micvar (version 0.1.0)

sim_var: Simulate data from a vector autoregressive model with specified coefficient matrices

Description

Simulates data from a stable vector autoregressive model with Gaussian innovations and specified coefficient matrices. Stability of the process is verified using verify_stability.

Usage

sim_var(A, n, mu = NULL, Sigma = NULL, burn_in = 500)

Value

n x k data matrix.

Arguments

A

List of coefficient matrices. Each element in A must be a square matrix. Dimension of matrix determines the number of variables. Length of A determines the order of the process. In the case of univariate time series each entry of A should be a 1 x 1 matrix.

n

Integer. Number of data points to simulate.

mu

Vector (default 0s). Means of Gaussian innovations.

Sigma

Square matrix (default Identity). Variance of Gaussian innovations.

burn_in

Integer (default 500). Number of observations used to start up simulated process. In total n + burn_in observations are simulated but the first burn_in are discarded.

Examples

# multivariate
VAR3_2_A <- list(gen_coef_mat(3, 0.1, 0.3, 0.8), # lag 1
                 gen_coef_mat(3, 0.1, 0.4 , 0.5)) # lag 2
x <- sim_var(VAR3_2_A, n = 1000)

# univariate
AR2 <- list(matrix(0.5), matrix(0.2))
x <- sim_var(AR2, n = 1000)

# non-identity covariance of Gaussian innovations
Sigma <- matrix(c(1,0.5,0.9,0.5,1.5,0.7,0.9,0.7,1.25), nrow = 3)
x <- sim_var(VAR3_2_A, n = 1000, Sigma = Sigma)