get_Sigmas {sstvars} | R Documentation |
Calculate the dp-dimensional covariance matrices \Sigma_{m,p}
in the transition weights
with weight_function="relative_dens"
Description
get_Sigmas
calculatesthe dp-dimensional covariance matrices \Sigma_{m,p}
in
the transition weights with weight_function="relative_dens"
so that the algorithm proposed
by McElroy (2017) employed whenever it reduces the computation time.
Usage
get_Sigmas(p, M, d, all_A, all_boldA, all_Omegas)
Arguments
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 |
all_A |
4D array containing all coefficient matrices |
all_boldA |
3D array containing the |
all_Omegas |
a |
Details
Calculates the dp-dimensional covariance matrix using the formula (2.1.39) in Lütkepohl (2005) when
d*p < 12
and using the algorithm proposed by McElroy (2017) otherwise.
The code in the implementation of the McElroy's (2017) algorithm (in the function VAR_pcovmat
) is
adapted from the one provided in the supplementary material of McElroy (2017). Reproduced under GNU General
Public License, Copyright (2015) Tucker McElroy.
Value
Returns a [dp, dp, M]
array containing the dp-dimensional covariance matrices for each regime.
References
Lütkepohl H. 2005. New Introduction to Multiple Time Series Analysis, Springer.
McElroy T. 2017. Computation of vector ARMA autocovariances. Statistics and Probability Letters, 124, 92-96.