Forecast functional demographic model.
The coefficients from the fitted object are forecast using a univariate time series model. The forecast coefficients are then multiplied by the basis functions to obtain a forecast demographic rate curve.
## S3 method for class 'fdm'
forecast(object, h = 50, level = 80,
jumpchoice = c("fit", "actual"), method = "arima",
warnings = FALSE, ...)object |
Output from |
h |
Forecast horizon. |
level |
Confidence level for prediction intervals. |
jumpchoice |
If "actual", the forecasts are bias-adjusted by the difference between the fit and the last year of observed data. Otherwise, no adjustment is used. |
method |
Forecasting method to be used. |
warnings |
If TRUE, warnings arising from the forecast models for
coefficients will be shown. Most of these can be ignored, so the default is
|
... |
Other arguments as for |
Object of class fmforecast with the following components:
label |
Name of 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. |
error |
Matrix of one-step errors for historical data |
fitted |
Matrix of one-step forecasts for historical data |
coeff |
List of objects of type |
coeff.error |
One-step errors for each of the coefficients. |
var |
List containing the various components of variance: model, error, mean, total and coeff. |
model |
Fitted model in |
type |
Type of data: “mortality”, “fertility” or “migration”. |
Rob J Hyndman
france.fit <- fdm(fr.mort,order=2) france.fcast <- forecast(france.fit,50) plot(france.fcast) models(france.fcast)
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