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).
See tests/var-expectations/9/example.mod for a self documented example.
Also updated all the integration tests using the option `expression` instead of
`variable` (which is deprecated and will be removed at some point).
Not yet working, a bug in the preprocessor remains to be fixed. The
preprocessor does not create the correct number of reduced form parameters
for VAR_EXPECTATION when the auxiliary model is a trend component model,
because it ignores the fact that the model has to be rewritten in levels.
Dóra Kocsis
Stéphane Adjemia (Scylla)
Houtan Bastani
Stéphane Adjemian(Charybdis)