A Bayesian Policy Learning Model of COVID-19 Interventions and its Impact on Household Debt Repayments

The paper 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 51,083 country-date observations. Although learning might not take place, most policy measures appear to assert some effect. In an application, we employ the main epidemiological parameters derived from the policy learning model to examine their impact on household debt repayments in the UK within a vector autoregressive system of equations. Results show that a higher transmission rate would increase household debt repayments, while the recovery rate would have a negative impact on debt repayment