inst/unitTests/runit.MackChainLadder Modif Skew.R
In edalmoro/ChainLadderEDM: Statistical Methods and Models for Claims Reserving in General Insurance
test.Mack1999 <- function() {
## by Markus Gesmann
## 2013-02-26 Don't check names of "check-values" f and f.se
## Test the MackChainLadder against example given in Mack's 1999 paper
MRT <- MackChainLadder(Mortgage, tail=1.05, tail.sigma=71, tail.se=0.02, est.sigma="Mack")
## Table 1 in the above paper
f <- c(11.10, 4.092, 1.708, 1.276, 1.139, 1.069, 1.026, 1.023, 1.05)
f.se <- c(2.24, 0.517, 0.122, 0.051, 0.042, 0.023, 0.015, 0.012, 0.02)
F.se3 <- c(7.38, 1.89, 0.357, 0.116, 0.078, 0.033, 0.015, 0.007, 0.03)
sig <- c(1337, 988.5, 440.1, 207, 164.2, 74.60, 35.49, 16.89,71)
## test output from MackChainLadder
checkEquals(MRT$f, f,tol=0.001, checkNames = FALSE)
checkEquals(MRT$f.se, f.se,tol=0.01, checkNames = FALSE)
checkEquals(as.numeric(MRT$F.se[3,]),F.se3,tol=0.001)
checkEquals(as.numeric(MRT$sigma),sig,tol=0.0001)
}
test.MackRAA <- function() {
## by Dan Murphy
## Make sure MackChainLadder run on RAA returns the correct overall
## total standard error, 26,880.74
checkEquals(sprintf("%.2f", MackChainLadder(RAA)$Total.Mack.S.E), "26880.74")
}
test.MackRAA_tail_equals_1.05 <- function() {
## by Dan Murphy
## Make sure MackChainLadder run on RAA returns the correct overall
## total standard error, 28,587.35, when input tail factor is 1.05
checkEquals(sprintf("%.2f", MackChainLadder(RAA, est.sigma = "Mack", tail = 1.05)$Total.Mack.S.E), "28669.91")
}
test.MackMortgageCVWithTail <- function() {
## by Dan Murphy
## Make sure amount in example checks out
MRT <- MackChainLadder(Mortgage, tail=1.05, tail.se = .05)
checkEquals(round(tail(MRT$Total.ParameterRisk, 1) / summary(MRT)$Totals["IBNR", ], 2), .19)
}
# test.NoDevelopmentExample <- function(){
# # If there's essentially no development from one age to the next, lm will warn
# # that it's "essentially perfect fit". Want to replace that with a better message.
# # The solution is to suppress that message, detect a perfect.fit separately,
# # and "warning" an informational message to the console.
# x <- matrix(byrow = TRUE, nrow = 4, ncol = 4,
# dimnames = list(origin = LETTERS[1:4], dev = 1:4),
# data = c(
# 100, 105, 105.00001, 105.00001,
# 200, 210, 210.00001, NA,
# 300, 315, NA, NA,
# 400, NA, NA, NA)
# )
# # This should no longer generate a warning
# m <- tryCatch(MackChainLadder(x, est.sigma = "Mack"), warning = function(w) w)
# checkTrue(inherits(m, "warning"))
# }
# test.loglinear.NA.Example <- function(){
# x <- matrix(byrow = TRUE, nrow = 4, ncol = 4,
# dimnames = list(origin = LETTERS[1:4], dev = 1:4),
# data = c(
# 100, 105, 105.00001, 105.00001,
# 200, 210, 210.00001, NA,
# 300, 315, NA, NA,
# 400, NA, NA, NA)
# )
# # This should no longer generate an error
# m <- tryCatch(MackChainLadder(x, est.sigma = "log-linear"), error = function(e) e)
# checkTrue(!inherits(m, "error"))
# }
test.MackNANissue <- function(){
MackNANTriIssue <- structure(c(72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101, NA,
101, NA, 124, NA, 124, NA, 100, NA, 100, NA, 100, NA, 100, NA,
100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 72, NA,
77, NA, 94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124,
NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 72, NA, 77, NA, 94, NA, 82, NA,
79, NA, 101, NA, 101, NA, 124, NA, 124, NA, 100, NA, 100, NA,
100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA,
NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101, NA, 101,
NA, 124, NA, 124, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, NA, NA, 72, NA, 77, NA,
