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Panel forecasts of country-level Covid-19 infections

Author: Laura Liu; Hyungsik Roger Moon; Frank Schorfheide; Centre for Economic Policy Research (Great Britain),
Publisher: London : Centre for Economic Policy Research, 2020.
Series: Discussion paper (Centre for Economic Policy Research (Great Britain)), no. 14790.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
We use dynamic panel data models to generate density forecasts for daily Covid-19 infections for a panel of countries/regions. At the core of our model is a specification that assumes that the growth rate of active infections can be represented by autoregressive fluctuations around a downward sloping deterministic trend function with a break. Our fully Bayesian approach allows us to flexibly estimate the  Read more...
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Details

Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Laura Liu; Hyungsik Roger Moon; Frank Schorfheide; Centre for Economic Policy Research (Great Britain),
OCLC Number: 1155638230
Notes: "Published 20 May 2020"
"Submitted 19 May 2020"
Description: 1 online resource (25 pages) : illustrations.
Series Title: Discussion paper (Centre for Economic Policy Research (Great Britain)), no. 14790.
Responsibility: Laura Liu, Hyungsik Roger Moon, and Frank Schorfheide.

Abstract:

We use dynamic panel data models to generate density forecasts for daily Covid-19 infections for a panel of countries/regions. At the core of our model is a specification that assumes that the growth rate of active infections can be represented by autoregressive fluctuations around a downward sloping deterministic trend function with a break. Our fully Bayesian approach allows us to flexibly estimate the cross-sectional distribution of heterogeneous coefficients and then implicitly use this distribution as prior to construct Bayes forecasts for the individual time series. According to our model, there is a lot of uncertainty about the evolution of infection rates, due to parameter uncertainty and the realization of future shocks. We find that over a one-week horizon the empirical coverage frequency of our interval forecasts is close to the nominal credible level. Weekly forecasts from our model are published at https://laurayuliu.com/covid19-panel-forecast/.

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