 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. 

                         

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(C) MD*TECH Method and Data Technologies, 21.9.2000