94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, NA, NA, NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101,
NA, 101, NA, 124, NA, 124, NA, 100, NA, 100, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, 72, NA,
77, NA, 94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124,
NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, NA, NA, NA, NA, NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79,
NA, 101, NA, 101, NA, 124, NA, 124, NA, 100, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA,
72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124,
NA, 124, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 72, NA, 77, NA, 94, NA, 82,
NA, 79, NA, 101, NA, 101, NA, 124, NA, 124, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101, NA, 101,
NA, 124, NA, 124, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72, NA, 77, NA, 94,
NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101,
NA, 101, NA, 124, NA, 124, NA, 100, NA, 100, NA, 100, NA, 100,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72, NA, 77,
NA, 94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124, NA,
100, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101,
NA, 101, NA, 124, NA, 124, NA, 100, NA, 100, NA, 100, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72, NA, 77,
NA, 94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124, NA,
100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101,
NA, 101, NA, 124, NA, 124, NA, 100, NA, 100, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72, NA, 77,
NA, 94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124, NA,
100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101,
NA, 101, NA, 124, NA, 124, NA, 100, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72, NA, 77, NA,
94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124, NA, 100,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 100, NA, 100, NA, 100, NA, 100, NA,
100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 100, NA, 100, NA, 100, NA, 100, NA,
100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 100, NA, 100, NA, 100, NA, 100, NA,
100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 100, NA, 100, NA, 100, NA, 100, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100, NA,
100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100, NA, 100,
NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100, NA, 100, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 100, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), .Dim = c(38L, 38L),
.Dimnames = structure(
list(origin =
c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24",
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35",
"36", "37", "38"),
dev = c("V1", "V2", "V3", "V4", "V5", "V6",
"V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16",
"V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25",
"V26", "V27", "V28", "V29", "V30", "V31", "V32", "V33", "V34",
"V35", "V36", "V37", "V38")), .Names = c("origin", "dev")),
class = c("triangle",
"matrix"))
checkEquals(summary(MackChainLadder(MackNANTriIssue, est.sigma="Mack"))$Totals[1,1],
1900)
}
test.Mack1993 <- function() {
## by Eric Dal Moro
## Check the additional Skewness elements addedto MachChainLadder function on the Mack 1993 triangles
## Results are in the paper by Eric Dal Moro:
## "An approximation of the non-life reserve risk distribution using the Cornish-Fisher expansion"
## available at SSRN: https://ssrn.com/abstract=2965384
MRT <- MackChainLadder(Mack1993, est.sigma="Mack")
