uGMAR (version 3.2.3)

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

Description

get_regime_autocovs calculates the first p regime specific autocovariances $\gamma$${}_{m,p}$ for the given GMAR, StMAR, or G-StMAR model.

Usage

get_regime_autocovs(gsmar)

Arguments

gsmar

object of class 'gsmar' created with the function fitGSMAR or GSMAR.

Value

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.

References

• Kalliovirta L., Meitz M. and Saikkonen P. 2015. Gaussian Mixture Autoregressive model for univariate time series. Journal of Time Series Analysis, 36, 247-266.

• Meitz M., Preve D., Saikkonen P. 2018. A mixture autoregressive model based on Student's t-distribution. arXiv:1805.04010 [econ.EM].

• There are currently no published references for the G-StMAR model, but it's a straightforward generalization with theoretical properties similar to the GMAR and StMAR models.

• Lutkepohl H. 2005. New Introduction to Multiple Time Series Analysis. Springer.

See Also

Other moment functions: condMoments(), get_regime_means(), get_regime_vars(), uncondMoments()

Examples

# NOT RUN {
# 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)
# }