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Case Studies Using Panel Data Models

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  • Edward Frees
  • Virginia Young
  • Yu Luo

Abstract

In this paper, we examine case studies from three different areas of insurance practice: health care, workers’ compensation, and group term life. These different case studies illustrate how the broad class of panel data models can be applied to different functional areas and to data that have different features. Panel data, also known as longitudinal data, models are regression-type models that have been developed extensively in the biological and economic sciences. The data features that we discuss include heteroscedasticity, random and fixed effect covariates, outliers, serial correlation, and limited dependent variable bias. We demonstrate the process of identifying these features using graphical and numerical diagnostic tools from standard statistical software.Our motivation for examining these cases comes from credibility rate making, a technique for pricing certain types of health care, property and casualty, workers’ compensation, and group life coverages. It has been a part of actuarial practice since Mowbray’s (1914) fundamental contribution. In earlier work, we showed how many types of credibility models could be expressed as special cases of panel data models. This paper exploits this link by using tools developed in connection with panel data models for credibility rate-making purposes. In particular, special routines written for credibility rate-making purposes are not required.

Suggested Citation

  • Edward Frees & Virginia Young & Yu Luo, 2001. "Case Studies Using Panel Data Models," North American Actuarial Journal, Taylor & Francis Journals, vol. 5(4), pages 24-42.
    • Handle: RePEc:taf:uaajxx:v:5:y:2001:i:4:p:24-42
    DOI: 10.1080/10920277.2001.10596010
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    Cited by:

    1. Wei Wang & Limin Wen & Zhixin Yang & Quan Yuan, 2020. "Quantile Credibility Models with Common Effects," Risks, MDPI, vol. 8(4), pages 1-10, September.
    2. Paulsen, Jostein & Lunde, Astrid & Skaug, Hans Julius, 2008. "Fitting mixed-effects models when data are left truncated," Insurance: Mathematics and Economics, Elsevier, vol. 43(1), pages 121-133, August.
    3. Antonio, Katrien & Beirlant, Jan, 2007. "Actuarial statistics with generalized linear mixed models," Insurance: Mathematics and Economics, Elsevier, vol. 40(1), pages 58-76, January.
    4. Zhang, Kong-Sheng & Lin, Jin-Guan & Xu, Pei-Rong, 2016. "A new class of copulas involving geometric distribution: Estimation and applications," Insurance: Mathematics and Economics, Elsevier, vol. 66(C), pages 1-10.
    5. Dornheim, Harald & Brazauskas, Vytaras, 2011. "Robust-efficient credibility models with heavy-tailed claims: A mixed linear models perspective," Insurance: Mathematics and Economics, Elsevier, vol. 48(1), pages 72-84, January.
    6. Lu Yang & Claudia Czado, 2022. "Two‐part D‐vine copula models for longitudinal insurance claim data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1534-1561, December.
    7. Wen, Limin & Wu, Xianyi & Zhou, Xian, 2009. "The credibility premiums for models with dependence induced by common effects," Insurance: Mathematics and Economics, Elsevier, vol. 44(1), pages 19-25, February.
    8. Pitselis, Georgios, 2004. "A seemingly unrelated regression model in a credibility framework," Insurance: Mathematics and Economics, Elsevier, vol. 34(1), pages 37-54, February.

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