## Table 1 in the above paper
Skewness <- c(0, 0, -0.029, -0.043, -0.001, 0.180, 0.055, 0.267, 0.286, 0.314)
OverSkew <- 0.214
OverSkewCalc <- MRT$OverSkew/MRT$Total.Mack.S.E^3
## test output from MackChainLadder
checkEquals(MRT$Skewness, Skewness,tol=0.0015, checkNames = FALSE)
checkEquals(OverSkewCalc, OverSkew,tol=0.0015, checkNames = FALSE)
}
test.SCORLiabProp <- function() {
## by Eric Dal Moro
## Check the additional Skewness elements addedto MachChainLadder function on the Mack 1993 triangles
## Results are in the paper by Eric Dal Moro:
## "An approximation of the non-life reserve risk distribution using the Cornish-Fisher expansion"
## available at SSRN: https://ssrn.com/abstract=2965384
MRT <- MackChainLadder(SCORLiabProp, est.sigma="Mack")
## Table 1 in the above paper
Skewness <- c(0, 0, -0.032, 0.157, -0.416, 0.542, 0.418, 0.392, 0.203, 0.226, 0.036, 0.048, 0.015, 0.060, 0.925)
OverSkew <- 0.534
OverSkewCalc <- MRT$OverSkew/MRT$Total.Mack.S.E^3
## test output from MackChainLadder
checkEquals(MRT$Skewness, Skewness,tol=0.0015, checkNames = FALSE)
checkEquals(OverSkewCalc, OverSkew,tol=0.0015, checkNames = FALSE)
}
test.SCORMotorNP <- function() {
## by Eric Dal Moro
## Check the additional Skewness elements addedto MachChainLadder function on the Mack 1993 triangles
## Results are in the paper by Eric Dal Moro:
## "An approximation of the non-life reserve risk distribution using the Cornish-Fisher expansion"
## available at SSRN: https://ssrn.com/abstract=2965384
MRT <- MackChainLadder(SCORMotorNP, est.sigma="Mack")
## Table 1 in the above paper
Skewness <- c(0, 0, -0.037, -0.138, -0.115, -0.026, 0.059, 0.101, 0.236, 0.185, 0.271, 0.142, 0.174, 0.278, 1.001)
OverSkew <- 0.333
OverSkewCalc <- MRT$OverSkew/MRT$Total.Mack.S.E^3
## test output from MackChainLadder
checkEquals(MRT$Skewness, Skewness,tol=0.0015, checkNames = FALSE)
checkEquals(OverSkewCalc, OverSkew,tol=0.0015, checkNames = FALSE)
}
test.EverestLiabProp <- function() {
## by Eric Dal Moro
## Check the additional Skewness elements addedto MachChainLadder function on the Mack 1993 triangles
## Results are in the paper by Eric Dal Moro:
## "An approximation of the non-life reserve risk distribution using the Cornish-Fisher expansion"
## available at SSRN: https://ssrn.com/abstract=2965384
MRT <- MackChainLadder(EverestLiabProp, est.sigma="Mack")
## Table 1 in the above paper
Skewness <- c(0, 0, 0.042, 0.087, 0.065, -0.268, -0.234, -0.164, 0.075, 0.157, 0.245, 0.289, 0.168, 0.231, 0.547)
OverSkew <- 0.321
OverSkewCalc <- MRT$OverSkew/MRT$Total.Mack.S.E^3
## test output from MackChainLadder
checkEquals(MRT$Skewness, Skewness,tol=0.0015, checkNames = FALSE)
checkEquals(OverSkewCalc, OverSkew,tol=0.0015, checkNames = FALSE)
}
test.EverestLiabNP <- function() {
## by Eric Dal Moro
## Check the additional Skewness elements addedto MachChainLadder function on the Mack 1993 triangles
## Results are in the paper by Eric Dal Moro:
## "An approximation of the non-life reserve risk distribution using the Cornish-Fisher expansion"
## available at SSRN: https://ssrn.com/abstract=2965384
MRT <- MackChainLadder(EverestLiabNP, est.sigma="Mack")
## Table 1 in the above paper
Skewness <- c(0, 0, -0.024, -0.016, -0.008, 0.100, 0.399, 0.241, 0.229, 0.129, 0.034, 0.224, 0.340, 0.017, 0.485)
OverSkew <- 0.318
OverSkewCalc <- MRT$OverSkew/MRT$Total.Mack.S.E^3
## test output from MackChainLadder
checkEquals(MRT$Skewness, Skewness,tol=0.0015, checkNames = FALSE)
checkEquals(OverSkewCalc, OverSkew,tol=0.0015, checkNames = FALSE)
}
edalmoro/ChainLadderEDM documentation built on May 15, 2019, 1:14 p.m.
