VAR_pcovmat: Calculate the dp-dimensional covariance matrix of p...

In sstvars: Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models

Calculate the dp-dimensional covariance matrix of p consecutive observations of a VAR process

Description

VAR_pcovmat calculate the dp-dimensional covariance matrix of p consecutive observations of a VAR process with the algorithm proposed by McElroy (2017).

Usage

VAR_pcovmat(p, d, all_Am, Omega_m)

Arguments

p	a positive integer specifying the autoregressive order
d	the number of time series in the system, i.e., the dimension
all_Am	[d, d, p] array containing the AR coefficient matrices
Omega_m	the (d\times d) positive definite error term covariance matrix

Details

Most of the code in this function 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 the (dp \times dp) covariance matrix.

References

• McElroy T. 2017. Computation of vector ARMA autocovariances. Statistics and Probability Letters, 124, 92-96.

sstvars documentation built on April 11, 2025, 5:47 p.m.