uGMAR (version 3.5.2)

uncond_moments: Calculate unconditional mean, variance, first p autocovariances and autocorrelations of the GSMAR process.

Description

uncond_moments calculates the unconditional mean, variance, and the first p autocovariances and autocorrelations of the GSMAR process.

Usage

uncond_moments(gsmar)

Value

Returns a list containing the unconditional mean, variance, and the first p autocovariances and autocorrelations. Note that the lag-zero autocovariance/correlation is not included in the "first p" but is given in the uncond_variance component separately.

Arguments

gsmar

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

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(), get_regime_vars()

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")
uncond_moments(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")
uncond_moments(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")
uncond_moments(gstmar12)