diff --git a/doc/manual/source/running-dynare.rst b/doc/manual/source/running-dynare.rst
index bde52ede77ad97d5725abadd0abae9751c98282a..983e2aa07606f8d8d6cef6896ea57585da2bf469 100644
--- a/doc/manual/source/running-dynare.rst
+++ b/doc/manual/source/running-dynare.rst
@@ -554,7 +554,7 @@ by the ``dynare`` command.
executing the ``dynare`` command will leave variables containing
results in the workspace available for further processing. More
details are given under the relevant computing tasks. The
- ``M_``,``oo_``, and ``options_`` structures are saved in a file
+ ``M_``, ``oo_``, and ``options_`` structures are saved in a file
called ``FILENAME_results.mat`` located in the ``MODFILENAME/Output`` folder.
If they exist, ``estim_params_``,
``bayestopt_``, ``dataset_``, ``oo_recursive_`` and
diff --git a/doc/manual/source/the-model-file.rst b/doc/manual/source/the-model-file.rst
index 32d70b055af5a6b33b10fbebd5815bd73196e73c..9ba4e6a5af75c59ae9891c19d6644b921f9f53f8 100644
--- a/doc/manual/source/the-model-file.rst
+++ b/doc/manual/source/the-model-file.rst
@@ -14354,13 +14354,13 @@ Macro expressions
Macro-expressions can be used in two places:
* Inside macro directives, directly;
- * In the body of the ``.mod`` file, between an at-sign and curly
+ * In the body of the ``.mod`` file, between an @-sign and curly
braces (like ``@{expr}``): the macro processor will substitute
the expression with its value
It is possible to construct macro-expressions that can be assigned to
macro-variables or used within a macro-directive. The expressions are
-constructed using literals of the basic types (boolean, real, string, tuple,
+constructed using literals (*i.e.*\ fixed values) of the basic types (boolean, real, string, tuple,
array), comprehensions, macro-variables, macro-functions, and standard
operators.
@@ -14409,7 +14409,7 @@ The following operators can be used on strings:
.. rubric:: Tuple
-Tuples are enclosed by parenthesis and elements separated by commas (like
+Tuples are enclosed by parentheses and elements are separated by commas (like
``(a,b,c)`` or ``(1,2,3)``).
The following operators can be used on tuples:
@@ -14503,7 +14503,8 @@ every selected element of an array.
.. rubric:: Function
Functions can be defined in the macro processor using the ``@#define``
-directive (see below). A function is evaluated at the time it is invoked, not
+directive (see below). A function is evaluated at the time it is invoked during
+the macroprocessing stage, not
at define time. Functions can be included in expressions and the operators that
can be combined with them depend on their return type.
@@ -14663,10 +14664,10 @@ Macro directives
|br| Conditional inclusion of some part of the ``.mod`` file. The lines
between ``@#if``, ``@#ifdef``, or ``@#ifndef`` and the next ``@#elseif``,
``@#else`` or ``@#endif`` is executed only if the condition evaluates to
- ``true``. Following the ``@#if`` body, you can zero or more ``@#elseif``
- branches. An ``@#elseif`` condition is only evaluated if the preceding
- ``@#if`` or ``@#elseif`` condition evaluated to ``false``. The ``@#else``
- branch is optional and is only evaluated if all ``@#if`` and ``@#elseif``
+ ``true``. Following the ``@#if`` body, zero or more ``@#elseif``
+ branches are allowed. An ``@#elseif`` condition is only evaluated if the preceding
+ ``@#if`` or ``@#elseif`` condition(s) evaluated to ``false``. The ``@#else``
+ branch is optional and only evaluated if all ``@#if`` and ``@#elseif``
statements evaluate to false.
Note that when using ``@#ifdef``, the condition will evaluate to ``true``
@@ -14685,7 +14686,7 @@ Macro directives
imprecision of reals, extra care must be taken when testing them in the
MACRO_EXPRESSION. For example, ``exp(log(5)) == 5`` will evaluate to
``false``. Hence, when comparing real values, you should generally use a
- zero tolerance around the value desired, e.g. ``exp(log(5)) > 5-1e-14 &&
+ non-zero tolerance around the value desired, e.g. ``exp(log(5)) > 5-1e-14 &&
exp(log(5)) < 5+1e-14``
*Example*
@@ -14716,10 +14717,7 @@ Macro directives
Choose between two alternative monetary policy rules using a
macro-variable. The only difference between this example and the
- previous one is the use of ``@#ifdef`` instead of ``@#if``. Even though
- ``linear_mon_pol`` contains the value ``false`` because ``@#ifdef`` only
- checks that the variable has been defined, the linear monetary policy
- is output::
+ previous one is the use of ``@#ifdef`` instead of ``@#if``.
