View source: R/deprecatedFunctions.R
simulateGSMAR | R Documentation |
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
)
object |
object of class |
nsim |
a positive integer specifying how many values (ahead from |
init_values |
a numeric vector with length |
ntimes |
a positive integer specifying how many sets of simulations should be performed. |
drop |
if |
gsmar |
a class 'gsmar' object, typically generated by |
nsimu |
a positive integer specifying how many values (ahead from |
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.
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...
$sample
a size (nsim
x
ntimes
) matrix containing the simulated values.
$component
a size (nsim
x
ntimes
) matrix containing the information from which
mixture component each value was generated from.
$mixing_weights
a 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.
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
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