It is now supported by the MATLAB editor (as of R2022a).
The old ASCII notation is left in some files that we copy as-is from other
sources (e.g. in the contrib/ and m4/ subdirectories).
The particles submodule is not updated at this point, because it is in an
inconsistent state.
[skip ci]
The taget in PAC equation can be decomposed into an arbitrary number of components (variables
in the VAR auxiliary model).
TODO Iterative OLS estimation (which is not the preferred estimation routine).
TODO Decomposition in the routine evaluating the forecasts for each component.
This commit only introduce new elements in the Dynare language (adding the
possibility to decompose the target into stationary and non stationary
components) and insure that all the former codes (ie without decomposition of
the target) are still working as expected.
This patch provide a mathematically equivalent approach to update the growth
neutrality correction.
(cherry picked from commit 980a890487cd983eba027bdec63c8a777fd793f7)
Non zero mean exogenous variables in non optimizing part where not accounted for
due to a wrong call to `isfield` function. It is not possible to test
simultaneously the existence of a field and a subfield.
(cherry picked from commit 5a7c0fd2dda6c0ccc554994524bbefb95c29e722)
- Force long run levels of the exogenous variables to be zero or g (the BGP growth rate of the LHS endogenous variable).
- Fix the correction of the correction for the share λ (aak γ).
- Provisions for the case where the long run level of the exogenous variable is
different from 0 or g (see tmp1 and ll which should be added to the growth
neutrality correction as a constant).
(cherry picked from commit a4423d734e1df1d4ee09c7225d7fd610e0d94cd1)
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).
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).
PAC equation has to be written as
diff(x) = a0*(xstar(-1)-x(-1)) + a1*diff(x(-1)) + ... + ap*diff(x(-p)) + PAC_EXPECTATION(pacmodelname) + ...;
In the error correction term, a0*(xstar(-1)-x(-1)), we must have the difference
between the target (the trend xstar(-1)) and the level of the endogenous
variable (x(-1)). To ensure stability around the trend, the parameter a0 needs
to be positive.
REMARKS
[1] In the TREND_COMPONENT_MODEL the error correction terms are written in
reverse order, ie as the difference betwwen the level of the endogenous
variable and the trend variable.
[2] In the estimation routine we do not constrain a0 to be positive, but is
would surely help to satisfy this condition in the initial condition.
Johannes Pfeifer
Stéphane Adjemian (Argos)
Sébastien Villemot
DoraK
Stéphane Adjemian (Charybdis)
Stéphane Adjemian (Charybdis)
Houtan Bastani
Stéphane Adjemian(Charybdis)