@#define linear_mon_pol = false // 0 would be treated the same
...
@@ -14732,7 +14730,9 @@ Macro directives
...
end;
- This would result in::
+ Although ``linear_mon_pol`` contains the value ``false`` because ``@#ifdef`` only
+ checks that the variable has been defined, the linear monetary policy
+ is output::This would result in::
...
model;
@@ -14749,8 +14749,8 @@ Macro directives
@#endfor
|br| Loop construction for replicating portions of the ``.mod``
- file. Note that this construct can enclose variable/parameters
- declaration, computational tasks, but not a model declaration.
+ file. Note that this construct can enclose variable/parameter
+ declarations, computational tasks, but not a model declaration.
*Example*
@@ -14871,11 +14871,11 @@ Example setup:
Includes ``modeldesc.mod``, declares priors on parameters, and runs
Bayesian estimation.
-Dynare can be called on ``simul.mod`` and ``estim.mod`` but it makes
+Dynare can be called on ``simul.mod`` and ``estim.mod``, but it makes
no sense to run it on ``modeldesc.mod``.
-The main advantage is that you don't have to copy/paste the whole model (at the
-beginning) or changes to the model (during development).
+The main advantage is that you don't have to copy/paste the whole model (during initial development)
+or changes to the model (during development).
Indexed sums of products
@@ -14917,7 +14917,7 @@ After macro processing, this is equivalent to::
Multi-country models
^^^^^^^^^^^^^^^^^^^^
-Here is a skeleton example for a multi-country model::
+Here is a bare bones example for a multi-country model::
@#define countries = [ "US", "EA", "AS", "JP", "RC" ]
@#define nth_co = "US"
@@ -14944,33 +14944,33 @@ Here is a skeleton example for a multi-country model::
Endogeneizing parameters
^^^^^^^^^^^^^^^^^^^^^^^^
-When calibrating the model, it may be useful to consider a parameter as an
-endogenous variable (and vice-versa).
+When calibrating the model, it may be useful to pin down parameters by targeting endogenous objects.
For example, suppose production is defined by a CES function:
.. math::
- y = \left(\alpha^{1/\xi} \ell^{1-1/\xi}+(1-\alpha)^{1/\xi}k^{1-1/\xi}\right)^{\xi/(\xi-1)}
+ y_t = \left(\alpha^{1/\xi} \ell_t^{1-1/\xi}+(1-\alpha)^{1/\xi}k_t^{1-1/\xi}\right)^{\xi/(\xi-1)}
and the labor share in GDP is defined as:
.. math::
- \textrm{lab\_rat} = (w \ell)/(p y)
+ \textrm{lab\_rat}_t = (w_t \ell_t)/(p_t y_t)
-In the model, :math:`\alpha` is a (share) parameter and ``lab_rat`` is an
+In the model, :math:`\alpha` is a (share) parameter and :math:`lab\_rat_t` is an
endogenous variable.
-It is clear that calibrating :math:`\alpha` is not straightforward;
-on the contrary, we have real world data for ``lab_rat`` and it
-is clear that these two variables are economically linked.
+It is clear that setting a value for :math:`\alpha` is not straightforward. But
+we have real world data for :math:`lab\_rat_t` and it
+is clear that these two objects are economically linked.
The solution is to use a method called *variable flipping*, which
consists in changing the way of computing the steady state. During
this computation, :math:`\alpha` will be made an endogenous variable
-and ``lab_rat`` will be made a parameter. An economically relevant
-value will be calibrated for ``lab_rat``, and the solution algorithm
+and the steady state value :math:`lab\_rat` of the dynamic variable :math:`lab\_rat_t`
+will be made a parameter. An economically sensible
+value will be calibrated for :math:`lab\_rat`, and the solution algorithm
will deduce the implied value for :math:`\alpha`.
An implementation could consist of the following files:
@@ -14989,7 +14989,7 @@ An implementation could consist of the following files:
var lab_rat;
@#endif
-``steady.mod``
+``steadystate.mod``
This file computes the steady state. It begins with::
@@ -14999,17 +14999,17 @@ An implementation could consist of the following files:
Then it initializes parameters (including ``lab_rat``, excluding
:math:`\alpha`), computes the steady state (using guess values for
endogenous, including :math:`\alpha`), then saves values of
- parameters and endogenous at steady state in a file, using the
+ parameters and variables at steady state in a file, using the
``save_params_and_steady_state`` command.
