Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library: multi

Macro: simvar
Description: simvar() computes a multidimensional autoregressive time series.

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.

Example:
a = #(.4,0)~#(.2,-.1)
randomize(101)
u = normal(2,5)
y = simvar( u, 10|20, a)  
Result:

Content of object y
[1,] 10.000000 8.994989 2.848346 1.894127 0.282635 1.207435 
[2,] 20.000000 -3.257751 1.687405 -1.600239 1.762842 -0.702735 

Library: multi

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Author: Benkwitz,
(C) MD*TECH Method and Data Technologies, 28.6.1999