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Prognoser på försäkringsdata: En utvärdering av prediktionsmodeller för antal skador på den svenska försäkringsmarknaden
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2018 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2018. , p. 47
Keywords [sv]
Tidsserier, Prognoser, ARIMA, Svensk Försäkring, Försäkring, Regression
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-374731OAI: oai:DiVA.org:uu-374731DiVA, id: diva2:1281908
Subject / course
Statistics
Supervisors
Examiners
Available from: 2019-01-25 Created: 2019-01-23 Last updated: 2019-01-25Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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