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

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.