The purpose of this report is to predict annual insurance data with quarterly data as predictors and to evaluate its accuracy against other naive prediction models. A relationship is discerned between the two data categories and the interest goes beyond publication frequency as there is a fundamental difference between quarterly and annual data. The insurance industry organization Insurance Sweden publishes quarterly data that contain all insurance events reported while the annual data only contain insurance events which led to disbursement from the insurance companies. This discrepancy shows to be problematic when predicting annual outcomes. Forecasts are estimated by ARIMA models on short time series and compared with classic linear regression models. The implied results from all insurance subcategories in traffic, motor vehicles and household- and corporate insurance are that, in some cases, prediction using linear regression on quarterly data is more precise than the constructed naive prediction models on annual data. However, the results vary between subcategories and the regression models using quarterly data need further improvement before it is the obvious choice when forecasting annual number of events that led to disbursements from the insurance companies.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-374731 |
| Date | January 2018 |
| Creators | Börsum, Jakob, Nyblom, Jakob |
| Publisher | Uppsala universitet, Statistiska institutionen, Uppsala universitet, Statistiska institutionen |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0025 seconds