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

Navid Feroze

Chaos, Solitons & Fractals, 2020, vol. 140, issue C

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.

Keywords: Posterior probabilities; Intervention analysis; Prediction intervals and forecast accuracy measures (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305920

DOI: 10.1016/j.chaos.2020.110196

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