Working Paper: NBER ID: w27248
Authors: Laura Liu; Hyungsik Roger Moon; Frank Schorfheide
Abstract: 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/.
Keywords: COVID-19; Panel Data; Density Forecasts; Bayesian Analysis
JEL Codes: C11; C23; C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Bayesian panel data model (C23) | growth rates of active COVID-19 infections (O57) |
| cross-sectional heterogeneity in infection dynamics (C21) | forecasts for countries that experienced outbreaks at different times (F17) |
| parameter uncertainty and future shocks (D84) | evolution of infection rates (J11) |
| information from countries with earlier outbreaks (F44) | forecast accuracy for later-affected regions (C53) |