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.
Stéphane Adjemian (Ryûk)
Sébastien Villemot
Dóra Kocsis
Stéphane Adjemia (Scylla)
Houtan Bastani
Stéphane Adjemian(Charybdis)