Panel forecasts of country-level Covid-19 infections.
J Econom
; 220(1): 2-22, 2021 Jan.
Article
in English
| MEDLINE | ID: covidwho-1071592
ABSTRACT
We use a dynamic panel data model to generate density forecasts for daily active Covid-19 infections for a panel of countries/regions. Our specification that assumes 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 slopes and then implicitly use this distribution as prior to construct Bayes forecasts for the individual time series. We find some evidence that information from locations with an early outbreak can sharpen forecast accuracy for late locations. There is generally a lot of uncertainty about the evolution of active infection, due to parameter and shock uncertainty, in particular before and around the peak of the infection path. 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:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Observational study
/
Randomized controlled trials
Language:
English
Journal:
J Econom
Year:
2021
Document Type:
Article
Affiliation country:
J.jeconom.2020.08.010
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