Detecting fuzzy relationships in regression models: The case of insurer solvency surveillance in Germany

Thomas R. Berry-Stölzle, Marie-Claire Koissi and Arnold F. Shapiro

Insurance: Mathematics and Economics, 2010, vol. 46, issue 3, 554-567

Abstract: We develop a test for the fuzziness of regression coefficients based on the Tanaka et al. (1982) and He et al. (2007) possibilistic fuzzy regression models. We interpret the spread of the regression coefficients as a statistic measuring the fuzziness of the relationship between the corresponding independent variable and the dependent variable. We derive test distributions based on the null hypothesis that such spreads could have been obtained by estimating a possibilistic regression with data generated by a classical regression model with random errors. As an example, we show how our test detects a fuzzy regression coefficient in a solvency prediction model for German property-liability insurance companies.

Keywords: Test; for; fuzziness; Possibilistic; fuzzy; regression; Financial; statement; data; Insurance; regulation (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:46:y:2010:i:3:p:554-567

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