Forecast demogdata data using Lee-Carter method.
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 class 'lca'
forecast(object, h = 50, se = c("innovdrift",
"innovonly"), jumpchoice = c("fit", "actual"), level = 80, ...)object |
Output from |
h |
Number of years ahead to forecast. |
se |
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. |
Object of class fmforecast with the following components:
label |
Region from which the data are taken. |
age |
Ages from |
year |
Years from |
rate |
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. |
fitted |
Matrix of one-step forecasts for historical data |
Other components included are
e0 |
Forecasts of life expectancies (including lower and upper bounds) |
kt.f |
Forecasts of coefficients from the model. |
type |
Data type. |
model |
Details about the fitted model |
Rob J Hyndman
france.lca <- lca(fr.mort, adjust="e0") france.fcast <- forecast(france.lca, 50) plot(france.fcast) plot(france.fcast,'c')
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