Pandemic economics: Optimal dynamic confinement under uncertainty and learning
Résumé
Most integrated models of the Covid pandemic have been developed
under the assumption that the policy-sensitive reproduction number is
certain. The decision to exit from the lockdown has been made in most
countries without knowing the reproduction number that would prevail
after the deconfinement. In this paper, I explore the role of uncertainty
and learning on the optimal dynamic lockdown policy. I limit the analysis
to suppression strategies. In the absence of uncertainty, the optimal
confinement policy is to impose a constant rate of lockdown until the
suppression of the virus in the population. I show that introducing
uncertainty about the reproduction number of deconfined people reduces
the optimal initial rate of confinement.
Domaines
Economies et finances
Origine : Accord explicite pour ce dépôt