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Panel forecasts of country-level Covid-19 infections()

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 f...

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Detalles Bibliográficos
Autores principales: Liu, Laura, Moon, Hyungsik Roger, Schorfheide, Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7566698/
https://www.ncbi.nlm.nih.gov/pubmed/33100475
http://dx.doi.org/10.1016/j.jeconom.2020.08.010
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author Liu, Laura
Moon, Hyungsik Roger
Schorfheide, Frank
author_facet Liu, Laura
Moon, Hyungsik Roger
Schorfheide, Frank
author_sort Liu, Laura
collection PubMed
description 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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spelling pubmed-75666982020-10-19 Panel forecasts of country-level Covid-19 infections() Liu, Laura Moon, Hyungsik Roger Schorfheide, Frank J Econom Article 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/. Elsevier B.V. 2021-01 2020-10-16 /pmc/articles/PMC7566698/ /pubmed/33100475 http://dx.doi.org/10.1016/j.jeconom.2020.08.010 Text en © 2020 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Liu, Laura
Moon, Hyungsik Roger
Schorfheide, Frank
Panel forecasts of country-level Covid-19 infections()
title Panel forecasts of country-level Covid-19 infections()
title_full Panel forecasts of country-level Covid-19 infections()
title_fullStr Panel forecasts of country-level Covid-19 infections()
title_full_unstemmed Panel forecasts of country-level Covid-19 infections()
title_short Panel forecasts of country-level Covid-19 infections()
title_sort panel forecasts of country-level covid-19 infections()
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7566698/
https://www.ncbi.nlm.nih.gov/pubmed/33100475
http://dx.doi.org/10.1016/j.jeconom.2020.08.010
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