Bayesian Policy Learning Modeling of COVID-19 Interventions: the Impact on Household Debt Repayment in UK and Internationally

42 Pages Posted: 27 Jul 2021

See all articles by E. C. Mamatzakis

E. C. Mamatzakis

University of London

Steven Ongena

University of Zurich

Mike Tsionas

Lancaster University

Date Written: July 17, 2021

Abstract

The rapid spread of COVID-19 across the globe primed a variety of non-pharmaceutical interventions (NPIs). Given these NPIs, whether the SIR parameters followed a Bayesian learning, a random walk pattern or other type of learning with evolving epidemiological data over time has implications for policy learning literature. Using a sample of UK country specific data and also for 168 countries and 51,083 country-date observations (January 1, 2020 to January 9, 2021), we estimate a SIR model with time-varying β and γ parameters in three context of a dynamic panel vector autoregressive model. Although learning does not seem to be taking place, and despite the absence of evidence of governments’ learning from the past, most policy measures are effective in reducing the values of the β and γ parameters. We also provide estimates of time-varying β and γ that can be used widely, and we develop novel testing procedures for testing for Bayesian learning.

Keywords: Bayesian learning, COVID-19, UK household debt repayment, interventions

JEL Classification: G20, IOO, CO1, C11

Suggested Citation

Mamatzakis, E. C. and Ongena, Steven and Tsionas, Efthymios G., Bayesian Policy Learning Modeling of COVID-19 Interventions: the Impact on Household Debt Repayment in UK and Internationally (July 17, 2021). Available at SSRN: https://ssrn.com/abstract=3888559 or http://dx.doi.org/10.2139/ssrn.3888559

E. C. Mamatzakis (Contact Author)

University of London

Senate House
Malet Street
London, WC1E 7HU
United Kingdom

Steven Ongena

University of Zurich

Rämistrasse 71
Zürich, CH-8006
Switzerland

Efthymios G. Tsionas

Lancaster University ( email )

Lancaster LA1 4YX
United Kingdom

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