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Forecast functional demographic model.

Source: R/fdm.R
forecast.fdm.Rd

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.

Usage

# S3 method for fdm
forecast(
  object,
  h = 50,
  level = 80,
  jumpchoice = c("fit", "actual"),
  method = "arima",
  warnings = FALSE,
  ...
)

Arguments

object

Output from fdm.

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 warnings=FALSE.

...

Other arguments as for forecast.ftsm.

Value

Object of class fmforecast with the following components:

label

Name of region from which the data are taken.

age

Ages from lcaout object.

year

Years from lcaout object.

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 forecast containing the coefficients and their forecasts.

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 obj.

type

Type of data: “mortality”, “fertility” or “migration”.

See also

fdm, forecast.lca, forecast.ftsm.

Author

Rob J Hyndman

Examples

france.fit <- fdm(fr.mort,order=2)
france.fcast <- forecast(france.fit,50)
plot(france.fcast)

models(france.fcast)
#> 
#> -- Coefficient 1 --
#> Series: xx[, i] 
#> ARIMA(1,1,1) with drift 
#> 
#> Coefficients:
#>          ar1      ma1    drift
#>       0.6383  -0.8145  -0.1265
#> s.e.  0.1481   0.1120   0.0411
#> 
#> sigma^2 = 1.206:  log likelihood = -285.97
#> AIC=579.95   AICc=580.16   BIC=592.93
#> 
#> -- Coefficient 2 --
#> Series: xx[, i] 
#> ARIMA(2,1,1) 
#> 
#> Coefficients:
#>          ar1     ar2      ma1
#>       0.3818  0.2926  -0.9679
#> s.e.  0.0758  0.0745   0.0280
#> 
#> sigma^2 = 0.9067:  log likelihood = -259.4
#> AIC=526.79   AICc=527.01   BIC=539.78