Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195817 
Authors: 
Year of Publication: 
2018
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 6 [Issue:] 2 [Publisher:] MDPI [Place:] Basel [Year:] 2018 [Pages:] 1-25
Publisher: 
MDPI, Basel
Abstract: 
Despite the widespread use of chain-ladder models, so far no theory was available to test for model specification. The popular over-dispersed Poisson model assumes that the over-dispersion is common across the data. A further assumption is that accident year effects do not vary across development years and vice versa. The log-normal chain-ladder model makes similar assumptions. We show that these assumptions can easily be tested and that similar tests can be used in both models. The tests can be implemented in a spreadsheet. We illustrate the implementation in several empirical applications. While the results for the log-normal model are valid in finite samples, those for the over-dispersed Poisson model are derived for large cell mean asymptotics which hold the number of cells fixed. We show in a simulation study that the finite sample performance is close to the asymptotic performance.
Subjects: 
Bartlett test
F-test
over-dispersed Poisson
log-normal
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Appears in Collections:

Files in This Item:
File
Size
528.39 kB





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