Panel forecasts of country-level Covid-19 infections

We use a dynamic panel data model to generate density forecasts for daily active Covid-19 infections for a panel of countries/regions. Our specification that assumes the growth rate of active infections can be represented by autoregressive fluctuations around a downward sloping deterministic trend f...

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Bibliographic Details
Published in:Journal of econometrics 2021-01, Vol.220 (1), p.2-22
Main Authors: Liu, Laura, Moon, Hyungsik Roger, Schorfheide, Frank
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian inference
Coronaviruses
COVID-19
Density
Density forecasts
Forecasting
Growth rate
Infections
Interval forecasts
Panel data
Panel data models
Random effects
SIR model
Specification
Time series
Uncertainty
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Summary:We use a dynamic panel data model to generate density forecasts for daily active Covid-19 infections for a panel of countries/regions. Our specification that assumes 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 slopes and then implicitly use this distribution as prior to construct Bayes forecasts for the individual time series. We find some evidence that information from locations with an early outbreak can sharpen forecast accuracy for late locations. There is generally a lot of uncertainty about the evolution of active infection, due to parameter and shock uncertainty, in particular before and around the peak of the infection path. 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/.
ISSN:0304-4076
1872-6895
0304-4076
DOI:10.1016/j.jeconom.2020.08.010