-``simul.mod``
+``simulate.mod``
This file computes the simulation. It begins with::
@#define steady = 0
@#include "modeqs.mod"
- Then it loads values of parameters and endogenous at steady state
+ Then it loads values of parameters and variables at steady state
from file, using the ``load_params_and_steady_state`` command, and
computes the simulations.
@@ -15027,12 +15027,17 @@ to run simulations for three values: :math:`\rho = 0.8, 0.9,
rhos = [ 0.8, 0.9, 1];
for i = 1:length(rhos)
- rho = rhos(i);
+ set_param_value('rho',rhos(i));
stoch_simul(order=1);
+ if info(1)~=0
+ error('Simulation failed for parameter draw')
+ end
end
Here the loop is not unrolled, MATLAB/Octave manages the
- iterations. This is interesting when there are a lot of iterations.
+ iterations. This is interesting when there are a lot of iterations.
+ It is strongly advised to always check whether the error flag ``info(1)==0``
+ to prevent erroneously relying on stale results from previous iterations.
*With a macro processor loop (case 1)*
@@ -15040,8 +15045,11 @@ to run simulations for three values: :math:`\rho = 0.8, 0.9,
rhos = [ 0.8, 0.9, 1];
@#for i in 1:3
- rho = rhos(@{i});
+ set_param_value('rho',rhos(@{i}));
stoch_simul(order=1);
+ if info(1)~=0
+ error('Simulation failed for parameter draw')
+ end
@#endfor
This is very similar to the previous example, except that the loop
@@ -15055,6 +15063,9 @@ to run simulations for three values: :math:`\rho = 0.8, 0.9,
@#for rho_val in [ 0.8, 0.9, 1]
rho = @{rho_val};
stoch_simul(order=1);
+ if info(1)~=0
+ error('Simulation failed for parameter draw')
+ end
@#endfor
The advantage of this method is that it uses a shorter syntax, since the list
@@ -15095,10 +15106,10 @@ Misc commands
=============
.. command:: set_dynare_seed (INTEGER)
- set_dynare_seed (`default')
- set_dynare_seed (`clock')
- set_dynare_seed (`reset')
- set_dynare_seed (`ALGORITHM', INTEGER)
+ set_dynare_seed ('default')
+ set_dynare_seed ('clock')
+ set_dynare_seed ('reset')
+ set_dynare_seed ('ALGORITHM', INTEGER)
|br| Sets the seed used for random number generation. It is
possible to set a given integer value, to use a default value, or
diff --git a/matlab/compute_Pinf_Pstar.m b/matlab/compute_Pinf_Pstar.m
index 150acac1877f43df6aae9ea0e2b1f8fafcdf3796..c727d73cdd5a431f0cef0a9dd3ece95171f1ed91 100644
--- a/matlab/compute_Pinf_Pstar.m
+++ b/matlab/compute_Pinf_Pstar.m
@@ -1,5 +1,5 @@
function [Pstar,Pinf] = compute_Pinf_Pstar(mf,T,R,Q,qz_criterium, restrict_columns)
-% function [Z,ST,QT,R1,Pstar,Pinf] = compute_Pinf_Pstar(mf,T,R,Q,qz_criterium, restrict_columns)
+% [Pstar,Pinf] = compute_Pinf_Pstar(mf,T,R,Q,qz_criterium, restrict_columns)
% Kitagawa transformation of state space system with a quasi-triangular
% transition matrix with unit roots at the top, but excluding zero columns of the transition matrix.
% Computation of Pstar and Pinf for Durbin and Koopman Diffuse filter
@@ -30,7 +30,7 @@ function [Pstar,Pinf] = compute_Pinf_Pstar(mf,T,R,Q,qz_criterium, restrict_colum
% SPECIAL REQUIREMENTS
% None
-% Copyright © 2006-2022 Dynare Team
+% Copyright © 2006-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -66,7 +66,6 @@ else
indx0=(find(max(abs(T))<1.e-10));
end
np0=length(indx0);
-Tbkp = T;
T0=T(indx0,indx); % static variables vs. dynamic ones
R0=R(indx0,:); % matrix of shocks for static variables
@@ -124,7 +123,6 @@ if i == nk+1
end
if np0
- ST1=ST;
% Now I recover stationarized static variables using
% ss = s-A*z
% and