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test.Mack1999 <- function() {
## by Markus Gesmann
## 2013-02-26 Don't check names of "check-values" f and f.se
## Test the MackChainLadder against example given in Mack's 1999 paper
MRT <- MackChainLadder(Mortgage, tail=1.05, tail.sigma=71, tail.se=0.02, est.sigma="Mack")
## Table 1 in the above paper
f <- c(11.10, 4.092, 1.708, 1.276, 1.139, 1.069, 1.026, 1.023, 1.05)
f.se <- c(2.24, 0.517, 0.122, 0.051, 0.042, 0.023, 0.015, 0.012, 0.02)
F.se3 <- c(7.38, 1.89, 0.357, 0.116, 0.078, 0.033, 0.015, 0.007, 0.03)
sig <- c(1337, 988.5, 440.1, 207, 164.2, 74.60, 35.49, 16.89,71)
## test output from MackChainLadder
checkEquals(MRT$f, f,tol=0.001, checkNames = FALSE)
checkEquals(MRT$f.se, f.se,tol=0.01, checkNames = FALSE)
checkEquals(as.numeric(MRT$F.se[3,]),F.se3,tol=0.001)
checkEquals(as.numeric(MRT$sigma),sig,tol=0.0001)
}
test.MackRAA <- function() {
## by Dan Murphy
## Make sure MackChainLadder run on RAA returns the correct overall
## total standard error, 26,880.74
checkEquals(sprintf("%.2f", MackChainLadder(RAA)$Total.Mack.S.E), "26880.74")
}
test.MackRAA_tail_equals_1.05 <- function() {
## by Dan Murphy
## Make sure MackChainLadder run on RAA returns the correct overall
## total standard error, 28,587.35, when input tail factor is 1.05
checkEquals(sprintf("%.2f", MackChainLadder(RAA, est.sigma = "Mack", tail = 1.05)$Total.Mack.S.E), "28669.91")
}
test.MackMortgageCVWithTail <- function() {
## by Dan Murphy
## Make sure amount in example checks out
MRT <- MackChainLadder(Mortgage, tail=1.05, tail.se = .05)
checkEquals(round(tail(MRT$Total.ParameterRisk, 1) / summary(MRT)$Totals["IBNR", ], 2), .19)
}
# test.NoDevelopmentExample <- function(){
# # If there's essentially no development from one age to the next, lm will warn
# # that it's "essentially perfect fit". Want to replace that with a better message.
# # The solution is to suppress that message, detect a perfect.fit separately,
# # and "warning" an informational message to the console.
# x <- matrix(byrow = TRUE, nrow = 4, ncol = 4,
# dimnames = list(origin = LETTERS[1:4], dev = 1:4),
# data = c(
# 100, 105, 105.00001, 105.00001,
# 200, 210, 210.00001, NA,
# 300, 315, NA, NA,
# 400, NA, NA, NA)
# )
# # This should no longer generate a warning
# m <- tryCatch(MackChainLadder(x, est.sigma = "Mack"), warning = function(w) w)
# checkTrue(inherits(m, "warning"))
# }
# test.loglinear.NA.Example <- function(){
# x <- matrix(byrow = TRUE, nrow = 4, ncol = 4,
# dimnames = list(origin = LETTERS[1:4], dev = 1:4),
# data = c(
# 100, 105, 105.00001, 105.00001,
# 200, 210, 210.00001, NA,
# 300, 315, NA, NA,
# 400, NA, NA, NA)
# )
# # This should no longer generate an error
# m <- tryCatch(MackChainLadder(x, est.sigma = "log-linear"), error = function(e) e)
# checkTrue(!inherits(m, "error"))
# }
test.MackNANissue <- function(){
MackNANTriIssue <- structure(c(72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101, NA,
101, NA, 124, NA, 124, NA, 100, NA, 100, NA, 100, NA, 100, NA,
100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 72, NA,
77, NA, 94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124,
NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 72, NA, 77, NA, 94, NA, 82, NA,
79, NA, 101, NA, 101, NA, 124, NA, 124, NA, 100, NA, 100, NA,
100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA,
NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101, NA, 101,
NA, 124, NA, 124, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, NA, NA, 72, NA, 77, NA,
94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, NA, NA, NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101,
