is_stationary {gmvarkit} | R Documentation |
Check the stationary condition of a given GMVAR, StMVAR, or G-StMVAR model
Description
is_stationary
checks the stationarity condition of a GMVAR, StMVAR, or G-StMVAR model.
Usage
is_stationary(
p,
M,
d,
params,
all_boldA = NULL,
structural_pars = NULL,
tolerance = 0.001
)
Arguments
p |
a positive integer specifying the autoregressive order of the model. |
M |
|
d |
the number of time series in the system. |
params |
a real valued vector specifying the parameter values.
Above, In the GMVAR model, The notation is similar to the cited literature. |
all_boldA |
3D array containing the |
structural_pars |
If
See Virolainen (forthcoming) for the conditions required to identify the shocks and for the B-matrix as well (it is |
tolerance |
Returns |
Details
If the model is constrained, remove the constraints first with the function reform_constrained_pars
.
Value
Returns TRUE
if the model is stationary and FALSE
if not. Based on the argument tolerance
,
is_stationary
may return FALSE
when the parameter vector is in the stationarity region, but
very close to the boundary (this is used to ensure numerical stability in estimation of the model parameters).
Warning
No argument checks!
References
Kalliovirta L., Meitz M. and Saikkonen P. 2016. Gaussian mixture vector autoregression. Journal of Econometrics, 192, 485-498.
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 Unpublished working paper, available as arXiv:2109.13648.
@keywords internal