DP14790 Panel Forecasts of Country-Level Covid-19 Infectionsliu
|Author(s):||Laura Liu, Hyungsik Roger Moon, Frank Schorfheide|
|Publication Date:||May 2020|
|Keyword(s):||Bayesian inference, COVID-19, Density forecasts, interval forecasts, panel data models, random effects, SIR model|
|JEL(s):||C11, C23, C53|
|Programme Areas:||Macroeconomics and Growth|
|Link to this Page:||cepr.org/active/publications/discussion_papers/dp.php?dpno=14790|
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/.