Panel Forecasts of Country-Level Covid-19 Infections
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/.
We thank the Johns Hopkins University Center for Systems Science and Engineering for making Covid-19 data publicly available on Github and Evan Chan for his help developing the website on which we publish our forecasts. Moon and Schorfheide gratefully acknowledge financial support from the National Science Foundation under Grants SES 1625586 and SES 1424843, respectively. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Laura Liu & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Panel forecasts of country-level Covid-19 infections," Journal of Econometrics, .