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.
# S3 method for lca
forecast(
object,
h = 50,
se = c("innovdrift", "innovonly"),
jumpchoice = c("fit", "actual"),
level = 80,
...
)Object of class fmforecast with the following components:
Region from which the data are taken.
Ages from object.
Years from object.
List 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.
Matrix of one-step forecasts for historical data
Other components included are
Forecasts of life expectancies (including lower and upper bounds)
Forecasts of coefficients from the model.
Data type.
Details about the fitted model
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).
Method used for computation of jumpchoice. Possibilities: “actual” (use actual rates from final year) and “fit” (use fitted rates).
Confidence level for prediction intervals.
Other arguments.
Rob J Hyndman
lca, plot.fmforecast
france.lca <- lca(fr.mort, adjust="e0")
france.fcast <- forecast(france.lca, 50)
plot(france.fcast)
plot(france.fcast,'c')
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