Allows to pass a dseries object saved on disk in a .mat file or .m to
initialize the paths for the endogenous variables and set the paths
for the exogenous variables. It is not required to pass the auxiliary
variables (automatically computed by initvalf routine), which is useful
if the baseline comes from another model. In this case, the
initval_file command or the datafile option of the
perfect_foresight_setup command sets the value of periods (deduced
from the number of observation in the dseries object and the number of
lags/leads in the model).
If there were more than 10 files of Metropolis parameter draws, the ordering
the files containing the posterior moments could be different from that of the
parameter draws. This is because the “dir()” command was used to order the
files containing the parameter draws, and because the command uses alphabetic
ordering, file #10 would come before #2.
This commit enforces the numerical ordering of files.
Removed threshold for detecting the non zero elements in the rows of
the Jacobian matrix. Using tolf as a threshold parameter for identifying
the non zero elements leaded (not systematically) the algorithm to not
reevaluate the residuals of the dynamic model while necessary.
- Even in models where there is only one endogenous variable in the
LHS and where all the LHS are unique, it may be that because of the
preprocessor transformations an auxiliary variable appears in more
than one LHS. If diff(X) is on the LHS of an equation in the original
model, the preprocessor will create an auxiliary variable AUX_DIFF
which will appear in the the original equation, replacing diff(X),
and in a new equation defining the auxiliary variable. In this case
the, the Dulmage-Mendelsohn decomposition will associate AUX_DIFF
with the original equation and X with the equation. This was
problematic in the previous version of the algorithm, since it was
assumed that each equation determines the LHS variable (here AUX_DIFF
= X - X(-1) determines a RHS variable (X).
- Changed the expression for evaluating an LHS variable under a log.
- Improved efficiency by not evaluating the residuals of the model if
not required for solving the current univariate block.
Stéphane Adjemian (Charybdis)
Sébastien Villemot
Johannes Pfeifer
Willi Mutschler
Michel Juillard
Houtan Bastani