Panel Forecasts of Country-Level Covid-19 Infections
National Bureau of Economic Research Working Paper Series
; No. 27248, 2020.
Article
in English
| NBER | ID: grc-748518
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/.
Full text:
Available
Collection:
Databases of international organizations
Database:
NBER
Type of study:
Experimental Studies
/
Observational study
/
Randomized controlled trials
Language:
English
Journal:
National Bureau of Economic Research Working Paper Series
Year:
2020
Document Type:
Article
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