simulate_gsmvar_int {gmvarkit} | R Documentation |
INTERNAL Simulate method for class 'gsmvar' objects
Description
simulate_gsmvar_int
an internal a simulation function for class 'gsmvar' objects.
It allows to simulate observations from a GMVAR, StMVAR, or G-StMVAR process.
Usage
simulate_gsmvar_int(
object,
nsim = 1,
seed = NULL,
...,
init_values = NULL,
init_regimes = 1:sum(gsmvar$model$M),
ntimes = 1,
drop = TRUE,
girf_pars = NULL
)
Arguments
object |
an object of class |
nsim |
number of observations to be simulated. |
seed |
set seed for the random number generator? |
... |
currently not in use. |
init_values |
a size |
init_regimes |
a numeric vector of length at most |
ntimes |
how many sets of simulations should be performed? |
drop |
if |
girf_pars |
This argument is used internally in the estimation of generalized impulse response functions (see
|
Details
The argument ntimes
is intended for forecasting: a GMVAR, StMVAR, or G-StMVAR process can be forecasted by simulating
its possible future values. One can easily perform a large number simulations and calculate the sample quantiles from the simulated
values to obtain prediction intervals (see the forecasting example).
Value
If drop==TRUE
and ntimes==1
(default): $sample
, $component
, and $mixing_weights
are matrices.
Otherwise, returns a list with...
$sample
a size (
nsim
\times d \times
ntimes
) array containing the samples: the dimension[t, , ]
is the time index, the dimension[, d, ]
indicates the marginal time series, and the dimension[, , i]
indicates the i:th set of simulations.$component
a size (
nsim
\times
ntimes
) matrix containing the information from which mixture component each value was generated from.$mixing_weights
a size (
nsim
\times M \times
ntimes
) array containing the mixing weights corresponding to the sample: the dimension[t, , ]
is the time index, the dimension[, m, ]
indicates the regime, and the dimension[, , i]
indicates the i:th set of simulations.
References
Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.
Lütkepohl H. 2005. New Introduction to Multiple Time Series Analysis, Springer.
McElroy T. 2017. Computation of vector ARMA autocovariances. Statistics and Probability Letters, 124, 92-96.
Virolainen S. 2025. A statistically identified structural vector autoregression with endogenously switching volatility regime. Journal of Business & Economic Statistics, 43, 1, 44-54.
Virolainen S. 2022. Gaussian and Student's t mixture vector autoregressive model with application to the asymmetric effects of monetary policy shocks in the Euro area. Unpublished working paper, available as arXiv:2109.13648.
See Also
fitGSMVAR
, GSMVAR
, diagnostic_plot
, predict.gsmvar
,
profile_logliks
, quantile_residual_tests
, GIRF
, GFEVD