Description Usage Arguments Value Warning References
View source: R/pickAndChangeParams.R
change_parametrization
changes the parametrization of the given parameter
vector to change_to
.
1 2 3 4 5 6 7 8 9 | change_parametrization(
p,
M,
params,
model = c("GMAR", "StMAR", "G-StMAR"),
restricted = FALSE,
constraints = NULL,
change_to = c("intercept", "mean")
)
|
p |
a positive integer specifying the autoregressive order of the model. |
M |
|
params |
a real valued parameter vector specifying the model.
Symbol φ denotes an AR coefficient, σ^2 a variance, α a mixing weight, and ν a degrees of
freedom parameter. If |
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 φ_{m,1},...,φ_{m,p} are restricted to be the same for all regimes. |
constraints |
specifies linear constraints imposed to each regime's autoregressive parameters separately.
The symbol φ denotes an AR coefficient. Note that regardless of any constraints, the autoregressive order
is always |
change_to |
either "intercept" or "mean" specifying to which parametrization it should be switched to.
If set to |
Returns parameter vector described in params
but with parametrization changed from intercept to mean
(when change_to==mean
) or from mean to intercept (when change_to==intercept
).
No argument checks!
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
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