Panel forecasts of country-level Covid-19 infections

Laura Liu, Hyungsik Roger Moon, Frank Schorfheide

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


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

Original languageEnglish
Pages (from-to)2-22
Number of pages21
JournalJournal of Econometrics
Issue number1
Publication statusPublished - 2021 Jan

Bibliographical note

Publisher Copyright:
© 2020 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


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