– a generic one: CommonEnums.hh
– and a bytecode-specific one: Bytecode.hh
By the way, rename global constant “near_zero” into “power_deriv_near_zero”,
for clarity.
In particular, use this feature in many loops which feature a special treatment
for the first iteration, using a boolean variable (replacing iterator
manipulation). By the way, also use std::exchange() to simultaneously test the
value of this variable and update it.
– also use vectorized versions for .+ and .-
– add a space before vectorized operators to avoid syntactical ambiguity
This commits complements bfdcc546ec.
When an endogenous is declared with “var(log)”, say “y”:
– creates an auxiliary named “LOG_y”
– replaces “y(±l)” everywhere by “exp(LOG_y(±l))”
– adds a new auxiliary equation “y=exp(LOG_y)”
– adds a new definition “LOG_y=log(y)” in set_auxiliary_variables.m and
dynamic_set_auxiliary_series.m files
This option also works in conjunction with “deflator=…”, such as “var(log,
deflator=…)” (the “log” must appear befor “deflator”). There are no provisions
for combining “log” with “log_deflator”, because that would not make much sense
from an economic point of view (amounts to taking the log two times).
Ref. dynare#349
This is a more natural semantics.
Incidentally, this fixes a bug in the variable mapping (M_.mapping) where some
endogenous, appearing in a log() in a VAR or TCM, would not be mentioned (e.g.
in the var-expectations/7/example1.mod test, and many others).
By the way:
– Fix and improve the explanation of the purpose of the orig_symb_id and
orig_lead_lag fields for auxvars
– Factorize the code that prints those fields in MATLAB and JSON output
The logic was flawed in several ways. In particular, the test files
pac/trend-component-{3,10,11}/example1.mod would return A0 and A0star matrices
where the (2,2) element was incorrectly zero.
Everything was already in place (since ages!), except that the parser interface
was missing.
Also fix the derivation formula for atanh, which was incorrect.
The implementation of PacExpectationNode::toStatic() did not make sense. It
should normally never be called, hence we error out at this point.
Also do a cosmetic change in the PacExpectationNode::clone() methode.
In particular, this implies that steady state values of endogenous in the
“occbin_constraints” block must now be specified using the STEADY_STATE()
operator (and not with a “_ss” suffix).
Moreover:
– make various simplifications to the fields generated under M_
– in the driver file, replace the call to occbin.initialize() by a few explicit operations
Ref. #68
As the name implies, this option allows contemporaneous variables on the RHS.
The A0 matrix for contemporaneous variables is added as a second (optional)
output to the generated var_ar.m file. Note that for reduced-form VAR, this
matrix will be the identity.
Also, the user is now allowed to write the VAR models in a more flexible form:
the LHS must still be a single variable, but the RHS can be an arbitrary
expression (as long as it is linear, obviously). Internally, the preprocessor
now uses derivation to compute the coefficients of the AR and A0. This change
applies to both reduced-form and structural VAR models.
Ref. dynare#1785
Introduce a new method for decomposing a product of factors, so that we can
identify expressions of the form (1-optim_share)*A*B.
Also enforce that the optim_share parameter be in a factor of the form
1-optim_share (previously it would accept any expression containing the
parameter).
Note that this fix does not yet allow to actually write non-optimizing parts of
the form (1-optim_share)*A*B, since at a later point the preprocessor imposes
that this part be a linear combination of variables (but in the future we could
think of expanding the A*B product into a linear combination if, for example, A
is a paramater or a constant and B is a linear combination).
Closes: #50
– Fix order of items in this structure. Previously, items were ordered
according to the declaration order of parameters. Now, items are order
according to lag order (first lag appears first)
– Gracefully handle the case where there is no autoregressive part
(Closes: #52)
Also be more strict on the form of the target (must now be X(-1) or log(X(-1))
where X is *not* an auxiliary variable).
By the way, improve some comments in SymbolTable.
The detection of the target EC variable to be used when constructing the
forward-looking expectation variable is rather fragile.
When the PAC model is written with an (non-)optimizing share of agents,
restrict the identification of the target variable to the optimizing
expression, to minimize the risk of wrong identification.
By the way, add a few comments, and a small simplification.
By default, the preprocessor is supposed to only do the “unary ops”
transformation in the equations of VAR/PAC/trend component models.
However, the implementation was slightly different so far. It would detect
candidates to this transformation in the chosen equations, but it would then
perform the substitution in *all* equations.
This could lead for crashes, for example if the chosen equation contains
log(X(-1)), but another (non-chosen) equation has log(X(-2)). Then this latter
expression, even though it belongs to the same lag-equivalence class, is not
properly handled, causing a segfault.
Also do a few related cosmetic changes.
Rather use a single vector as in non-block mode.
By the way, change the order of output arguments in static functions, to be
closer to the dynamic ones.
Temporary terms need to be computed per equation (as was done previously), and
not simply per block.
It’s necessary to track temporary terms per equation, because some equations
are evaluated instead of solved, and an equation E1 may depend on the value of
an endogenous Y computed by a previously evaluated equation E2; in this case,
if some temporary term TT of equation E2 contains Y, then TT needs to be
computed after E1, but before E2.
In particular, in dynamic models, temporary terms are now computed for
derivatives w.r.t. exogenous, and also w.r.t. endogenous variables that do not
belong to the block.
This was only adding unneeded complexity, for no clear reason (we’re very far
from reaching 2³¹ equations, and if we wanted to support models that large, it
would be better to use long integers to avoid being limited to 2³²).
If a float smaller than one in absolute value is written without a
leading zero in the mod file, for instance as `.5`, we really need to
prefix the number with a zero. The simplest approach is to convert
the strings representing the numerical constants into floats.
The “diff” operator was incorrectly replaced by its argument in the static
model, leading to an incorrect result for the steady state.
This is because the information contained in the “expr_node” field of the
auxiliary variables storage was not consistent across all types of auxiliary
variables: for a “diff()” operator, it would contain the argument of the
operator, instead of the full node. Hence it would not simplify to zero at the
steady state.
A similar inconsistency was also present for the “expectation()” operator,
though it was not leading to an incorrect static model.
In the absence of braces, the last “else” clause is always associated with the
closest “if”, which is not what was intended here. The indentation was
misleading.
Sébastien Villemot
MichelJuillard
Houtan Bastani
