pick_weightpars {sstvars} | R Documentation |
Pick transition weight parameters
Description
pick_weightpars
picks the transition weight parameters from the given parameter vector.
Usage
pick_weightpars(
p,
M,
d,
params,
weight_function = c("relative_dens", "logistic", "mlogit", "exponential", "threshold",
"exogenous"),
weightfun_pars = NULL,
cond_dist = c("Gaussian", "Student", "ind_Student", "ind_skewed_t")
)
Arguments
p |
the autoregressive order of the model |
M |
the number of regimes |
d |
the number of time series in the system, i.e., the dimension |
params |
a real valued vector specifying the parameter values.
Should have the form
For models with...
Above, |
weight_function |
What type of transition weights
See the vignette for more details about the weight functions. |
weightfun_pars |
|
cond_dist |
specifies the conditional distribution of the model as |
Value
- If
weight_function = "relative_dens"
: Returns a length
M
vector containing the transition weight parameters\alpha_{m}, m=1,...,M
, including the non-parametrized\alpha_{M}
.weight_function="logistic"
:Returns a length two vector
(c,\gamma)
, wherec\in\mathbb{R}
is the location parameter and\gamma >0
is the scale parameter.- If
weight_function = "mlogit"
: Returns a length
(M-1)k
vector(\gamma_1,...,\gamma_M)
, where\gamma_m
(k\times 1)
,m=1,...,M-1
(\gamma_M=0
) contains the mlogit-regression coefficients of them
th regime. Specifically, for switching variables with indices inJ\subset\lbrace 1,...,d\rbrace
, and with\tilde{p}\in\lbrace 1,...,p\rbrace
lags included,\gamma_m
contains the coefficients for the vectorz_{t-1} = (1,\tilde{z}_{\min\lbrace I\rbrace},...,\tilde{z}_{\max\lbrace I\rbrace})
, where\tilde{z}_{i} =(y_{j,t-1},...,y_{j,t-\tilde{p}})
,i\in I
. Sok=1+|I|\tilde{p}
where|I|
denotes the number of elements inI
.weight_function="exponential"
:Returns a length two vector
(c,\gamma)
, wherec\in\mathbb{R}
is the location parameter and\gamma >0
is the scale parameter.weight_function="threshold"
:Returns a length
M-1
vector(r_1,...,r_{M-1})
, wherer_1,...,r_{M-1}
are the threshold values.weight_function="exogenous"
:Returns
numeric(0)
.
Warning
No argument checks!