An Econometric Panel Data Model of the COVID-19 Pandemic

Antoine Djogbenou, Christian Gouriéroux, Joann Jasiak and Paul Rilstone

Journal of Statistical and Econometric Methods, 2022, vol. 11, issue 1, 3

Abstract: New flexible-form and semi-parametric autoregressive non-linear count models for panel data are developed to analyse the spread and containment of the COVID-19 pandemic. The models are based on a discrete time form of the SIR model. These methods lead naturally to estimators of the infection process and daily reproduction numbers by jurisdiction. Two semi-parametric versions of the reproduction numbers are developed corresponding to currently popular parametric estimators. The estimators are applied to a large international data set to estimate these parameters for 221 jurisdictions at both national and subnational levels. Â Â JEL classification numbers: C14, C23, I18.

Keywords: COVID-19; Reproduction Numbers; Panel Data; Count Models; Semi-parametric Approach. (search for similar items in EconPapers)
Date: 2022
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Working Paper: An econometric panel data model of the COVID-19 pandemic (2022)
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