NA, 101, NA, 124, NA, 124, NA, 100, NA, 100, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, 72, NA,
77, NA, 94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124,
NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, NA, NA, NA, NA, NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79,
NA, 101, NA, 101, NA, 124, NA, 124, NA, 100, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA,
72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124,
NA, 124, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 72, NA, 77, NA, 94, NA, 82,
NA, 79, NA, 101, NA, 101, NA, 124, NA, 124, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101, NA, 101,
NA, 124, NA, 124, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72, NA, 77, NA, 94,
NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101,
NA, 101, NA, 124, NA, 124, NA, 100, NA, 100, NA, 100, NA, 100,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72, NA, 77,
NA, 94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124, NA,
100, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101,
NA, 101, NA, 124, NA, 124, NA, 100, NA, 100, NA, 100, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72, NA, 77,
NA, 94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124, NA,
100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101,
NA, 101, NA, 124, NA, 124, NA, 100, NA, 100, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72, NA, 77,
NA, 94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124, NA,
100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 72, NA, 77, NA, 94, NA, 82, NA, 79, NA, 101,
NA, 101, NA, 124, NA, 124, NA, 100, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72, NA, 77, NA,
94, NA, 82, NA, 79, NA, 101, NA, 101, NA, 124, NA, 124, NA, 100,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 100, NA, 100, NA, 100, NA, 100, NA,
100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 100, NA, 100, NA, 100, NA, 100, NA,
100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 100, NA, 100, NA, 100, NA, 100, NA,
100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100,
NA, 100, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 100, NA, 100, NA, 100, NA, 100, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100, NA,
100, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 100, NA, 100, NA, 100, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100, NA, 100,
NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 100, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100, NA, 100, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 100, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), .Dim = c(38L, 38L),
.Dimnames = structure(
list(origin =
c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24",
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35",
"36", "37", "38"),
dev = c("V1", "V2", "V3", "V4", "V5", "V6",
"V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16",
"V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25",
"V26", "V27", "V28", "V29", "V30", "V31", "V32", "V33", "V34",
"V35", "V36", "V37", "V38")), .Names = c("origin", "dev")),
class = c("triangle",
"matrix"))
checkEquals(summary(MackChainLadder(MackNANTriIssue, est.sigma="Mack"))$Totals[1,1],
1900)
}
test.Mack1993 <- function() {
## by Eric Dal Moro
## Check the additional Skewness elements addedto MachChainLadder function on the Mack 1993 triangles
## Results are in the paper by Eric Dal Moro:
## "An approximation of the non-life reserve risk distribution using the Cornish-Fisher expansion"
## available at SSRN: https://ssrn.com/abstract=2965384
MRT <- MackChainLadder(Mack1993, est.sigma="Mack")
