Artikel
Log-normal or over-dispersed poisson?
Although both over-dispersed Poisson and log-normal chain-ladder models are popular in claim reserving, it is not obvious when to choose which model. Yet, the two models are obviously different. While the over-dispersed Poisson model imposes the variance to mean ratio to be common across the array, the log-normal model assumes the same for the standard deviation to mean ratio. Leveraging this insight, we propose a test that has the power to distinguish between the two models. The theory is asymptotic, but it does not build on a large size of the array and, instead, makes use of information accumulating within the cells. The test has a non-standard asymptotic distribution; however, saddle point approximations are available. We show in a simulation study that these approximations are accurate and that the test performs well in finite samples and has high power.
- Language
-
Englisch
- Bibliographic citation
-
Journal: Risks ; ISSN: 2227-9091 ; Volume: 6 ; Year: 2018 ; Issue: 3 ; Pages: 1-37 ; Basel: MDPI
- Classification
-
Wirtschaft
- Subject
-
non-nested testing
encompassing
chain-ladder
- Event
-
Geistige Schöpfung
- (who)
-
Harnau, Jonas
- Event
-
Veröffentlichung
- (who)
-
MDPI
- (where)
-
Basel
- (when)
-
2018
- DOI
-
doi:10.3390/risks6030070
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Harnau, Jonas
- MDPI
Time of origin
- 2018
Other Objects (12)
Misspecification tests for log-normal and over-dispersed poisson chain-ladder models
Piecewise log-normal approximation of size distributions for aerosol modelling
Power-Law and Log-Normal Distributions in Firm Size Displacement Data
Volatility is log-normal: But not for the reason you think
Log-normal approximation of the equity premium in the production model
Power-Law and Log-Normal Distributions in Firm Size Displacement Data
Log-normal approximation of the equity premium in the production model
Power-Law and Log-Normal Distributions in Temporal Changes of Firm-Size Variables
Power-Law and Log-Normal Distributions in Temporal Changes of Firm-Size Variables
Power-Law and Log-Normal Distributions in Temporal Changes of Firm-Size Variables
Log-moment estimators of the Nakagami-lognormal distribution
Bayesian active learning line sampling with log-normal process for rare-event probability estimation
Misspecification tests for log-normal and over-dispersed poisson chain-ladder models
Piecewise log-normal approximation of size distributions for aerosol modelling
Power-Law and Log-Normal Distributions in Firm Size Displacement Data
Volatility is log-normal: But not for the reason you think
Log-normal approximation of the equity premium in the production model
Power-Law and Log-Normal Distributions in Firm Size Displacement Data
Log-normal approximation of the equity premium in the production model
Power-Law and Log-Normal Distributions in Temporal Changes of Firm-Size Variables
Power-Law and Log-Normal Distributions in Temporal Changes of Firm-Size Variables
Power-Law and Log-Normal Distributions in Temporal Changes of Firm-Size Variables
Log-moment estimators of the Nakagami-lognormal distribution
Bayesian active learning line sampling with log-normal process for rare-event probability estimation
Misspecification tests for log-normal and over-dispersed poisson chain-ladder models
Piecewise log-normal approximation of size distributions for aerosol modelling
Power-Law and Log-Normal Distributions in Firm Size Displacement Data
Volatility is log-normal: But not for the reason you think
Log-normal approximation of the equity premium in the production model
Power-Law and Log-Normal Distributions in Firm Size Displacement Data
Log-normal approximation of the equity premium in the production model
Power-Law and Log-Normal Distributions in Temporal Changes of Firm-Size Variables
Power-Law and Log-Normal Distributions in Temporal Changes of Firm-Size Variables
Power-Law and Log-Normal Distributions in Temporal Changes of Firm-Size Variables
Log-moment estimators of the Nakagami-lognormal distribution