An index was used instead of a parameter value when modifying the
correction (for handling models with non optimizing agents and/or models with
exogenous variables).
DO not print files for parameters declaration, exogenous variables declarations
or parameter values if the selected equations do not have parameters and/or
exogenous variables (typically accounting identities).
- Can handle more than one equation.
- Can handle identities.
- Forbids dynamic equations.
- Can handle following LHS y, diff(y), diff(diff(y)), log(y), diff(log(y)) and
diff(diff(log(y))), other transformations will result in an error.
- Added integration tests.
Remark 1. In the integration tests I compare the values returned by the
evaluate routine with the values computed with the simulation
routines. Normally the discrepancies should be small, but this is not
the case when the endogenous variable appear under a log on the
LHS. My current conclusion is that this has more to do with the
cumulation of the accuracy errors in the simulation routine (a
sequence of Newton algorithms) rather than with the evaluate routine.
Remark 2. Currently the only allowed nonlinear transformation on the LHS
endogenous variable is the log. It is not difficult to generalise, at
some point I had all the matlab functions allowed by Dynare,
but this would complicate the code for not much gain.
Renamed the auxiliary variables for PAC/VAR expectations unrolled
expressions. Using shorter prefixes, PE_ and VE_ instead of pac_expectation and
var_expectations.
Added the possibility to associate more than one trend to an endogenous
variable. The number of error correction equations is longer required to be
equal to the number of trends.
Extracts equations from a mod file and produce .inc files (equations, lists of
parameters, endogenous variables and exogenous variables) that can be included
in a mod file that will be used to simulate the model.
If an innovation has a tag `(used='estimationonly')` it will be excluded from
the generated files (ie list of shocks and equations).
Also fixes growth neutrality correction in models with non optimizing
agents (correction was not taking into account the value of the share of non
optimizing agents).