get_regime_vars: Calculate regime specific variances gamma_{m,0} In uGMAR: Estimate Univariate Gaussian and Student's t Mixture Autoregressive Models

Description

`get_regime_vars` calculates the unconditional regime specific variances γ_{m,0} for the given GMAR, StMAR, or G-StMAR model.

Usage

 `1` ```get_regime_vars(gsmar) ```

Arguments

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

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, 247-266.

• Meitz M., Preve D., Saikkonen P. 2021. A mixture autoregressive model based on Student's t-distribution. Communications in Statistics - Theory and Methods, doi: 10.1080/03610926.2021.1916531

• Virolainen S. 2021. A mixture autoregressive model based on Gaussian and Student's t-distributions. Studies in Nonlinear Dynamics & Econometrics, doi: 10.1515/snde-2020-0060

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

Other moment functions: `cond_moments()`, `get_regime_autocovs()`, `get_regime_means()`, `uncond_moments()`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```# 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) ```