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Analyzing the trend in COVID-19 data: The structural break approach

Author

Listed:
  • Nityananda Sarkar

    (Indian Statistical Institute, Kolkata, West Bengal, India, 700108.)

  • Kushal Banik Chowdhury

    (Indian Statistical institute, North-East Centre, Tezpur, Assam, India 784501.)

Abstract

In this paper, we have considered three important variables concerning COVID-19 viz., (i) the number of daily new cases, (ii) the number of daily total cases, and (iii) the number of daily deaths, and proposed a modelling procedure, so that the nature of trend in these series could be studied appropriately and then used for identifying the current phase of the pandemic including the phase of containment, if happening /happened, in any country. The proposed modelling procedure gives due consideration to structural breaks in the series. The data from four countries, Brazil, India, Italy and the UK, have been used to study the efficacy of the proposed model. Regarding the phase of infection in these countries, we have found, using data till 19 May 2020, that both Brazil and India are in the increasing phase with infections rising up and further up, but Italy and the UK are in decreasing/containing phase suggesting that these two countries are expected to be free of this pandemic in due course of time provided their respective trend continues. The forecast performance of this model has also established its superiority, as compared to two other standard trend models viz., polynomial and exponential trend models.

Suggested Citation

  • Nityananda Sarkar & Kushal Banik Chowdhury, 2022. "Analyzing the trend in COVID-19 data: The structural break approach," International Econometric Review (IER), Econometric Research Association, vol. 14(3), pages 72-96, September.
    • Handle: RePEc:erh:journl:v:14:y:2022:i:3:p:72-96
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    More about this item

    Keywords

    COVID-19; Structural breaks; Non-stationarity; Forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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