IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v140y2020ics0960077920305920.html
   My bibliography  Save this article

Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian Structural Time Series Models

Author

Listed:
  • Feroze, Navid

Abstract

There are numerous studies dealing with analysis for the future patterns of COVID-19 in different countries using conventional time series models. This study aims to provide more flexible analytical framework that decomposes the important components of the time series, incorporates the prior information, and captures the evolving nature of model parameters.

Suggested Citation

  • Feroze, Navid, 2020. "Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian Structural Time Series Models," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305920
    DOI: 10.1016/j.chaos.2020.110196
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077920305920
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2020.110196?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yousaf, Muhammad & Zahir, Samiha & Riaz, Muhammad & Hussain, Sardar Muhammad & Shah, Kamal, 2020. "Statistical analysis of forecasting COVID-19 for upcoming month in Pakistan," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    2. ., 2020. "National transportation infrastructure," Chapters, in: The Infrastructured State, chapter 2, pages 31-60, Edward Elgar Publishing.
    3. ., 2020. "Innovation and national security," Chapters, in: Defense Technological Innovation, chapter 2, pages 17-38, Edward Elgar Publishing.
    4. Steven L. Scott & Hal R. Varian, 2015. "Bayesian Variable Selection for Nowcasting Economic Time Series," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 119-135, National Bureau of Economic Research, Inc.
    5. Nea, 2020. "National legislative and regulatory activities: 0," Nuclear Law Bulletin, OECD Publishing, vol. 2019(2).
    6. Obryan Poyser, 2019. "Exploring the dynamics of Bitcoin’s price: a Bayesian structural time series approach," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 9(1), pages 29-60, March.
    7. ., 2020. "National water infrastructure system," Chapters, in: The Infrastructured State, chapter 5, pages 106-129, Edward Elgar Publishing.
    8. ., 2020. "National energy infrastructure," Chapters, in: The Infrastructured State, chapter 4, pages 82-105, Edward Elgar Publishing.
    9. ., 2020. "National information infrastructure," Chapters, in: The Infrastructured State, chapter 3, pages 61-81, Edward Elgar Publishing.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ballı, Serkan, 2021. "Data analysis of Covid-19 pandemic and short-term cumulative case forecasting using machine learning time series methods," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    2. Ondrej Bednar, 2021. "The Causal Impact of the Rapid Czech Interest Rate Hike on the Czech Exchange Rate Assessed by the Bayesian Structural Time Series Model," International Journal of Economic Sciences, European Research Center, vol. 10(2), pages 1-17, December.
    3. Hwang, Eunju, 2022. "Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    4. Jaime Pinilla & Patricia Barber & Laura Vallejo-Torres & Silvia Rodríguez-Mireles & Beatriz G. López-Valcárcel & Luis Serra-Majem, 2021. "The Economic Impact of the SARS-COV-2 (COVID-19) Pandemic in Spain," IJERPH, MDPI, vol. 18(9), pages 1-13, April.
    5. Muhammed Navas Thorakkattle & Shazia Farhin & Athar Ali khan, 2022. "Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA," Annals of Data Science, Springer, vol. 9(5), pages 1025-1047, October.
    6. Fernandes, Leonardo H.S. & de Araujo, Fernando H.A. & Tabak, Benjamin M., 2021. "Insights from the (in)efficiency of Chinese sectoral indices during COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    7. Krzysztof Drachal & Daniel González Cortés, 2022. "Estimation of Lockdowns’ Impact on Well-Being in Selected Countries: An Application of Novel Bayesian Methods and Google Search Queries Data," IJERPH, MDPI, vol. 20(1), pages 1-24, December.
    8. Lipić, Tomislav & Štajduhar, Andrija & Medvidović, Luka & Wild, Dorian & Korošak, Dean & Podobnik, Boris, 2022. "Stringency without efficiency is not adequate to combat pandemics," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).

