, Elsevier, vol. 28(1-2), pages 36-46, January.
    2. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    3. Nemati, Mehdi & Saghaian, Sayed H., 2016. "Dynamics of Price Adjustment in Qualitatively Differentiated Markets in the U.S.: The Case of Organic and Conventional Apples," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 229950, Southern Agricultural Economics Association.
    4. Olawale Awe O. & Adedayo Adepoju A., 2018. "Modified Recursive Bayesian Algorithm For Estimating Time-Varying Parameters In Dynamic Linear Models," Statistics in Transition New Series, Statistics Poland, vol. 19(2), pages 258-293, June.
    5. Freebairn, John W. & Rausser, Gordon C., 1974. "Updating Parameter Estimates: A Least Squares Approach with an Application to the Inventory of Beef Cows," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 42(02), pages 1-17, June.
    6. Elvio Accinelli & Juan Gabriel Brida, 2007. "Modelos económicos con múltiples regímenes," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 1(2), pages 96-115.
    7. Aastveit, Knut Are & Jore, Anne Sofie & Ravazzolo, Francesco, 2016. "Identification and real-time forecasting of Norwegian business cycles," International Journal of Forecasting, Elsevier, vol. 32(2), pages 283-292.
    8. Wang, Hong & Forbes, Catherine S. & Fenech, Jean-Pierre & Vaz, John, 2020. "The determinants of bank loan recovery rates in good times and bad – New evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 875-897.
    9. Kal, Süleyman Hilmi & Arslaner, Ferhat & Arslaner, Nuran, 2015. "The dynamic relationship between stock, bond and foreign exchange markets," Economic Systems, Elsevier, vol. 39(4), pages 592-607.
    10. Wan-Lun Wang, 2019. "Mixture of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 196-222, March.
    11. Hunt, Julien & Devolder, Pierre, 2011. "Semi Markov regime switching interest rate models and minimal entropy measure," LIDAM Discussion Papers ISBA 2011010, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    12. Wong, Jian Cheng & Lian, Heng & Cheong, Siew Ann, 2009. "Detecting macroeconomic phases in the Dow Jones Industrial Average time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(21), pages 4635-4645.
    13. Wajeeh Mustafa Sarsour & Shamsul Rijal Muhammad Sabri, 2020. "A Simulation Study: Obtaining a Sufficient Sample Size of Discrete-Time Markov Chains of Investment in a Short Frequency of Time," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 10(8), pages 906-919, August.
    14. Houda Rharrabti Zaid, 2015. "Transmission du stress financier de la zone euro aux Pays de l’Europe Centrale et Orientale," EconomiX Working Papers 2015-37, University of Paris Nanterre, EconomiX.
    15. repec:wsr:wpaper:y:2010:i:057 is not listed on IDEAS
    16. Gaia Garino & Lucio Sarno, 2004. "Speculative Bubbles in U.K. House Prices: Some New Evidence," Southern Economic Journal, John Wiley & Sons, vol. 70(4), pages 777-795, April.
    17. Dmitry Kulikov, 2012. "Testing for Rational Speculative Bubbles on the Estonian Stock Market," Research in Economics and Business: Central and Eastern Europe, Tallinn School of Economics and Business Administration, Tallinn University of Technology, vol. 4(1).
    18. Yao, Weixin & Wei, Yan & Yu, Chun, 2014. "Robust mixture regression using the t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 116-127.
    19. Chen, Shu-Ling & Miranda, Mario J., 2006. "Modeling Yield Distribution In High Risk Counties: Application To Texas Upland Cotton," 2006 Annual meeting, July 23-26, Long Beach, CA 21392, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    20. Masaru Chiba, 2023. "Robust and efficient specification tests in Markov-switching autoregressive models," Statistical Inference for Stochastic Processes, Springer, vol. 26(1), pages 99-137, April.
    21. Gao, Huan & Mamon, Rogemar & Liu, Xiaoming & Tenyakov, Anton, 2015. "Mortality modelling with regime-switching for the valuation of a guaranteed annuity option," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 108-120.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jecomi:v:13:y:2025:i:1:p:19-:d:1567185. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.