forecast.lca: Forecast demogdata data using Lee-Carter method.

Description

The kt coefficients are forecast using a random walk with drift. The forecast coefficients are then multiplied by bx to obtain a forecast demographic rate curve.

Usage

## S3 method for class 'lca':
forecast(object, h = 50, se = c("innovdrift", "innovonly"),
				 jumpchoice = c("fit", "actual"), level = 80, ...)

Arguments

object

Output from lca.

Number of years ahead to forecast.

Method used for computation of standard error. Possibilities: innovdrift (innovations and drift) and innovonly (innovations only).

jumpchoice

Method used for computation of jumpchoice. Possibilities: actual (use actual rates from final year) and fit (use fitted rates).

level

Confidence level for prediction intervals.

...

Other arguments.

Value

Object of class fmforecast with the following components:
labelRegion from which the data are taken.
ageAges from object.
yearYears from object.
rateList of matrices containing forecasts, lower bound and upper bound of prediction intervals. Point forecast matrix takes the same name as the series that has been forecast.
fittedMatrix of one-step forecasts for historical data
Other components included are
e0Forecasts of life expectancies (including lower and upper bounds)
kt.fForecasts of coefficients from the model.
typeData type.
modelDetails about the fitted model

Examples

Run this code

france.lca <- lca(fr.mort, adjust="e0")
france.fcast <- forecast(france.lca, 50)
plot(france.fcast)
plot(france.fcast,'c')

Run the code above in your browser using DataLab