Predicting the COVID-19 pandemic in Canada and the US

Ba Chu and Shafiullah Qureshi ()
Additional contact information
Shafiullah Qureshi: Carleton University

Economics Bulletin, 2020, vol. 40, issue 3, 2565-2585

Abstract: We propose a time series model with the quartic trend function to make short-term forecasts of the COVID-19 confirmed cases in Canada and the U.S. Our one- to seven- days ahead out-of-sample forecast exercise demonstrates that the quartic trend model can produce very competitive short-term forecasts relative to the benchmark Susceptible, Infected, and Recovered (SIR) model. The bootstrap distance-based test of independence and the XGBoost algorithm reveals a strong link between the coronavirus case count and relevant Google Trends features (defined by search intensities of various keywords that the public entered in the Google internet search engine during this pandemic). Moreover, dynamic linear panel data models suggest a statistically significant relationship between the coronavirus case count and people's mobility trend provided by Google Mobility Reports (GMR) during the pandemic period.

Keywords: Google Trends (GT) data; COVID-19 forecasts; Panel data; Google Mobility Trends; SIR model; Quartic trend function; Bootstrap; XGBoost. (search for similar items in EconPapers)
JEL-codes: C2 C5 (search for similar items in EconPapers)
Date: 2020-09-24
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.accessecon.com/Pubs/EB/2020/Volume40/EB-20-V40-I3-P223.pdf (application/pdf)

Related works:
Working Paper: Predicting the COVID-19 Pandemic in Canada and the US (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ebl:ecbull:eb-20-00405

Access Statistics for this article

More articles in Economics Bulletin from AccessEcon
Bibliographic data for series maintained by John P. Conley ().