consumptionPath: A generic function to plot and/or return the estimated...

In BALD: Robust Loss Development Using MCMC

Description

A generic function to plot and/or return the estimated consumption path vs development year time.

Arguments

object	The object from which to plot and/or return the estimated consumption path.
plot	A logical value. If TRUE, the plot is generated and the statistics are returned; otherwise only the statistics are returned.

Details

At the heart of aggregate loss development models in BALD is the consumption path. The consumption path is (on a log scale) the trajectory of incremental payments absent calendar year effects and with exposure normalized to the first row. Note that the measurement error term is (possibly) a skewed t and as such (possibly) has a non zero mean. The consumption path is absent any such shifts due to skewness. This is a generic function that allows for the retrieval and illustration of this consumption path. See vignette('BALD').

Value

Mainly called for the side effect of plotting. Also returns the plotted statistics. Returned invisibly.

See Also

consumptionPath("StandardAnnualAggLossDevModelOutput") consumptionPath("BreakAnnualAggLossDevModelOutput") consumptionPathTracePlot

Examples

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)
consumptionPath(standard.model.output)

## End(Not run)

BALD documentation built on May 2, 2019, 6:51 a.m.