simulate_varx: VARX simulation

In sparsevar: Sparse VAR (Vector Autoregression) / VECM (Vector Error Correction Model) Estimation

VARX simulation

Description

This function generates a simulated multivariate VAR time series.

Usage

simulate_varx(n, k, p, m, nobs, rho,
                    sparsity_a1, sparsity_a2, sparsity_a3,
                    mu, method, covariance, ...)

Arguments

n	dimension of the time series.
k	TODO
p	number of lags of the VAR model.
m	TODO
nobs	number of observations to be generated.
rho	base value for the covariance matrix.
sparsity_a1	density (in percentage) of the number of nonzero elements of the A1 block.
sparsity_a2	density (in percentage) of the number of nonzero elements of the A2 block.
sparsity_a3	density (in percentage) of the number of nonzero elements of the A3 block.
mu	a vector containing the mean of the simulated process.
method	which method to use to generate the VAR matrix. Possible values are "normal" or "bimodal".
covariance	type of covariance matrix to use in the simulation. Possible values: "toeplitz", "block1", "block2" or simply "diagonal".
...	the options for the simulation. These are: muMat: the mean of the entries of the VAR matrices; sdMat: the sd of the entries of the matrices;

Value

A a list of NxN matrices ordered by lag

data a list with two elements: series the multivariate time series and noises the time series of errors

S the variance/covariance matrix of the process

sparsevar documentation built on Feb. 5, 2026, 1:07 a.m.