# mortcast: Coherent Rotated Lee-Carter Prediction In MortCast: Estimation and Projection of Age-Specific Mortality Rates

## Description

Predict age-specific mortality rates using the coherent rotated Lee-Carter method.

## Usage

 `1` ```mortcast(e0m, e0f, lc.pars, rotate = TRUE, keep.lt = FALSE) ```

## Arguments

 `e0m` A time series of future male life expectancy. `e0f` A time series of future female life expectancy. `lc.pars` A list of coherent Lee-Carter parameters with elements `bx`, `ultimate.bx`, `female` and `male` as returned by `lileecarter.estimate`. The `female` and `male` objects are again lists that should contain a vector `ax` and optionally a matrix `axt` if the a_x parameter needs to be defined as time dependent. In such a case, rows are age groups and columns are time periods corresponding to the length of the `e0f` and `e0m` vectors. `rotate` If `TRUE` the rotation method of b_x is used as described in Li et al. (2013). `keep.lt` Logical. If `TRUE` additional life table columns are kept in the resulting object.

## Details

This function implements Steps 6-9 of Algorithm 2 in Sevcikova et al. (2016). It uses an abridged life table function to find the level of mortality that coresponds to the given life expectancy.

## Value

List with elements `female` and `male`, each of which contains a matrix `mx` with the predicted mortality rates. If `keep.lt` is `TRUE`, it also contains matrices `sr` (survival rates), and life table quantities `Lx` and `lx`.

## References

Li, N., Lee, R. D. and Gerland, P. (2013). Extending the Lee-Carter method to model the rotation of age patterns of mortality decline for long-term projections. Demography, 50, 2037-2051.

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

`rotate.leecarter`, `leecarter.estimate`, `lileecarter.estimate`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```data(mxM, mxF, e0Fproj, e0Mproj, package = "wpp2017") country <- "Brazil" # estimate parameters from historical mortality data mxm <- subset(mxM, name == country)[,4:16] mxf <- subset(mxF, name == country)[,4:16] rownames(mxm) <- rownames(mxf) <- c(0,1, seq(5, 100, by=5)) lc <- lileecarter.estimate(mxm, mxf) # project into future e0f <- as.numeric(subset(e0Fproj, name == country)[-(1:2)]) e0m <- as.numeric(subset(e0Mproj, name == country)[-(1:2)]) pred <- mortcast(e0m, e0f, lc) # plot first projection in black and the remaining ones in grey plot(pred\$female\$mx[,1], type="l", log="y", ylim=range(pred\$female\$mx), ylab="female mx", xlab="Age", main=country) for(i in 2:ncol(pred\$female\$mx)) lines(pred\$female\$mx[,i], col="grey") ```

MortCast documentation built on Sept. 22, 2018, 9:03 a.m.