Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/242997
Authors: 
Çakmaklı, Cem
Şimşek, Yasin
Year of Publication: 
2021
Series/Report no.: 
Working Paper No. 2013
Abstract: 
This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-varying parameters for real-time measurement and prediction of the trajectory of the Covid-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modeling structure designed for the typical daily count data related to the pandemic. The resulting specification permits a flexible yet parsimonious model with a low computational cost. The model is extended to allow for unreported cases as well. Results suggest that these cases' effects on the parameter estimates diminish with the increasing number of testing. Full sample results show that the flexible framework captures the successive waves of the pandemic accurately. A real-time exercise indicates that the proposed structure delivers timely and precise information on the pandemic's current stance. This superior performance, in turn, transforms into accurate predictions of the confirmed cases.
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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