id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-280683-5572l6bo	Liu, Laura	Panel forecasts of country-level Covid-19 infections()	2020-10-16		.txt	text/plain	7198	494	61	We use a dynamic panel data model to generate density forecasts for daily active Covid-19 infections for a panel of countries/regions. 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. In addition to forecasts from our panel data model, we also consider forecasts based on location-level time series estimates of our trend-break model and a simple SIR model. Once we decompose the set of locations into those that experienced the Covid-19 outbreak early (prior to 2020-03-28) and those that experience the outbreak later on, then we find some evidence that for the late group the panel density forecasts are more accurate than the time-series forecasts. First, as in Section 4, we generate time-series forecasts based on the trend-break model (3) for each location.	./cache/cord-280683-5572l6bo.txt	./txt/cord-280683-5572l6bo.txt