rh {StMoMo} | R Documentation |
Create a Renshaw and Haberman (Lee-Carter with cohorts) mortality model
Description
Utility function to initialise a StMoMo
object representing a
Renshaw and Haberman (Lee-Carter with cohorts) mortality model introduced
in Renshaw and Haberman (2006).
Usage
rh(link = c("log", "logit"), cohortAgeFun = c("1", "NP"),
approxConst = FALSE)
Arguments
link |
defines the link function and random component associated with
the mortality model. |
cohortAgeFun |
defines the cohort age modulating parameter
|
approxConst |
defines if the approximate identifiability constraint of
Hunt and Villegas (2015) is applied or not. If |
Details
The created model is either a log-Poisson or a logit-Binomial version of the Renshaw and Haberman model which has predictor structure
or
depending on the value of argument cohortAgeFun
.
To ensure identifiability the following constraints are imposed
plus
if cohortAgeFun = "NP"
In addition, if approxConst=TRUE
then the approximate
identifiability constraint
is applied to improve the stability and robustness of the model (see Hunt and Villegas (2015)).
By default as this model has shown to be more
stable (see Haberman and Renshaw (2011) and Hunt and Villegas (2015)).
Value
An object of class "StMoMo"
or "rh"
.
References
Haberman, S., & Renshaw, A. (2011). A comparative study of parametric mortality projection models. Insurance: Mathematics and Economics, 48(1), 35-55.
Hunt, A., & Villegas, A. M. (2015). Robustness and convergence in the Lee-Carter model with cohorts. Insurance: Mathematics and Economics, 64, 186-202.
Renshaw, A. E., & Haberman, S. (2006). A cohort-based extension to the Lee-Carter model for mortality reduction factors. Insurance: Mathematics and Economics, 38(3), 556-570.
See Also
Examples
LCfit <- fit(lc(), data = EWMaleData, ages.fit = 55:89)
wxt <- genWeightMat(55:89, EWMaleData$years, clip = 3)
RHfit <- fit(rh(), data = EWMaleData, ages.fit = 55:89, wxt = wxt,
start.ax = LCfit$ax, start.bx = LCfit$bx, start.kt = LCfit$kt)
plot(RHfit)
#Impose approximate constraint as in Hunt and Villegas (2015)
## Not run:
RHapprox <- rh(approxConst = TRUE)
RHapproxfit <- fit(RHapprox, data = EWMaleData, ages.fit = 55:89,
wxt = wxt)
plot(RHapproxfit)
## End(Not run)