DoubleGap: Fit the DoubleGap Life Expectancy Forecasting Model
In MortalityGaps: The Double-Gap Life Expectancy Forecasting Model

Description Usage Arguments Value Author(s) References See Also Examples

Fit a DoubleGap 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 (2017).

1 2	DoubleGap(DF, DM, age, country, years, arima.order = NULL, drift = NULL, tau = NULL, A = NULL)

`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 recods 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 contry 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.

The output is of "DoubleGap" class with the components:

`input`	List with arguments provided in input. Saved for convenience.
`call`	Short information about the model.
`coefficients`	Estimated coefficients.
`fitted.values`	Fitted values of the selected model.
`observed.values`	Country specific observed values.
`model.parts`	Object containing detailed results of the fitted model.
`residuals`	Deviance residuals.

Marius D. Pascariu

Pascariu M.D., Canudas-Romo V. and Vaupel W.J. 2017. The double-gap life expectancy forecasting model. Insurance: Mathematics and Economics Volume 78, January 2018, Pages 339-350.

predict.DoubleGap

# 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)