## Table 1 in the above paper
Skewness <- c(0, 0, -0.029, -0.043, -0.001, 0.180, 0.055, 0.267, 0.286, 0.314)
OverSkew <- 0.214
OverSkewCalc <- MRT$OverSkew/MRT$Total.Mack.S.E^3
## test output from MackChainLadder
checkEquals(MRT$Skewness, Skewness,tol=0.0015, checkNames = FALSE)
checkEquals(OverSkewCalc, OverSkew,tol=0.0015, checkNames = FALSE)
}
test.SCORLiabProp <- function() {
## by Eric Dal Moro
## Check the additional Skewness elements addedto MachChainLadder function on the Mack 1993 triangles
## Results are in the paper by Eric Dal Moro:
## "An approximation of the non-life reserve risk distribution using the Cornish-Fisher expansion"
## available at SSRN: https://ssrn.com/abstract=2965384
MRT <- MackChainLadder(SCORLiabProp, est.sigma="Mack")
## Table 1 in the above paper
Skewness <- c(0, 0, -0.032, 0.157, -0.416, 0.542, 0.418, 0.392, 0.203, 0.226, 0.036, 0.048, 0.015, 0.060, 0.925)
OverSkew <- 0.534
OverSkewCalc <- MRT$OverSkew/MRT$Total.Mack.S.E^3
## test output from MackChainLadder
checkEquals(MRT$Skewness, Skewness,tol=0.0015, checkNames = FALSE)
checkEquals(OverSkewCalc, OverSkew,tol=0.0015, checkNames = FALSE)
}
test.SCORMotorNP <- function() {
## by Eric Dal Moro
## Check the additional Skewness elements addedto MachChainLadder function on the Mack 1993 triangles
## Results are in the paper by Eric Dal Moro:
## "An approximation of the non-life reserve risk distribution using the Cornish-Fisher expansion"
## available at SSRN: https://ssrn.com/abstract=2965384
MRT <- MackChainLadder(SCORMotorNP, est.sigma="Mack")
## Table 1 in the above paper
Skewness <- c(0, 0, -0.037, -0.138, -0.115, -0.026, 0.059, 0.101, 0.236, 0.185, 0.271, 0.142, 0.174, 0.278, 1.001)
OverSkew <- 0.333
OverSkewCalc <- MRT$OverSkew/MRT$Total.Mack.S.E^3
## test output from MackChainLadder
checkEquals(MRT$Skewness, Skewness,tol=0.0015, checkNames = FALSE)
checkEquals(OverSkewCalc, OverSkew,tol=0.0015, checkNames = FALSE)
}
test.EverestLiabProp <- function() {
## by Eric Dal Moro
## Check the additional Skewness elements addedto MachChainLadder function on the Mack 1993 triangles
## Results are in the paper by Eric Dal Moro:
## "An approximation of the non-life reserve risk distribution using the Cornish-Fisher expansion"
## available at SSRN: https://ssrn.com/abstract=2965384
MRT <- MackChainLadder(EverestLiabProp, est.sigma="Mack")
## Table 1 in the above paper
Skewness <- c(0, 0, 0.042, 0.087, 0.065, -0.268, -0.234, -0.164, 0.075, 0.157, 0.245, 0.289, 0.168, 0.231, 0.547)
OverSkew <- 0.321
OverSkewCalc <- MRT$OverSkew/MRT$Total.Mack.S.E^3
## test output from MackChainLadder
checkEquals(MRT$Skewness, Skewness,tol=0.0015, checkNames = FALSE)
checkEquals(OverSkewCalc, OverSkew,tol=0.0015, checkNames = FALSE)
}
test.EverestLiabNP <- function() {
## by Eric Dal Moro
## Check the additional Skewness elements addedto MachChainLadder function on the Mack 1993 triangles
## Results are in the paper by Eric Dal Moro:
## "An approximation of the non-life reserve risk distribution using the Cornish-Fisher expansion"
## available at SSRN: https://ssrn.com/abstract=2965384
MRT <- MackChainLadder(EverestLiabNP, est.sigma="Mack")
## Table 1 in the above paper
Skewness <- c(0, 0, -0.024, -0.016, -0.008, 0.100, 0.399, 0.241, 0.229, 0.129, 0.034, 0.224, 0.340, 0.017, 0.485)
OverSkew <- 0.318
OverSkewCalc <- MRT$OverSkew/MRT$Total.Mack.S.E^3
## test output from MackChainLadder
checkEquals(MRT$Skewness, Skewness,tol=0.0015, checkNames = FALSE)
checkEquals(OverSkewCalc, OverSkew,tol=0.0015, checkNames = FALSE)
}
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