R: DEPRECATED! USE THE FUNCTION simulate.gsmvar INSTEAD!...

simulateGMVAR {gmvarkit}

R Documentation

DEPRECATED! USE THE FUNCTION simulate.gsmvar INSTEAD! Simulate from GMVAR process

Description

DEPRECATED! USE THE FUNCTION simulate.gsmvar INSTEAD! simulateGMVAR simulates observations from a GMVAR

Usage

simulateGMVAR(
  gmvar,
  nsimu,
  init_values = NULL,
  ntimes = 1,
  drop = TRUE,
  seed = NULL,
  girf_pars = NULL
)

Arguments

`gmvar`	object of class 'gmvar'
`nsimu`	number of observations to be simulated.
`init_values`	a size `(pxd)` matrix specifying the initial values, where d is the number of time series in the system. The last row will be used as initial values for the first lag, the second last row for second lag etc. If not specified, initial values will be drawn according to mixture distribution specifed by the argument `init_regimes`.
`ntimes`	how many sets of simulations should be performed?
`drop`	if `TRUE` (default) then the components of the returned list are coerced to lower dimension if `ntimes==1`, i.e., `$sample` and `$mixing_weights` will be matrices, and `$component` will be vector.
`seed`	set seed for the random number generator?
`girf_pars`	This argument is used internally in the estimation of generalized impulse response functions (see `?GIRF`). You should ignore it (specifying something else than null to it will change how the function behaves).

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 x d x 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 x ntimes) matrix containing the information from which mixture component each value was generated from.
$mixing_weights: a size (nsim x M x 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. (forthcoming). A statistically identified structural vector autoregression with endogenously switching volatility regime. Journal of Business & Economic Statistics.
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.