Handling Hierarchically Structured Risk Factors using Random Effects Models

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.

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

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)
# }