Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/185812
Authors: 
Bondarev, Anton
Krysiak, Frank C.
Year of Publication: 
2018
Series/Report no.: 
WWZ Working Paper 2018/10
Abstract: 
We study the optimal adaptation to extreme climate events by the central government in a setup where events are dynamically uncertain and the government does not know the true probabilities of events. We analyze different policy decision rules minimizing expected welfare losses for sites with different expected damages from the catastrophic event. We find out under which conditions it is optimal to wait before implementation of a prevention measures to obtain more information about the underlying probabilistic process. This waiting time crucially depends on the information set of the planner and the implemented learning procedure. We study different learning procedures on behalf of the planner ranging from simple perfect learning to two-layers Bayesian updating in the form of Dirichlet mixture processes.
Subjects: 
climate change adaptation
catastrophic events
model uncertainty
Bayesian updating
Dirichlet mixtures
JEL: 
Q54
H41
C41
Document Type: 
Working Paper
Social Media Mentions:

Files in This Item:
File
Size
745.19 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.