uncond_moments {uGMAR} | R Documentation |
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)
Arguments
gsmar |
a class 'gsmar' object, typically generated by |
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.
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)