is_stationary_int {uGMAR} | R Documentation |
Check the stationarity and identification conditions of specified GMAR, StMAR, or G-StMAR model.
Description
is_stationary_int
checks the stationarity condition and is_identifiable
checks the identification condition
of the specified GMAR, StMAR, or G-StMAR model.
Usage
is_stationary_int(p, M, params, restricted = FALSE)
is_identifiable(
p,
M,
params,
model = c("GMAR", "StMAR", "G-StMAR"),
restricted = FALSE,
constraints = NULL
)
Arguments
p |
a positive integer specifying the autoregressive order of the model. |
M |
|
params |
a real valued parameter vector specifying the model.
Symbol |
restricted |
a logical argument stating whether the AR coefficients |
model |
is "GMAR", "StMAR", or "G-StMAR" model considered? In the G-StMAR model, the first |
constraints |
specifies linear constraints imposed to each regime's autoregressive parameters separately.
The symbol |
Details
is_stationary_int
does not support models imposing linear constraints. In order to use it for a model imposing linear
constraints, one needs to expand the constraints first to obtain an unconstrained parameter vector.
Note that is_stationary_int
returns FALSE
for stationary parameter vectors if they are extremely close to the boundary
of the stationarity region.
is_identifiable
checks that the regimes are sorted according to the mixing weight parameters and that there are no duplicate
regimes.
Value
Returns TRUE
or FALSE
accordingly.
Warning
These functions don't have any argument checks!
References
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.