Description Usage Arguments Details Value
View source: R/simulatefitiMoMo.R
Simulate mortaility improvment rates and mortality rates using the fit from 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 |
object |
an object of class |
nsim |
number of sample paths to simulate. |
seed |
either |
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 |
kt.include.trend |
a logical value indicating if the VARI model
should have a linear trend. The default is |
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 |
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
|
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 |
... |
other arguments. |
The modelling of the period indexes kappa_t is done using a integrated vector autoregressive model of differencing order d and autorregressive order p:
Δ^d k_t = C+Dt+∑_{i=1}^p A_i Δ^d k_{t-i} + ε_t
where C and D are N-dimensional vectors for parameters and
A_1,...,A_p are N\times N matrices of autoregressive parampeters.
If kt.include.constant
is TRUE
then C is included in the
equation. Similarly, if kt.include.trend
is TRUE
then D
is included in the equation.
Fitting and simulating of the VAR model is done using the fucntion
simpleVAR2
.
Fitting and simulating of the ARIMA model for the cohort index
is done with function Arima
from package
forecast. See the latter function for further details on
input arguments gc.order
and gc.include.constant
.
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 |
years |
vector of years for which a forecast has been produced. This
corresponds to the second dimension of |
#'
kt.s |
information on the simulated paths of the period indexes of the model.
This is a list with the |
gc.s |
information on the simulated paths of the cohort index of
the model. This is a list with the |
fittedImprovements |
a three dimensional array with the in-sample improvements of the model for the years for which the improvement rate model was fitted. |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.