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    A Bayesian policy learning model of COVID-19 non-pharmaceutical interventions

    Mamatzakis, Emmanuel and Ongena, S. and Patel, P.C. and Tsionas, M. (2023) A Bayesian policy learning model of COVID-19 non-pharmaceutical interventions. Applied Economics , ISSN 0003-6846.

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    Abstract

    This article examines the impact of non-pharmaceutical interventions on the initial exponential growth of the infected population and the final exponential decay of the infected population. We employ a Bayesian dynamic model to test whether there is learning, a random walk pattern, or another type of learning with evolving epidemiological data over time across 168 countries and 41,706 country-date observations. Although we show that Bayesian learning is not taking place, most policy measures appear to assert some effect. In particular, we show that economic policy variables are of importance for the main epidemiological parameters derived from the policy learning model. In an empirical second-stage application, we further investigate the underlying dynamics between the epidemiological parameters and household debt repayments, a key economic variable, in the UK. Results show no Bayesian learning, although a higher transmission rate would increase household debt repayments, while the recovery rate would have a negative impact. Therefore, suboptimal learning is taking place.

    Metadata

    Item Type: Article
    School: Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School
    Research Centres and Institutes: Accounting and Finance Research Centre
    Depositing User: Emmanuel Mamatzakis
    Date Deposited: 07 Jun 2023 05:17
    Last Modified: 24 Jan 2024 14:23
    URI: https://eprints.bbk.ac.uk/id/eprint/51303

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