meanExposureGrowth {BALD} | R Documentation |
A generic function to plot and/or return the posterior of the mean exposure growth for models in BALD.
object |
The object from which to plot and/or return the mean exposure growth. |
plotDensity |
A logical value. If |
plotTrace |
A logical value. If |
(Optionally) exposure growth is modeled as an ar1 process. This inherently assumes that periods of high exposure growth are (or at least have the possibility of being) followed by continued high periods.
See vignette('BALD')
.
Mainly called for the side effect of plotting.
meanExposureGrowth("AnnualAggLossDevModelOutput")
rm(list=ls()) options(device.ask.default=FALSE) library(BALD) data(IncrementalGeneralLiablityTriangle) IncrementalGeneralLiablityTriangle <- as.matrix(IncrementalGeneralLiablityTriangle) print(IncrementalGeneralLiablityTriangle) data(PCE) PCE <- as.matrix(PCE)[,1] PCE.rate <- PCE[-1] / PCE[-length(PCE)] - 1 PCE.rate.length <- length(PCE.rate) PCE.years <- as.integer(names(PCE.rate)) years.available <- PCE.years <= max(as.integer( dimnames(IncrementalGeneralLiablityTriangle)[[1]])) PCE.rate <- PCE.rate[years.available] PCE.rate.length <- length(PCE.rate) standard.model.input <- makeStandardAnnualInput( incremental.payments = IncrementalGeneralLiablityTriangle, stoch.inflation.weight = 1, non.stoch.inflation.weight = 0, stoch.inflation.rate = PCE.rate, exp.year.type = 'ay', extra.dev.years=5, use.skew.t=TRUE) ## Not run: standard.model.output <- runLossDevModel(standard.model.input, burnIn=30.0E+3, sampleSize=30.0E+3, thin=10) meanExposureGrowth(standard.model.output) ## End(Not run)