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Handling Hierarchically Structured Risk Factors using Random Effects Models

actuaRE.Rd

Using this package, you can fit a random effects model using either the hierarchical credibility model, a combination of the hierarchical credibility model with a generalized linear model or a Tweedie generalized linear mixed model. See Campo, B.D.C. and Antonio, K. (2023) <doi:10.1080/03461238.2022.2161413>.

References

Campo, B.D.C. and Antonio, Katrien (2023). Insurance pricing with hierarchically structured data an illustration with a workers' compensation insurance portfolio. Scandinavian Actuarial Journal, doi: 10.1080/03461238.2022.2161413

Dannenburg, D. R., Kaas, R. and Goovaerts, M. J. (1996). Practical actuarial credibility models. Amsterdam: IAE (Institute of Actuarial Science and Econometrics of the University of Amsterdam).

Jewell, W. S. (1975). The use of collateral data in credibility theory: a hierarchical model. Laxenburg: IIASA.

Ohlsson, E. (2005). Simplified estimation of structure parameters in hierarchical credibility. Presented at the Zurich ASTIN Colloquium.http://www.actuaries.org/ASTIN/Colloquia/Zurich/Ohlsson.pdf

Ohlsson, E. (2008). Combining generalized linear models and credibility models in practice. Scandinavian Actuarial Journal 2008(4), 301–314.

See also

hierCredibility hierCredGLM hierCredTweedie tweedieGLMM BalanceProperty

Examples

# \donttest{
  library(actuaRE)
  # Vignette of the package
  vignette(package = "actuaRE")
#> no vignettes found

  # Load data
  data(hachemeisterLong)
  data(dataCar)

  # Hierarchical credibility model of Jewell
  fit = hierCredibility(ratio, weight, cohort, state, hachemeisterLong)

  # Combination of the hierarchical credibility model with a GLM (Ohlsson, 2008)
  fit = hierCredGLM(Y ~ area + (1 | VehicleType / VehicleBody), dataCar, weights = w,
  p = 1.7)
# }