Predicting the COVID-19 Pandemic in Canada and the US

Ba Chu and Shafiullah Qureshi (shafiullahqureshi@cmail.carleton.ca)
Additional contact information
Shafiullah Qureshi: Department of Economics, Carleton University, https://carleton.ca/economics/people/qureshi-shafiullah/

No 20-05, Carleton Economic Papers from Carleton University, Department of Economics

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 the 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.

JEL-codes: C22 C53 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2020-05-04, Revised 2020-07-30
New Economics Papers: this item is included in nep-big and nep-cmp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published: Carleton Economics Papers

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Journal Article: Predicting the COVID-19 pandemic in Canada and the US (2020)
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