get_alpha_mt {sstvars} | R Documentation |
Get the transition weights alpha_mt
Description
get_alpha_mt
computes the transition weights.
Usage
get_alpha_mt(
data,
Y2,
p,
M,
d,
weight_function = c("relative_dens", "logistic", "mlogit", "exponential", "threshold",
"exogenous"),
weightfun_pars = NULL,
all_A,
all_boldA,
all_Omegas,
weightpars,
all_mu,
epsilon,
log_mvdvalues = NULL
)
Arguments
data |
a matrix or class |
Y2 |
the data arranged as obtained from |
p |
a positive integer specifying the autoregressive order |
M |
a positive integer specifying the number of regimes |
d |
the number of time series in the system, i.e., the dimension |
weight_function |
What type of transition weights
See the vignette for more details about the weight functions. |
weightfun_pars |
|
all_A |
4D array containing all coefficient matrices |
all_boldA |
3D array containing the |
all_Omegas |
A 3D array containing the covariance matrix parameters obtain from
|
weightpars |
numerical vector containing the transition weight parameters, obtained from |
all_mu |
an |
epsilon |
the smallest number such that its exponent is wont classified as numerically zero
(around |
log_mvdvalues |
a |
Details
Note that we index the time series as -p+1,...,0,1,...,T
.
Value
Returns the mixing weights a (T x M)
matrix, so that the t
th row is for the time period t
and m
:th column is for the regime m
.
References
Kheifets I.L., Saikkonen P.J. 2020. Stationarity and ergodicity of Vector STAR models. Econometric Reviews, 39:4, 407-414.
Lütkepohl H. 2005. New Introduction to Multiple Time Series Analysis, Springer.
Lanne M., Virolainen S. 2024. A Gaussian smooth transition vector autoregressive model: An application to the macroeconomic effects of severe weather shocks. Unpublished working paper, available as arXiv:2403.14216.
Virolainen S. 2024. Identification by non-Gaussianity in structural threshold and smooth transition vector autoregressive models. Unpublished working paper, available as arXiv:2404.19707.
@keywords internal