Panel Forecasts of Country-Level Covid-19 Infectionsliu

Frank Schorfheide, Laura Liu and Hyungsik Roger Moon

No 14790, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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: Bayesian inference; Covid-19; Density forecasts; Interval forecasts; Panel data models; Random effects; Sir model (search for similar items in EconPapers)
JEL-codes: C11 C23 C53 (search for similar items in EconPapers)
Date: 2020-05
New Economics Papers: this item is included in nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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