IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v155y2022ics0960077921011425.html
   My bibliography  Save this article

Prediction intervals of the COVID-19 cases by HAR models with growth rates and vaccination rates in top eight affected countries: Bootstrap improvement

Author

Listed:
  • Hwang, Eunju

Abstract

This paper is devoted to modeling and predicting COVID-19 confirmed cases through a multiple linear regression. Especially, prediction intervals of the COVID-19 cases are extensively studied. Due to long-memory feature of the COVID-19 data, a heterogeneous autoregression (HAR) is adopted with Growth rates and Vaccination rates; it is called HAR-G-V model. Top eight affected countries are taken with their daily confirmed cases and vaccination rates. Model criteria results such as root mean square error (RMSE), mean absolute error (MAE), R2, AIC and BIC are reported in the HAR models with/without the two rates. The HAR-G-V model performs better than other HAR models. Out-of-sample forecasting by the HAR-G-V model is conducted. Forecast accuracy measures such as RMSE, MAE, mean absolute percentage error and root relative square error are computed. Furthermore, three types of prediction intervals are constructed by approximating residuals to normal and Laplace distributions, as well as by employing bootstrap procedure. Empirical coverage probability, average length and mean interval score are evaluated for the three prediction intervals. This work contributes three folds: a novel trial to combine both growth rates and vaccination rates in modeling COVID-19; construction and comparison of three types of prediction intervals; and an attempt to improve coverage probability and mean interval score of prediction intervals via bootstrap technique.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:155:y:2022:i:c:s0960077921011425
    DOI: 10.1016/j.chaos.2021.111789
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077921011425
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2021.111789?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. Hwang, Eunju & Hong, Won-Tak, 2021. "A multivariate HAR-RV model with heteroscedastic errors and its WLS estimation," Economics Letters, Elsevier, vol. 203(C).
    2. 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).
    3. Liu, Laura & Moon, Hyungsik Roger & Schorfheide, Frank, 2021. "Panel forecasts of country-level Covid-19 infections," Journal of Econometrics, Elsevier, vol. 220(1), pages 2-22.
    4. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    5. Maleki, Mohsen & Mahmoudi, Mohammad Reza & Heydari, Mohammad Hossein & Pho, Kim-Hung, 2020. "Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    6. Soukhovolsky, Vladislav & Kovalev, Anton & Pitt, Anne & Kessel, Boris, 2020. "A new modelling of the COVID 19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    7. Reis, Ruy Freitas & de Melo Quintela, Bárbara & de Oliveira Campos, Joventino & Gomes, Johnny Moreira & Rocha, Bernardo Martins & Lobosco, Marcelo & Weber dos Santos, Rodrigo, 2020. "Characterization of the COVID-19 pandemic and the impact of uncertainties, mitigation strategies, and underreporting of cases in South Korea, Italy, and Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    8. Won-Tak Hong & Jiwon Lee & Eunju Hwang, 2020. "A Note on the Asymptotic Normality Theory of the Least Squares Estimates in Multivariate HAR-RV Models," Mathematics, MDPI, vol. 8(11), pages 1-18, November.
    9. Sarkar, Kankan & Khajanchi, Subhas & Nieto, Juan J., 2020. "Modeling and forecasting the COVID-19 pandemic in India," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    10. Pitchaimani, M. & Brasanna Devi, M., 2021. "Stochastic probical strategies in a delay virus infection model to combat COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    11. Ribeiro, Matheus Henrique Dal Molin & da Silva, Ramon Gomes & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2020. "Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    12. Prasanth, Sikakollu & Singh, Uttam & Kumar, Arun & Tikkiwal, Vinay Anand & Chong, Peter H.J., 2021. "Forecasting spread of COVID-19 using google trends: A hybrid GWO-deep learning approach," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    13. Ekinci, Aykut, 2021. "Modelling and forecasting of growth rate of new COVID-19 cases in top nine affected countries: Considering conditional variance and asymmetric effect," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    14. Wilms, Ines & Rombouts, Jeroen & Croux, Christophe, 2021. "Multivariate volatility forecasts for stock market indices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 484-499.
    15. Kalantari, Mahdi, 2021. "Forecasting COVID-19 pandemic using optimal singular spectrum analysis," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    16. Nikolopoulos, Konstantinos & Punia, Sushil & Schäfers, Andreas & Tsinopoulos, Christos & Vasilakis, Chrysovalantis, 2021. "Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions," European Journal of Operational Research, Elsevier, vol. 290(1), pages 99-115.
    17. Luo, Xilin & Duan, Huiming & Xu, Kai, 2021. "A novel grey model based on traditional Richards model and its application in COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    18. Edouard Mathieu & Hannah Ritchie & Esteban Ortiz-Ospina & Max Roser & Joe Hasell & Cameron Appel & Charlie Giattino & Lucas Rodés-Guirao, 2021. "A global database of COVID-19 vaccinations," Nature Human Behaviour, Nature, vol. 5(7), pages 947-953, July.
    19. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    20. 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).
    21. Kırbaş, İsmail & Sözen, Adnan & Tuncer, Azim Doğuş & Kazancıoğlu, Fikret Şinasi, 2020. "Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    22. J. Bracher & D. Wolffram & J. Deuschel & K. Görgen & J. L. Ketterer & A. Ullrich & S. Abbott & M. V. Barbarossa & D. Bertsimas & S. Bhatia & M. Bodych & N. I. Bosse & J. P. Burgard & L. Castro & G. Fa, 2021. "A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    23. Salgotra, Rohit & Gandomi, Mostafa & Gandomi, Amir H, 2020. "Time Series Analysis and Forecast of the COVID-19 Pandemic in India using Genetic Programming," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    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. Eunju Hwang, 2023. "Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA Models," Forecasting, MDPI, vol. 6(1), pages 1-18, December.
    2. Ghimire, Sujan & AL-Musaylh, Mohanad S. & Nguyen-Huy, Thong & Deo, Ravinesh C. & Acharya, Rajendra & Casillas-Pérez, David & Yaseen, Zaher Mundher & Salcedo-Sanz, Sancho, 2025. "Explainable deeply-fused nets electricity demand prediction model: Factoring climate predictors for accuracy and deeper insights with probabilistic confidence interval and point-based forecasts," Applied Energy, Elsevier, vol. 378(PA).
    3. He, Yaoyao & Wang, Yun & Wang, Shuo & Yao, Xin, 2022. "A cooperative ensemble method for multistep wind speed probabilistic forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    4. Alexander Gusev & Alexander Chervyakov & Anna Alexeenko & Evgeny Nikulchev, 2023. "Particle Swarm Training of a Neural Network for the Lower Upper Bound Estimation of the Prediction Intervals of Time Series," Mathematics, MDPI, vol. 11(20), pages 1-12, October.

