simulate_var: VAR simulation

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

VAR simulation

Description

This function generates a simulated multivariate VAR time series.

Usage

simulate_var(n, p, nobs, rho, sparsity, mu, method, covariance, ...)

Arguments

n	dimension of the time series (default n = 100).
p	number of lags of the VAR model (default p = 1).
nobs	number of observations to be generated (default nobs = 250).
rho	base value for the covariance matrix (default rho = 0.5).
sparsity	density (in percentage) of the number of nonzero elements of the VAR matrices (default sparsity = 0.05).
mu	a vector containing the mean of the simulated process (default mu = 0).
method	which method to use to generate the VAR matrix. Possible values are "normal" or "bimodal" ((default method = "normal")).
covariance	type of covariance matrix to use in the simulation. Possible values: "Toeplitz", "block1", "block2", "Wishart" or simply "diagonal" (default covariance = "Toeplitz").
...	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.