    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. Chen, Bing & Li, Li & Peng, Fei & Anwar, Sajid, 2020. "Risk contagion in the banking network: New evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    2. Bas, Javier & Cirillo, Cinzia & Cherchi, Elisabetta, 2021. "Classification of potential electric vehicle purchasers: A machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    3. Marinello, Samantha & Leider, Julien & Pugach, Oksana & Powell, Lisa M., 2021. "The impact of the Philadelphia beverage tax on employment: A synthetic control analysis," Economics & Human Biology, Elsevier, vol. 40(C).
    4. Madurai Elavarasan, Rajvikram & Pugazhendhi, Rishi & Irfan, Muhammad & Mihet-Popa, Lucian & Khan, Irfan Ahmad & Campana, Pietro Elia, 2022. "State-of-the-art sustainable approaches for deeper decarbonization in Europe – An endowment to climate neutral vision," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    5. Mayock, Tom & Tzioumis, Konstantinos, 2021. "New construction and mortgage default," Journal of Banking & Finance, Elsevier, vol. 133(C).
    6. Maione, Carol, 2020. "Adapting to drought and extreme climate: Hunger Safety Net Programme, Kenya," World Development Perspectives, Elsevier, vol. 20(C).
    7. Moghari, Somaye & Ghorani, Maryam, 2022. "A symbiosis between cellular automata and dynamic weighted multigraph with application on virus spread modeling," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    8. Reed, Jeffrey & Dailey, Emily & Shaffer, Brendan & Lane, Blake & Flores, Robert & Fong, Amber & Samuelsen, Scott, 2023. "Potential evolution of the renewable hydrogen sector using California as a reference market," Applied Energy, Elsevier, vol. 331(C).
    9. Alice Jar Rein Aung & Chun Yee Wong, 2022. "The Effects of Education on Fertility and Child Mortality: Evidence from the free secondary education policy in the Philippines," Working Papers EMS_2022_02, Research Institute, International University of Japan.
    10. Takahiro Yabe & Yunchang Zhang & Satish Ukkusuri, 2020. "Quantifying the Economic Impact of Extreme Shocks on Businesses using Human Mobility Data: a Bayesian Causal Inference Approach," Papers 2004.11121, arXiv.org.
    11. Ondrej Bednar, 2021. "The Causal Impact of the Rapid Czech Interest Rate Hike on the Czech Exchange Rate Assessed by the Bayesian Structural Time Series Model," International Journal of Economic Sciences, European Research Center, vol. 10(2), pages 1-17, December.
    12. Obryan Poyser, 2017. "Exploring the determinants of Bitcoin's price: an application of Bayesian Structural Time Series," Papers 1706.01437, arXiv.org.
    13. Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
    14. Yutaka Kurihara & Akio Fukushima & Shinichiro Maeda, 2020. "Can Bitcoin’S Price Be A Predictor Of Stock Prices?," Noble International Journal of Economics and Financial Research, Noble Academic Publsiher, vol. 5(4), pages 50-55, April.
    15. Muhammed Navas Thorakkattle & Shazia Farhin & Athar Ali khan, 2022. "Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA," Annals of Data Science, Springer, vol. 9(5), pages 1025-1047, October.
    16. Nivín, Rafael & Pérez, Fernando, 2019. "Estimación de un Índice de Condiciones Financieras para el Perú," Revista Estudios Económicos, Banco Central de Reserva del Perú, issue 37, pages 49-64.
    17. Hulya Bakirtas & Vildan Gulpinar Demirci, 2022. "Can Google Trends data provide information on consumer’s perception regarding hotel brands?," Information Technology & Tourism, Springer, vol. 24(1), pages 57-83, March.
    18. Liran Einav & Jonathan Levin, 2014. "The Data Revolution and Economic Analysis," Innovation Policy and the Economy, University of Chicago Press, vol. 14(1), pages 1-24.
    19. Zhou, Siwen, 2018. "Exploring the Driving Forces of the Bitcoin Exchange Rate Dynamics: An EGARCH Approach," MPRA Paper 89445, University Library of Munich, Germany.
    20. Kurtis, Kimberly E. & Lolli, Francesca, 2020. "Alkali-activated Materials: Environmental Preliminary Assessment for U.S. Roadway Applications," Institute of Transportation Studies, Working Paper Series qt76z7m878, Institute of Transportation Studies, UC Davis.

    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:eee:chsofr:v:140:y:2020:i:c:s0960077920305920. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.