| get_regime_vars {uGMAR} | R Documentation |
Calculate regime specific variances \gamma_{m,0}
Description
get_regime_vars calculates the unconditional regime specific variances \gamma_{m,0}
for the given GMAR, StMAR, or G-StMAR model.
Usage
get_regime_vars(gsmar)
Arguments
gsmar |
a class 'gsmar' object, typically generated by |
Value
Returns a length M vector containing the unconditional variances of the components processes: m:th element for the m:th regime.
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.
Lütkepohl H. 2005. New Introduction to Multiple Time Series Analysis. Springer.
See Also
Other moment functions:
cond_moments(),
get_regime_autocovs(),
get_regime_means(),
uncond_moments()
Examples
# GMAR model
params13 <- c(1.4, 0.88, 0.26, 2.46, 0.82, 0.74, 5.0, 0.68, 5.2, 0.72, 0.2)
gmar13 <- GSMAR(p=1, M=3, params=params13, model="GMAR")
get_regime_vars(gmar13)
# StMAR model
params12t <- c(1.38, 0.88, 0.27, 3.8, 0.74, 3.15, 0.8, 100, 3.6)
stmar12t <- GSMAR(p=1, M=2, params=params12t, model="StMAR")
get_regime_vars(stmar12t)
# G-StMAR model (similar to the StMAR model above)
params12gs <- c(1.38, 0.88, 0.27, 3.8, 0.74, 3.15, 0.8, 3.6)
gstmar12 <- GSMAR(p=1, M=c(1, 1), params=params12gs, model="G-StMAR")
get_regime_vars(gstmar12)