sim_var {bvhar} | R Documentation |
Generate Multivariate Time Series Process Following VAR(p)
Description
This function generates multivariate time series dataset that follows VAR(p).
Usage
sim_var(
num_sim,
num_burn,
var_coef,
var_lag,
sig_error = diag(ncol(var_coef)),
init = matrix(0L, nrow = var_lag, ncol = ncol(var_coef)),
method = c("eigen", "chol"),
process = c("gaussian", "student"),
t_param = 5
)
Arguments
num_sim |
Number to generated process |
num_burn |
Number of burn-in |
var_coef |
VAR coefficient. The format should be the same as the output of |
var_lag |
Lag of VAR |
sig_error |
Variance matrix of the error term. By default, |
init |
Initial y1, ..., yp matrix to simulate VAR model. Try |
method |
Method to compute |
process |
Process to generate error term.
|
t_param |
Details
Generate
For i = 1, ... n,
Then the output is
Initial values might be set to be zero vector or .
Value
T x k matrix
References
Lütkepohl, H. (2007). New Introduction to Multiple Time Series Analysis. Springer Publishing.