BALD (version 1.0.0-3)

standardDeviationForScaleInnovation: A generic function to plot and/or return the posterior of the standard deviation for the innovation in the scale parameter for models in BALD.

Description

A generic function to plot and/or return the posterior of the standard deviation for the innovation in the scale parameter for models in BALD.

Arguments

object

The object from which to plot and/or return the standard deviation for the innovation in the log of the scale parameter.

plotDensity

A logical value. If TRUE, then the density is plotted. If plotTrace is also TRUE, then two plots are generated. If they are both FALSE, then only the statistics are returned.

plotTrace

A logical value. If TRUE, then the trace is plotted. If plotDensity is also TRUE, then two plots are generated. If they are both FALSE, only the statistics are returned.

Value

Mainly called for the side effect of plotting.

Details

Changes in the scale parameter (see scaleParameter) are assumed to follow a second-order random walk on the log scale. This function plots the posterior standard deviation for this random walk. See vignette('BALD').

See Also

standardDeviationForScaleInnovation("AnnualAggLossDevModelOutput") standardDeviationVsDevelopmentTime

Examples

# NOT RUN {
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)
standardDeviationForScaleInnovation(standard.model.output)
# }