R: Cumulative Insurance Loss Payments

loss {brms} | R Documentation

## Cumulative Insurance Loss Payments

### Description

This dataset, discussed in Gesmann & Morris (2020), contains cumulative insurance loss payments over the course of ten years.

### Usage

    loss

### Format

A data frame of 55 observations containing information on the following 4 variables.

AY

  Origin year of the insurance (1991 to 2000)

dev

  Deviation from the origin year in months

cum

  Cumulative loss payments

premium

  Achieved premiums for the given origin year

### Source

Gesmann M. & Morris J. (2020). Hierarchical Compartmental Reserving Models. CAS Research Papers.

### Examples

    ## Not run:
    # non-linear model to predict cumulative loss payments
    fit_loss <- brm(
      bf(cum ~ ult * (1 - exp(-(dev/theta)^omega)),
         ult ~ 1 + (1|AY), omega ~ 1, theta ~ 1,
         nl = TRUE),
      data = loss, family = gaussian(),
      prior = c(
        prior(normal(5000, 1000), nlpar = "ult"),
        prior(normal(1, 2), nlpar = "omega"),
        prior(normal(45, 10), nlpar = "theta")
      ),
      control = list(adapt_delta = 0.9)
    )

    # basic summaries
    summary(fit_loss)
    conditional_effects(fit_loss)

    # plot predictions per origin year
    conditions <- data.frame(AY = unique(loss$AY))
    rownames(conditions) <- unique(loss$AY)
    me_loss <- conditional_effects(
      fit_loss, conditions = conditions,
      re_formula = NULL, method = "predict"
    )
    plot(me_loss, ncol = 5, points = TRUE)

    ## End(Not run)

[Package brms version 2.16.3 Index]