Panel forecasts of country-level Covid-19 infections.
Panel forecasts of country-level Covid-19 infections.
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DOI：
10.1016/j.jeconom.2020.08.010
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发表时间：
2021-01
影响因子：
6.3
通讯作者：
Schorfheide F
中科院分区：
文献类型：
--
作者：
Liu L;Moon HR;Schorfheide F
我们使用动态面板数据模型来生成一组国家/地区的每日活跃新冠肺炎感染密度预测。我们的规范假设活动性感染的增长率可以用带间断的向下倾斜的确定性趋势函数周围的自回归波动来表示。我们的完全贝叶斯方法允许我们灵活地估计斜坡的横截面分布，然后像以前一样隐含地使用该分布来构建对单个时间序列的贝叶斯预测。我们发现一些证据表明，来自早期暴发地点的信息可以提高对较晚地点的预测准确性。由于参数和冲击的不确定性，特别是在感染路径的峰值之前和附近，关于活动性感染的演变通常存在很大的不确定性。在一周的时间范围内，我们的区间预测的经验覆盖频率接近名义可信水平。我们模型的每周预报发布在https://laurayuliu.com/covid19-panel-forecast/.上
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/.
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影响因子：
0.9
作者：
Askanazi, Ross;Diebold, Francis X.;Shin, Minchul
通讯作者：
Shin, Minchul
DOI：
10.1098/rspa.1927.0118
发表时间：
1927-08-01
期刊：
PROCEEDINGS OF THE ROYAL SOCIETY OF LONDON SERIES A-CONTAINING PAPERS OF A MATHEMATICAL AND PHYSICAL CHARACTER
影响因子：
--
作者：
Kermack, WO;McKendrick, AG
通讯作者：
McKendrick, AG
影响因子：
--
作者：
Ho, Paul;Lubik, Thomas A.;Matthes, Christian
通讯作者：
Matthes, Christian
影响因子：
6.1
作者：
Haavelmo, Trygve
通讯作者：
Haavelmo, Trygve
影响因子：
2.1
作者：
Gu, Jiaying;Koenker, Roger
通讯作者：
Koenker, Roger