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

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  • 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
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    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. 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.
    6. ., 2020. "National energy infrastructure," Chapters, in: The Infrastructured State, chapter 4, pages 82-105, Edward Elgar Publishing.
    7. ., 2020. "National information infrastructure," Chapters, in: The Infrastructured State, chapter 3, pages 61-81, Edward Elgar Publishing.
    8. Nea, 2020. "National legislative and regulatory activities: 0," Nuclear Law Bulletin, OECD Publishing, vol. 2019(2).
    9. ., 2020. "National water infrastructure system," Chapters, in: The Infrastructured State, chapter 5, pages 106-129, Edward Elgar Publishing.
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    Citations

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    Cited by:

    1. 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).
    2. 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.
    3. 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).
    4. 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).
    5. 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.
    6. 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.
    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).

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