Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/227920
Authors: 
Çakmaklı, Cem
Şimşek, Yasin
Year of Publication: 
2020
Series/Report no.: 
Working Paper No. 2013
Abstract: 
This paper extends the canonical model of epidemiology, SIRD model, to allow for time varying parameters for real-time measurement of the stance of the COVID-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modelling structure designed for the typically daily count data related to pandemic. The resulting specification permits a flexible yet parsimonious model structure with a very low computational cost. This is especially crucial at the onset of the pandemic when the data is scarce and the uncertainty is abundant. Full sample results show that countries including US, Brazil and Russia are still not able to contain the pandemic with the US having the worst performance. Furthermore, Iran and South Korea are likely to experience the second wave of the pandemic. A real-time exercise show that the proposed structure delivers timely and precise information on the current stance of the pandemic ahead of the competitors that use rolling window. This, in turn, transforms into accurate short-term predictions of the active cases. We further modify the model to allow for unreported cases. Results suggest that the effects of the presence of these cases on the estimation results diminish towards the end of sample with the increasing number of testing.
Subjects: 
COVID-19
SIRD
Observation driven models
Score models
Count data
time varying parameters
JEL: 
C13
C32
C51
I19
Document Type: 
Working Paper

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