simulate.bootiMoMo: Simulate future sample paths from a Bootstrapped Improvement...
In amvillegas/iMoMo: Mortality Improvement Rate Modelling

Description Usage Arguments Details Value See Also

Simulate mortaility improvment rates and mortality rates using the a bootstrapped a mortality improvement rate model. The period indexes are κ_t^{(i)}, i = 1,..N, are forecasted using integrated vector autoregressive model. The cohort index γ_{t-x} is forecasted using an ARIMA(p, d, q). By default an ARIMA(1, 1, 0) with a constant is used.

## S3 method for class 'bootiMoMo'
simulate(
  object,
  nsim = 1,
  seed = NULL,
  h = 50,
  kt.order = c(1, 0, 0),
  kt.include.constant = TRUE,
  kt.include.trend = FALSE,
  gc.order = c(1, 0, 0),
  gc.include.constant = TRUE,
  jumpRates = NULL,
  kt.lookback = NULL,
  gc.lookback = NULL,
  ...
)

`object`	an object of class `"bootiMoMo"` with the bootstrapped parameters of an improvement rate model.
`nsim`	number of sample paths to simulate from each bootstrapped sample. Thus if there are `nBoot` bootstrapped samples the total number of paths will be `nsim * nBoot`.
`seed`	either `NULL` or an integer that will be used in a call to `set.seed` before simulating the time series. The default, `NULL` will not change the random generator state.
`h`	number of years ahead to forecast.
`kt.order`	an optional vector indicating the order of autorregression and of differetiation of the VARI model. The two components (p, d) are the AR order and the degree of differencing.
`kt.include.constant`	a logical value indicating if the VARI model should include a constant value. The default is `TRUE`.
`kt.include.trend`	a logical value indicating if the VARI model should have a linear trend. The default is `FALSE`.
`gc.order`	a specification of the ARIMA model for the cohort effect: the three components (p, d, q) are the AR order, the degree of differencing, and the MA. The default is an ARIMA(1, 0, 0).
`gc.include.constant`	a logical value indicating if the ARIMA model should include a constant value. The default is `TRUE`.
`jumpRates`	optional vector of moratlity rates for the last year used as starting rates. for the projection. If it is not provided the rates from the the actual rates from the final year are used.
`kt.lookback`	optional argument to specify the look-back window to use in the estimation of the time series model for the period indexes. By default all the estimated values are used. If `kt.lookback` is provided then the last `kt.lookback` years of κ_t^{(i)}, i = 1,..N, are used.
`gc.lookback`	optional argument to specify the look-back window to use in the estimation of the ARIMA model for the cohort effect. By default all the estimated values are used in estimating the ARIMA model. If `gc.lookback` is provided then the last `gc.lookback` years of γ_{t-x} are used.
`...`	other arguments.

For further details see simulate.fitiMoMo.

A list of class "simiMoMo" with components:

`improvements`	a three dimensional array with the future simulated improvement rates.
`rates`	a three dimensional array with the future simulated mortality rates.
`ages`	vector of ages corresponding to the first dimension of `improvements`.
`years`	vector of years for which a forecast has been produced. This corresponds to the second dimension of `improvements`.

`kt.s`	information on the simulated paths of the period indexes of the model. This is a list with the `model` fitted to κ_t; the simulated paths `sim`; and the `years` for which a forecast was produced. If the model does not have any age-period terms (i.e. N=0) this is set to `NULL`.
`gc.s`	information on the simulated paths of the cohort index of the model. This is a list with the `model` fitted to γ_c; the simulated paths (`sim`); and the `cohorts` for which simulations were produced. If the mortality model does not have a cohort effect this is set to `NULL`.
`fittedImprovements`	a three dimensional array with the in-sample improvements of the model for the years for which the improvement rate model was fitted.