cb4384bb27
The routine takes a dseries object as unique argument and return an updated
object with the expectation term.
If the mod file is named `example.mod` and if the (VAR/PAC) expectation model is
named `toto`, then after
var_expectation.print('toto');
the expectation term can be evaluated:
ts = example.var_expectations.evaluate_varexp(ts);
where ts is a dseries object containing all the time series appearign in the
auxiliary (var or trend_component).
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