simulate.gsmar INSTEAD! Simulate observations from GMAR, StMAR, and G-StMAR processessimulateGSMAR simulates observations from the specified GMAR, StMAR, or G-StMAR process.
Can be utilized for forecasting future values of the process. DEPRECATED, USE simulate.gsmar INSTEAD!
simulateGSMAR(
object,
nsim,
init_values = NULL,
ntimes = 1,
drop = TRUE,
gsmar = NULL,
nsimu = NULL
)If drop==TRUE and ntimes==1 (default): $sample and $component are vectors
and $mixing_weights is a (nsim\(xM\)) matrix. Otherwise, returns a list with...
$samplea size (nsim\(x\)ntimes) matrix containing the simulated values.
$componenta size (nsim\(x\)ntimes) matrix containing the information from which
mixture component each value was generated from.
$mixing_weightsa size (nsim\(xMx\)ntimes) array containing the mixing weights corresponding to the
sample: the dimension [i, , ] is the time index, the dimension [, i, ] indicates the regime, and the dimension
[, , i] indicates the i:th set of simulations.
object of class 'gsmar', typically created with the function fitGSMAR or GSMAR.
a positive integer specifying how many values (ahead from init_values) will be simulated.
a numeric vector with length >=p specifying the initial values for the simulation. The last
element will be used as the initial value for the first lag, the second last element will be initial value for the second lag, etc.
If NULL, initial values will be simulated from the process's stationary distribution.
a positive integer specifying how many sets of simulations should be performed.
if TRUE (default) then the components of the returned list are coerced to lower dimension if ntimes==1,
i.e., $sample and $component will be vectors and $mixing_weights will be matrix.
a class 'gsmar' object, typically generated by fitGSMAR or GSMAR.
a positive integer specifying how many values (ahead from init_values) will be simulated.
DEPRECATED, USE simulate.gsmar INSTEAD!
The argument ntimes is intended for forecasting: a GSMAR process can be forecasted by simulating its
possible future values. One can perform a large number of sets of simulations and calculate the sample quantiles from
the simulated values to obtain prediction intervals. See the forecasting example below for a hand-on demonstration.
Galbraith, R., Galbraith, J. 1974. On the inverses of some patterned matrices arising in the theory of stationary time series. Journal of Applied Probability 11, 63-71.
Kalliovirta L. (2012) Misspecification tests based on quantile residuals. The Econometrics Journal, 15, 358-393.
Kalliovirta L., Meitz M. and Saikkonen P. 2015. Gaussian Mixture Autoregressive model for univariate time series. Journal of Time Series Analysis, 36(2), 247-266.
Meitz M., Preve D., Saikkonen P. 2023. A mixture autoregressive model based on Student's t-distribution. Communications in Statistics - Theory and Methods, 52(2), 499-515.
Virolainen S. 2022. A mixture autoregressive model based on Gaussian and Student's t-distributions. Studies in Nonlinear Dynamics & Econometrics, 26(4) 559-580.
fitGSMAR, GSMAR, predict.gsmar,
add_data, cond_moments, mixing_weights