Journal
JOURNAL OF ECONOMETRICS
Volume 220, Issue 1, Pages 2-22Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2020.08.010
Keywords
Bayesian inference; Covid-19; Density forecasts; Interval forecasts; Panel data models; Random effects; SIR model
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Funding
- National Science Foundation, USA [SES 1625586, SES 1851634]
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A dynamic panel data model is used to generate density forecasts for daily active Covid-19 infections for a panel of countries/regions, finding that information from locations with early outbreaks can enhance forecast accuracy for later locations. There is considerable uncertainty about the evolution of active infections, especially before and around the peak of the infection path, but over a one week horizon, the empirical coverage frequency of forecasts is close to the nominal credible level.
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/. (C) 2020 Elsevier B.V. All rights reserved.
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