Package 'MortalityGaps'

Title: The Double-Gap Life Expectancy Forecasting Model
Description: Life expectancy is highly correlated over time among countries and between males and females. These associations can be used to improve forecasts. Here we have implemented a method for forecasting female life expectancy based on analysis of the gap between female life expectancy in a country compared with the record level of female life expectancy in the world. Second, to forecast male life expectancy, the gap between male life expectancy and female life expectancy in a country is analysed. We named this method the Double-Gap model. For a detailed description of the method see Pascariu et al. (2018). <doi:10.1016/j.insmatheco.2017.09.011>.
Authors: Marius D. Pascariu [aut, cre]
Maintainer: Marius D. Pascariu <[email protected]>
License: GPL-3
Version: 1.0.6
Built: 2025-02-27 03:16:57 UTC
Source: https://github.com/mpascariu/mortalitygaps

Help Index

Fit the Double-Gap Life Expectancy Forecasting Model

Description

Fit a Double-Gap model for forecasting life expectancy. The method combines separate forecasts to obtain joint male and female life expectancies that are coherent with a best-practice trend. See the entire description and mathematical formulation of the model in Pascariu et al. (2018)

Usage

DoubleGap(DF, DM, age, country, years, arima.order = NULL,
  drift = NULL, tau = NULL, A = NULL)

Arguments

DF

data.frame containing life expectancy records for females. The table must contain the following 4 columns: country, year, age, ex.
DM

data.frame containing life expectancy records for males. The table must have the same format and dimensions as DF.
age

Indicate the age for which the model to be fitted. Assuming DF and DM contain records for different ages, this argument it is used to subset the data. If you want to fit the model for age 0, add age = 0. Type: scalar.
country

Indicate for which country you want to fit the model. The country name or code must exist in DF and DM. Type: character.
years

Period of time to be used. Type: numeric vector.
arima.order

A specification of the the ARIMA model to be used in fitting the best-practice gap. The ARIMA order is country specific. The three integer components (p, d, q) are the AR order, the degree of differencing, and the MA order. Format: numerical vector of length 3. If arima.order = NULL the function conducts a search over possible models according to AIC. See auto.arima for details.
drift

Indicate whether the ARIMA model should include a linear drift term or not. Type: logical value. If drift = NULL, it will be estimate automatically.
tau

The level of female life expectancy at which the sex-gap is expected to stop widening and to start narrowing. If NULL then the model will run an algorithm to find it.
A

The level of female life expectancy where we assume no further change in the sex-gap. If NULL the model will estimate it.

Value

The output is of class DoubleGap with the components:

input

List with arguments provided in input. Saved for convenience.
call

An unevaluated function call, that is, an unevaluated expression which consists of the named function applied to the given arguments.
coefficients

Estimated coefficients.
fitted.values

Fitted values of the selected model.
observed.values

Country specific observed values obtained from input data. This is a data.frame containing information about female, male and best-practice life expectancy measures over time, together with associated gaps in life expectancy.
model.parts

Object containing detailed results of the fitted model.
residuals

Deviance residuals.

Author(s)

Marius D. Pascariu

References

Pascariu MD, Canudas-Romo V, Vaupel JW (2018). “The double-gap life expectancy forecasting model.” Insurance: Mathematics and Economics, 78, 339–350. doi:10.1016/j.insmatheco.2017.09.011.

See Also

predict.DoubleGap

Examples

# Input data ------------------------------------
# Collection of life expectancies for female populations
exF <- MortalityGaps.data$exF
# Life expectancy for male populations
exM <- MortalityGaps.data$exM

# Example 1 ----------------------------------------------
# Fit DG model at age 0 for Australia using data from 1950 to 2014
M0 <- DoubleGap(DF = exF,
                DM = exM,
                age = 0,
                country = "AUS",
                years = 1950:2014)
M0
summary(M0)
ls(M0)

# Forecast life expectancy in Australia until 2030
P0 <- predict(M0, h = 16)
P0
# Plot the results
plot(P0)

## Not run: 
# Example 2 ----------------------------------------------
# Fit DG model at age 0 for Sweden. Provide details about models.
# Reproduce published results in the article.
M1 <- DoubleGap(DF = exF, 
                DM = exM, 
                age = 0, 
                country = "SWE", 
                years = 1950:2014, 
                arima.order = c(2, 1, 1), 
                drift = TRUE, 
                tau = 75, 
                A = 86)
summary(M1)
# Predict model 
P1 <- predict(M1, h = 36)
plot(P1)

