IDEAS home Printed from https://ideas.repec.org/a/gam/jecomi/v9y2021i4p151-d654226.html
   My bibliography  Save this article

Forecasting for the Optimal Numbers of COVID-19 Infection to Maintain Economic Circular Flows of Thailand

Author

Listed:
  • Chanamart Intapan

    (Faculty of Agriculture, Chiang Mai University, Chiang Mai 50200, Thailand
    Modern Quantitative Economic Research Centre (MQERC), Chiang Mai University, Chiang Mai 50200, Thailand
    MICE Excellence Centre, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Chukiat Chaiboonsri

    (Modern Quantitative Economic Research Centre (MQERC), Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Pairach Piboonrungroj

    (MICE Excellence Centre, Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand)

Abstract

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.

Suggested Citation

  • Chanamart Intapan & Chukiat Chaiboonsri & Pairach Piboonrungroj, 2021. "Forecasting for the Optimal Numbers of COVID-19 Infection to Maintain Economic Circular Flows of Thailand," Economies, MDPI, vol. 9(4), pages 1-22, October.
  • Handle: RePEc:gam:jecomi:v:9:y:2021:i:4:p:151-:d:654226
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7099/9/4/151/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7099/9/4/151/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Can, Ufuk & Can, Zeynep Gizem & Bocuoglu, Mehmet Emin & Dogru, Muhammed Erkam, 2021. "The effectiveness of the post-Covid-19 recovery policies: Evidence from a simulated DSGE model for Turkey," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 694-708.
    2. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models—Rejoinder," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 211-219.
    3. J.E. Boscá & R. Doménech & J. Ferri & J.R. García & C. Ulloa, 2021. "The stabilizing effects of economic policies in Spain in times of COVID-19," Applied Economic Analysis, Emerald Group Publishing Limited, vol. 29(85), pages 4-20, January.
    4. Ng, Wung Lik, 2020. "To lockdown? When to peak? Will there be an end? A macroeconomic analysis on COVID-19 epidemic in the United States," Journal of Macroeconomics, Elsevier, vol. 65(C).
    5. Fabio Canova, 2007. "DSGE Models, Solutions, and Approximations, from Methods for Applied Macroeconomic Research," Introductory Chapters, in: Methods for Applied Macroeconomic Research, Princeton University Press.
    6. Amiri, Hossein & Sayadi, Mohammad & Mamipour, Siab, 2021. "Oil Price Shocks and Macroeconomic Outcomes; Fresh Evidences from a scenario-based NK-DSGE analysis for oil-exporting countries," Resources Policy, Elsevier, vol. 74(C).
    7. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
    8. Nakhli, Seyyed Reza & Rafat, Monireh & Dastjerdi, Rasul Bakhshi & Rafei, Meysam, 2021. "Oil sanctions and their transmission channels in the Iranian economy: A DSGE model," Resources Policy, Elsevier, vol. 70(C).
    9. Sunghae Jun, 2019. "Bayesian Structural Time Series and Regression Modeling for Sustainable Technology Management," Sustainability, MDPI, vol. 11(18), pages 1-12, September.
    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. Shah, Sayar Ahmad & Garg, Bhavesh, 2023. "Testing policy effectiveness during COVID-19: An NK-DSGE analysis," Journal of Asian Economics, Elsevier, vol. 84(C).
    2. Shah, Sayar Ahmad & Garg, Bhavesh, 2023. "Identifying efficient policy mix under different targeting regimes: A tale of two crises," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 975-994.
    3. Adnan Haider Bukhari & Safdar Ullah Khan, 2008. "A Small Open Economy DSGE Model for Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 47(4), pages 963-1008.
    4. Kocięcki, Andrzej & Kolasa, Marcin, 2023. "A solution to the global identification problem in DSGE models," Journal of Econometrics, Elsevier, vol. 236(2).
    5. Alexander Beames & Mariano Kulish & Nadine Yamout, 2022. "Fiscal Policy and the Slowdown in Trend Growth in an Open Economy," Working Papers 143, Red Nacional de Investigadores en Economía (RedNIE).
    6. Sarah Mouabbi & Jean‐Guillaume Sahuc, 2019. "Evaluating the Macroeconomic Effects of the ECB's Unconventional Monetary Policies," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(4), pages 831-858, June.
    7. Pau Rabanal, 2009. "Inflation Differentials between Spain and the EMU: A DSGE Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(6), pages 1141-1166, September.
    8. D. Siena, 2014. "The European Monetary Union and Imbalances: Is it an Anticipation Story ?," Working papers 501, Banque de France.
    9. Gary Koop & M. Hashem Pesaran & Ron P. Smith, 2013. "On Identification of Bayesian DSGE Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 300-314, July.
    10. Tatiana Kirsanova & Stephanus le Roux, 2013. "Commitment vs. Discretion in the UK: An Empirical Investigation of the Monetary and Fiscal Policy Regime," International Journal of Central Banking, International Journal of Central Banking, vol. 9(4), pages 99-152, December.
    11. Benchimol, Jonathan & Ivashchenko, Sergey, 2021. "Switching volatility in a nonlinear open economy," Journal of International Money and Finance, Elsevier, vol. 110(C).
    12. Zhicheng Zhou & Prapatchon Jariyapan, 2013. "The impact of macroeconomic policies to real estate market in People's Republic of China," The Empirical Econometrics and Quantitative Economics Letters, Faculty of Economics, Chiang Mai University, vol. 2(3), pages 75-92, September.
    13. Benchimol, Jonathan & Bounader, Lahcen, 2023. "Optimal monetary policy under bounded rationality," Journal of Financial Stability, Elsevier, vol. 67(C).
    14. Lorenzo Burlon & Paolo D'Imperio, 2019. "The euro-area output gap through the lens of a DSGE model," Questioni di Economia e Finanza (Occasional Papers) 477, Bank of Italy, Economic Research and International Relations Area.
    15. Michel Mouchart & Renzo Orsi, 2016. "Building a Structural Model: Parameterization and Structurality," Econometrics, MDPI, vol. 4(2), pages 1-16, April.
    16. Coenen, Günter & Straub, Roland & Trabandt, Mathias, 2013. "Gauging the effects of fiscal stimulus packages in the euro area," Journal of Economic Dynamics and Control, Elsevier, vol. 37(2), pages 367-386.
    17. Müller, Tobias & Christoffel, Kai & Mazelis, Falk & Montes-Galdón, Carlos, 2022. "Disciplining expectations and the forward guidance puzzle," Journal of Economic Dynamics and Control, Elsevier, vol. 137(C).
    18. Aruoba, S. Borağan & Bocola, Luigi & Schorfheide, Frank, 2017. "Assessing DSGE model nonlinearities," Journal of Economic Dynamics and Control, Elsevier, vol. 83(C), pages 34-54.
    19. Alisdair McKay, "undated". "Idiosyncratic risk, insurance, and aggregate consumption dynamics: a likelihood perspective," Boston University - Department of Economics - Working Papers Series 2013-013, Boston University - Department of Economics.
    20. Giovanni Angelini & Luca Fanelli, 2016. "Misspecification and Expectations Correction in New Keynesian DSGE Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(5), pages 623-649, October.

    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:9:y:2021:i:4:p:151-:d:654226. 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.