simulateVAR: VAR simulation

In sparsevar: Sparse VAR/VECM Models Estimation

Description

This function generates a simulated multivariate VAR time series.

Usage

simulateVAR(N, p, nobs, rho, sparsity, mu, method, covariance, ...)

Arguments

N	dimension of the time series.
p	number of lags of the VAR model.
nobs	number of observations to be generated.
rho	base value for the covariance matrix.
sparsity	density (in percentage) of the number of nonzero elements of the VAR matrices.
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 April 18, 2021, 9:08 a.m.