# Example 3 ----------------------------------------------
# Fit DG model for USA at age 65.
M2 <- DoubleGap(DF = exF, 
                DM = exM, 
                age = 65, 
                country = "USA", 
                years = 1950:2014, 
                arima.order = c(0, 1, 0), 
                drift = FALSE, 
                tau = 15, 
                A = 24)
summary(M2)
# Predict model 
P2 <- predict(M2, h = 36)
plot(P2)

## End(Not run)

MortalityGaps: The Double-Gap Life Expectancy Forecasting Model

Description

Life expectancy is highly correlated over time among countries and between males and females. These associations can be used to improve forecasts. Here we have implemented a method for forecasting female life expectancy based on analysis of the gap between female life expectancy in a country compared with the record level of female life expectancy in the world. Second, to forecast male life expectancy, the gap between male life expectancy and female life expectancy in a country is analysed. We named this method the Double-Gap model. For a detailed description of the method see Pascariu et al. (2018). <doi:10.1016/j.insmatheco.2017.09.011>.

Details

To learn more about the package, start with the vignettes: browseVignettes(package = "MortalityGaps")

Author(s)

Maintainer: Marius D. Pascariu [email protected] (0000-0002-2568-6489)

References

Human Mortality Database (2017). “University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). Data downloaded on 10/06/2017.” https://www.mortality.org.

Pascariu MD (2018). MortalityLaws: Parametric Mortality Models, Life Tables and HMD. R package version 1.6.0, https://github.com/mpascariu/MortalityLaws.

Pascariu MD, Canudas-Romo V, Vaupel JW (2018). “The double-gap life expectancy forecasting model.” Insurance: Mathematics and Economics, 78, 339–350. doi:10.1016/j.insmatheco.2017.09.011.

See Also

Useful links:

DATA - for testing purposes

Description

Dataset containing records of life expectancy at birth and at age 65 for female and male populations living in 38 countries between 1950 and 2014. This dataset is used in Pascariu et al. (2018) article. The data is provided in the package for testing purposes and to ensure the reproducibility of the results and figures published in the article. By the time you are using this package, the data might be outdated. Download actual demographic data free of charge from Human Mortality Database (2017). Once a username and a password is created on the website the MortalityLaws R package can be used to extract data directly into your R console.

Usage

MortalityGaps.data

Format

An object of class MortalityGaps.data of length 2.

Source

Human Mortality Database

References

Human Mortality Database (2017). “University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). Data downloaded on 10/06/2017.” https://www.mortality.org.

Pascariu MD (2018). MortalityLaws: Parametric Mortality Models, Life Tables and HMD. R package version 1.6.0, https://github.com/mpascariu/MortalityLaws.

Pascariu MD, Canudas-Romo V, Vaupel JW (2018). “The double-gap life expectancy forecasting model.” Insurance: Mathematics and Economics, 78, 339–350. doi:10.1016/j.insmatheco.2017.09.011.

See Also

ReadHMD

Generic Plot Function for Class predict.DoubleGap

Description

Generic Plot Function for Class predict.DoubleGap

Usage

## S3 method for class 'predict.DoubleGap'
plot(x, show.legend = TRUE, ylim = NULL,
  asp = 1.8, xlab = "\nYear", ylab = "\nLife Expectancy Level",
  ...)

Arguments

x

An object of class predict.DoubleGap
show.legend

Logical. Indicate whether to display the legend or not. Default: TRUE.
ylim

Numeric vectors of length 2, giving the x and y coordinates ranges.
asp

Numeric, giving the aspect ratio y/x.
xlab

A title for the x axis: see title.
ylab

A title for the y axis: see title.
...

Further graphical parameters as in par.

Author(s)

Marius D. Pascariu

See Also

DoubleGap

Examples

# Complete examples are provided in help page of the DoubleGap function.

Generic Predict Function for Class DoubleGap

Description

Predict DoubleGap model

Usage

## S3 method for class 'DoubleGap'
predict(object, h, iter = 500, ci = c(0.8, 0.95),
  ...)

Arguments

object

An object of class DoubleGap.
h

Number of periods for forecasting.
iter

Number of iterations. Default: 500
ci

Confidence levels. Default: c(0.8, 0.95)
...

Additional arguments affecting the predictions produced.

Value

A list containing predicted value of best-practice life expectancy, best-practice gap, sex gap, and forecast life expectancy for females and males (together with prediction intervals).

Author(s)

Marius D. Pascariu

See Also

DoubleGap

Examples

# Complete examples are provided in help page of the DoubleGap function.