skewnessParameter: A generic function to plot and/or return the posterior of the skewness parameter for models in BALD.

Description

A generic function to plot and/or return the posterior of the skewness parameter for models in BALD.

Arguments

object

The object from which to plot and/or return the skewness 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, then only the statistics are returned.

Value

Mainly called for the side effect of plotting.

Details

The skewness parameter does not directly correspond to the degree of skewness. However, all else being equal, a larger (in magnitude) skewness parameter indicates a higher degree of skewness, and a skewness parameter of zero equates to zero skew. See vignette('BALD').

References

Kim, Y., and J. McCulloch (2007) “The Skew-Student Distribution with Application to U.S. Stock Market Returns and the Equity Premium,” Department of Economics, Ohio State University, October 2007

Examples

Run this code

# 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)
skewnessParameter(standard.model.output)
# }