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Panel Forecasts of Country-Level Covid-19 Infections / Laura Liu, Hyungsik Roger Moon, Frank Schorfheide.
- Format:
- Book
- Author/Creator:
- Liu, Laura.
- Series:
- Working Paper Series (National Bureau of Economic Research) no. w27248.
- NBER working paper series no. w27248
- Language:
- English
- Physical Description:
- 1 online resource: illustrations (black and white);
- Place of Publication:
- Cambridge, Mass. National Bureau of Economic Research 2020.
- Summary:
- 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/.
- Notes:
- Print version record
- May 2020.
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