Panel forecasts of country-level Covid-19 infections

Laura Liu, Hyungsik Roger Moon, Frank Schorfheide

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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/.

Original languageEnglish
Pages (from-to)2-22
Number of pages21
JournalJournal of Econometrics
Volume220
Issue number1
DOIs
Publication statusPublished - 2021 Jan

Bibliographical note

Funding Information:
We thank Elie Tamer (co-editor), an anonymous referee, Graham Elliott, and participants at the 26th IIF workshop on Economic Forecasting in Times of COVID-19, the NBER-NSF Seminar on Bayesian Inference in Econometrics and Statistics, and the FRB Philadelphia Conference on Real Time Data Analysis for helpful comments and suggestions. We also thank the Johns Hopkins University Center for Systems Science and Engineering for making Covid-19 data publicly available on GitHub and Evan Chan for his help in developing the website on which we publish our forecasts. Moon and Schorfheide gratefully acknowledge financial support from the National Science Foundation, USA under Grants SES 1625586 and SES 1851634, respectively. Moon thanks Dr. S. Kim, Dr. C. Moon, and Dr. H. Song of GemVax & KAEL for helpful conversations on Covid-19 related topics.

Funding Information:
We thank Elie Tamer (co-editor), an anonymous referee, Graham Elliott, and participants at the 26th IIF workshop on Economic Forecasting in Times of COVID-19, the NBER-NSF Seminar on Bayesian Inference in Econometrics and Statistics, and the FRB Philadelphia Conference on Real Time Data Analysis for helpful comments and suggestions. We also thank the Johns Hopkins University Center for Systems Science and Engineering for making Covid-19 data publicly available on GitHub and Evan Chan for his help in developing the website on which we publish our forecasts. Moon and Schorfheide gratefully acknowledge financial support from the National Science Foundation, USA under Grants SES 1625586 and SES 1851634 , respectively. Moon thanks Dr. S. Kim, Dr. C. Moon, and Dr. H. Song of GemVax & KAEL for helpful conversations on Covid-19 related topics.

Publisher Copyright:
© 2020 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

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