    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. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Matouk, A.E., 2020. "Complex dynamics in susceptible-infected models for COVID-19 with multi-drug resistance," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    4. Das, Saikat & Bose, Indranil & Sarkar, Uttam Kumar, 2023. "Predicting the outbreak of epidemics using a network-based approach," European Journal of Operational Research, Elsevier, vol. 309(2), pages 819-831.
    5. Li, Li & Kang, Yanfei & Li, Feng, 2023. "Bayesian forecast combination using time-varying features," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1287-1302.
    6. Crokidakis, Nuno, 2020. "COVID-19 spreading in Rio de Janeiro, Brazil: Do the policies of social isolation really work?," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    7. Niu, Zibo & Liu, Yuanyuan & Gao, Wang & Zhang, Hongwei, 2021. "The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China," Resources Policy, Elsevier, vol. 73(C).
    8. David Harris & Gael M. Martin & Indeewara Perera & Don S. Poskitt, 2017. "Construction and visualization of optimal confidence sets for frequentist distributional forecasts," Monash Econometrics and Business Statistics Working Papers 9/17, Monash University, Department of Econometrics and Business Statistics.
    9. Yuru Sun & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Gael M. Martin, 2023. "Optimal probabilistic forecasts for risk management," Papers 2303.01651, arXiv.org.
    10. Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org, revised Nov 2024.
    11. Sander Barendse, 2017. "Interquantile Expectation Regression," Tinbergen Institute Discussion Papers 17-034/III, Tinbergen Institute.
    12. Ahumada, M. & Ledesma-Araujo, A. & Gordillo, L. & Marín, J.F., 2023. "Mutation and SARS-CoV-2 strain competition under vaccination in a modified SIR model," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    13. Malte Knuppel & Fabian Kruger & Marc-Oliver Pohle, 2022. "Score-based calibration testing for multivariate forecast distributions," Papers 2211.16362, arXiv.org, revised Dec 2023.
    14. Pham, Son Duy & Nguyen, Thao Thac Thanh & Li, Xiao-Ming, 2024. "Stabilizing global foreign exchange markets in the time of COVID-19: The role of vaccinations," Global Finance Journal, Elsevier, vol. 59(C).
    15. Catania, Leopoldo & Grassi, Stefano, 2022. "Forecasting cryptocurrency volatility," International Journal of Forecasting, Elsevier, vol. 38(3), pages 878-894.
    16. Kafieh, Rahele & Saeedizadeh, Narges & Arian, Roya & Amini, Zahra & Serej, Nasim Dadashi & Vaezi, Atefeh & Javanmard, Shaghayegh Haghjooy, 2020. "Isfahan and Covid-19: Deep spatiotemporal representation," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    17. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
    18. Ray, Evan L. & Brooks, Logan C. & Bien, Jacob & Biggerstaff, Matthew & Bosse, Nikos I. & Bracher, Johannes & Cramer, Estee Y. & Funk, Sebastian & Gerding, Aaron & Johansson, Michael A. & Rumack, Aaron, 2023. "Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1366-1383.
    19. Zeroual, Abdelhafid & Harrou, Fouzi & Dairi, Abdelkader & Sun, Ying, 2020. "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    20. Behnood, Ali & Mohammadi Golafshani, Emadaldin & Hosseini, Seyedeh Mohaddeseh, 2020. "Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA)," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).

    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:155:y:2022:i:c:s0960077921011425. 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.