uncond_moments_int {uGMAR} | R Documentation |
Calculate unconditional mean, variance, and the first p autocovariances and autocorrelations of a GSMAR process.
Description
uncond_moments_int
calculates the unconditional mean, variance, and the first p
autocovariances and autocorrelations of the specified GSMAR process.
Usage
uncond_moments_int(
p,
M,
params,
model = c("GMAR", "StMAR", "G-StMAR"),
restricted = FALSE,
constraints = NULL,
parametrization = c("intercept", "mean")
)
Arguments
p |
a positive integer specifying the autoregressive order of the model. |
M |
|
params |
a real valued parameter vector specifying the model.
Symbol |
model |
is "GMAR", "StMAR", or "G-StMAR" model considered? In the G-StMAR model, the first |
restricted |
a logical argument stating whether the AR coefficients |
constraints |
specifies linear constraints imposed to each regime's autoregressive parameters separately.
The symbol |
parametrization |
is the model parametrized with the "intercepts" |
Details
Differs from the function uncond_moments
in arguments. This function exists for technical
reasons only.
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.