In a PAC equation, the error correction term must be written as:
β×(yₜ₋₁-xₜ₋₁)
where x is the endogenous variable determined by the equation and y
the target for x. The ordering matters, if not respected the
preprocessor wll not identify the error correction term (hence the
target) and will throw an error. The target must be an endogenous
variable or the log of an endogenous variable. For a more general
target, ie an expression, one can create an auxiliary variable.
We impose the ordering, target comes first in the error correction
term, so that the stability condition is always β>0 (this constraint
can be enforced when estimating the PAC equation).
- 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.
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).
Fixes the iterative_ols estimation of PAC equation when the Rule of Thumbs (non
optimizing) part of the equations contains endogenous and/or exogenous variables.
Stéphane Adjemian (Charybdis)
Dóra Kocsis
Stéphane Adjemian (Charybdis)
Sébastien Villemot
Stéphane Adjemian (Charybdis)
Houtan Bastani
Stéphane Adjemian(Charybdis)