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Erscheinungsjahr: 
2018
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[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 6 [Issue:] 2 [Publisher:] MDPI [Place:] Basel [Year:] 2018 [Pages:] 1-25
Verlag: 
MDPI, Basel
Zusammenfassung: 
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.
Schlagwörter: 
Bartlett test
F-test
over-dispersed Poisson
log-normal
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