Usage: |
y = simvar(u,y0,a)
|
Input: |
| u | (K x T)-matrix of 'noise'. Each column represents
the K-dimensional noise, or innovation of a point in time.
|
| y0 | (K x p)-matrix of starting ('pre-sample')-values of
time series.
|
| a | (K x K*p) or (K x K*p+1)-matrix of model parameters.
The model can be specified with [(K x K*p+1)] or without
[(K x K*p)] intercept. If an intercept is specified simvar()
regards the first column of 'a' as the intercept.
|
Output: |
| y | (K x p+T)-matrix of autoregressive time series. The first
p columns of 'y' are 'y0', the remaining are the computed
time series. |