cokannisto {MortCast}

Coherent Kannisto Method

Description

Extrapolate given mortality rates into higher ages using the Coherent Kannisto method as described in Sevcikova et al. (2016).

Usage

cokannisto(
  mxM,
  mxF,
  est.ages = seq(80, 95, by = 5),
  proj.ages = seq(100, 130, by = 5)
)

Arguments

mxM

A vector or matrix of male mortality rates. If it is a matrix, rows correspond to age groups with rownames identifying the corresponding age as integers. By default five-years age groups are assigned to rows if rownames are not given.

mxF

A vector or matrix of female mortality rates. Its length or dimension should be the same mxM.

est.ages

A vector of integers identifying the ages to be used for estimation. It should be a subset of rownames of mxM. Change the defaults if 1-year age groups are used (see Example in kannisto).

proj.ages

A vector of integers identifying the age groups for which mortality rates are to be projected. Change the defaults if 1-year age groups are used (see Example in kannisto).

Details

The function first estimates the coherent Kannisto parameters by passing mortality rates for age groups est.ages into the cokannisto.estimate function. The estimated parameters are then passed to the projection function kannisto.predict to extrapolate into ages proj.ages. Lastly, the input mortality objects are extended by results for the extrapolated ages. If proj.ages contains age groups that are included in mxM and mxF, values for those age groups are overwritten.

Value

A list of two vectors or matrices (for male and female) containing the input motality objects extended by the extrapolated age groups.

References

Sevcikova H., Li N., Kantorova V., Gerland P., Raftery A.E. (2016). Age-Specific Mortality and Fertility Rates for Probabilistic Population Projections. In: Schoen R. (eds) Dynamic Demographic Analysis. The Springer Series on Demographic Methods and Population Analysis, vol 39. Springer, Cham

See Also

cokannisto.estimate, kannisto.predict

Examples

data(mxM, mxF, package = "wpp2017")
country <- "South Africa"
mxm <- subset(mxM, name == country)[,-(1:3)]
mxf <- subset(mxF, name == country)[,-(1:3)]
rownames(mxm) <- rownames(mxf) <- c(0,1, seq(5, 100, by=5))
mxnew <- cokannisto(mxm, mxf)
ages <- as.integer(rownames(mxnew$male))
plot(ages, mxnew$male[,"2095-2100"], type="l", log="y", 
    xlab="age", ylab="mx", col="blue", main=country)
lines(ages, mxnew$female[,"2095-2100"], col="red")
lines(ages, mxnew$male[,"2010-2015"], lty=2, col="blue")
lines(ages, mxnew$female[,"2010-2015"], lty=2, col="red")
legend("bottomright", legend=c("male 2010-2015", "female 2010-2015",
    "male 2095-2100", "female 2095-2100"), bty="n",
    col=rep(c("blue", "red"),2), lty=c(2,2,1,1))