A causal time series is a stochastic process 
that can be represented using only
present and past innovations. For an autoregressive
model this takes the form
 |
where 
and 
,
with no dependence on future innovations 
for 
. For an autoregressive model with zero-mean, uncorrelated
innovations of constant variance, the condition that every root
of the autoregressive polynomial lie
outside the unit circle gives the unique causal stationary
solution.
