A Novel Solution to Biased Data in COVID-19 Incidence Studies

28 Pages Posted: 7 Jul 2020

See all articles by Hrishikesh D. Vinod

Hrishikesh D. Vinod

Fordham University - Department of Economics

Katherine Theiss

Fordham University, Fordham University

Date Written: June 29, 2020

Abstract

Complete novelty and uncertainty of the COVID-19 pandemic have created many challenging scientific problems, including biased data arising from a lack of randomized testing over the general population. We describe the bias problem and its solution from Econometrics literature, which seems to have been neglected by epidemiology experts. We study a large COVID-19 US data set, providing nationwide forecasts of deaths to illustrate the model's power. Our two-equation model overcomes the bias by using the inverse Mills ratio and improves forecasts of new deaths in all nine out of nine weekly out-of-sample comparisons. It can be applied to a variety of problems associated with the pandemic. A focused study of trends in deaths predicted by lagged cumulative infections reveals that forty-two states have negative trends and that seven of nine states with undesirable positive trends have Republican governors.

Note: Funding: None to declare

Declaration of Interest: None to declare

Keywords: Inverse Mills Ratio, Selection Models, Poisson probit model, outcome equation

JEL Classification: I18, C10, C33

Suggested Citation

Vinod, Hrishikesh D. and Theiss, Katherine, A Novel Solution to Biased Data in COVID-19 Incidence Studies (June 29, 2020). Available at SSRN: https://ssrn.com/abstract=3637682 or http://dx.doi.org/10.2139/ssrn.3637682

Hrishikesh D. Vinod (Contact Author)

Fordham University - Department of Economics ( email )

Dealy Hall
Bronx, NY 10458
United States
718-817-4065 (Phone)
718-817-3518 (Fax)

Katherine Theiss

Fordham University, Fordham University ( email )

Bronx, NY 10458
United States

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