BALD (version 1.0.0-3)

calendarYearEffectErrors: A generic function to plot and/or return predicted and forecast calendar year effect errors for models in BALD.

Description

A generic function to plot and/or return predicted and forecast calendar year effect errors for models in BALD.

Arguments

object

The object from which to plot and/or return the calendar year effect errors.

extraYears

An integer expressing the (maximum) number of years to plot (beyond the final observed calendar year). Must be greater than or equal to zero. Default is 15.

plot

A logical value. If TRUE, the plot is generated and the statistics are returned; otherwise only the statistics are returned.

Value

Mainly called for the side effect of plotting.

Details

The calendar year effect is comprised of two components: 1) a prior expected value which may be unique to every cell (subject to weights and bounds) and 2) a diagonal-specific error term. This function only plots and returns the error term, which includes an autoregressive component if the model is estimated with such a feature. See vignette('BALD').

See Also

calendarYearEffectErrors("AnnualAggLossDevModelOutput") calendarYearEffect autoregressiveParameter standardDeviationOfCalendarYearEffect calendarYearEffectErrorTracePlot

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)
calendarYearEffectErrors(standard.model.output)
# }