get_regime_autocovs function

Calculate regime specific autocovariances gamma $_{m,p}$

get_regime_autocovs calculates the first p regime specific autocovariances gamma $_{m,p}$

for the given GMAR, StMAR, or G-StMAR model.

get_regime_autocovs(gsmar)

Arguments

• gsmar: a class 'gsmar' object, typically generated by fitGSMAR or GSMAR.

Returns

Returns a size $(pxM)$ matrix containing the first p autocovariances of the components processes: i:th autocovariance in the i:th row and m:th component process in the m:th column.

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_autocovs(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_autocovs(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_autocovs(gstmar12)

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_means(), get_regime_vars(), uncond_moments()