Panel Forecasts of Country-Level Covid-19 Infections

Laura Liu, Hyungsik Roger Moon, Frank Schorfheide

NBER Working Paper No. 27248
Issued in May 2020
NBER Program(s):Economic Fluctuations and Growth, Health Economics

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/.

Supplementary materials for this paper:

• Weekly Forecasts and Replication Code

Acknowledgments

Machine-readable bibliographic record - MARC, RIS, BibTeX

Document Object Identifier (DOI): 10.3386/w27248

Published: Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Panel forecasts of country-level Covid-19 infections," Journal of Econometrics, . citation courtesy of