G2TT
来源类型Discussion paper
规范类型论文
来源IDDP14790
DP14790 Panel Forecasts of Country-Level Covid-19 Infectionsliu
Laura Liu; Hyungsik Roger Moon; Frank Schorfheide
发表日期2020-05-20
出版年2020
语种英语
摘要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/.
主题Macroeconomics and Growth
关键词Bayesian inference Covid-19 Density forecasts Interval forecasts Panel data models Random effects Sir model
URLhttps://cepr.org/publications/dp14790
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/543720
推荐引用方式
GB/T 7714
Laura Liu,Hyungsik Roger Moon,Frank Schorfheide. DP14790 Panel Forecasts of Country-Level Covid-19 Infectionsliu. 2020.
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