Fix header doc.

NEWS.md

@@ -1,405 +1,3 @@
-Announcement for Dynare 6.0 (on 2024-02-02)
-===========================================
-
-We are pleased to announce the release of Dynare 6.0.
-
-This major release adds new features and fixes various bugs.
-
-The Windows, macOS, MATLAB Online and source packages are already available for
-download at [the Dynare website](https://www.dynare.org/download/).
-
-This release is compatible with MATLAB versions ranging from 9.5 (R2018b) to
-23.2 (R2023b), and with GNU Octave versions ranging from 7.1.0 to 8.4.0 (NB:
-the Windows package requires version 8.4.0 specifically).
-
-Major user-visible changes
---------------------------
-
- - The Sequential Monte Carlo sampler as described by Herbst and Schorfheide
-   (2014) is now available under value `hssmc` for option
-   `posterior_sampling_method`.
-
- - New routines for perfect foresight simulation with expectation errors. In
-   such a scenario, agents make expectation errors in that the path they had
-   anticipated in period 1 is not realized exactly. More precisely, in some
-   simulation periods, they may receive new information that makes them revise
-   their anticipation for the path of future shocks. Also, under this scenario,
-   it is assumed that agents behave as under perfect foresight, *i.e.* they
-   make their decisions as if there were no uncertainty and they knew exactly
-   the path of future shocks; the new information that they may receive comes
-   as a total surprise to them. Available under new
-   `perfect_foresight_with_expectation_errors_setup` and
-   `perfect_foresight_with_expectation_errors_solver` commands, and
-   `shocks(learnt_in=…)`, `mshocks(learnt_in=…)` and `endval(learnt_in=…)`
-   blocks.
-
- - New routines for IRF matching with stochastic simulations:
-
-   - Both frequentist (as in Christiano, Eichenbaum, and Evans, 2005) and
-     Bayesian (as in Christiano, Trabandt, and Walentin, 2010) IRF matching
-     approaches are implemented. The core idea of IRF matching is to treat
-     empirical impulse responses (*e.g.* given from an SVAR or local projection
-     estimation) as data and select model parameters that align the model’s
-     IRFs closely with their empirical counterparts.
-
-   - Available under option `mom_method = irf_matching` option to the
-     `method_of_moments` command.
-
-   - New blocks `matched_irfs` and `matched_irfs_weights` for specifying the
-     values and weights of the empirical impulse response functions.
-
- - Pruning à la Andreasen et al. (2018) is now available at an arbitrary
-   approximation order when performing stochastic simulations with
-   `stoch_simul`, and at 3rd order when performing particle filtering.
-
- - New `log` option to the `var` statement. In addition to the endogenous
-   variable(s) thus declared, this option also triggers the creation of
-   auxiliary variable(s) equal to the log of the corresponding endogenous
-   variable(s). For example, given a `var(log) y;` statement, two endogenous
-   will be created (`y` and `LOG_y`), and an auxiliary equation linking the two
-   will also be added (equal to `y = exp(LOG_y);`). Moreover, every occurrence
-   of `y` in the model will be replaced by `exp(LOG_y)`. This option is, for
-   example, useful for performing a loglinear approximation of some variable(s)
-   in the context of a first-order stochastic approximation; or for ensuring
-   that the variable(s) stay(s) in the definition domain of the function
-   defining the steady state or the dynamic residuals when the nonlinear solver
-   is used.
-
- - New model editing features
-
-   - Multiple `model` blocks are now supported (this was already working but
-     not explicitly documented).
-
-   - Multiple `estimated_params` blocks now concatenate their contents (instead
-     of overwriting previous ones, which was the former undocumented behavior);
-     an `overwrite` option has been added to provide the old behavior.
-
-   - New `model_options` statement to set model options in a global fashion.
-
-   - New `model_remove` command to remove equations.
-
-   - New `model_replace` block to replace equations.
-
-   - New `var_remove` command to remove variables (or parameters).
-
-   - New `estimated_params_remove` block to remove estimated parameters.
-
- - Stochastic simulations
-
-   - Performance improvements for simulation of the solution under perturbation
-     and for particle filtering at higher order (⩾ 3).
-
-   - Performance improvement for the first order perturbation solution using
-     either cycle reduction (`dr=cycle_reduction` option) or logarithmic
-     reduction (`dr=logarithmic_reduction`).
-
-   - New `nomodelsummary` option to the `stoch_simul` command, to suppress the
-     printing of the model summary and the covariance of the exogenous shocks.
-
- - Estimation
-
-   - A truncated normal distribution can now be specified as a prior, using the
-     3rd and 4th parameters of the `estimated_params` block as the bounds.
-
-   - New `conditional_likelihood` option to the `estimation` command. When the
-     option is set, instead of using the Kalman filter to evaluate the
-     likelihood, Dynare will evaluate the conditional likelihood based on the
-     first-order reduced form of the model by assuming that the initial state
-     vector is at its steady state.
-
-   - New `additional_optimizer_steps` option to the `estimation` command to
-     trigger the sequential execution of several optimizers when looking for
-     the posterior mode.
-
-   - The `generate_trace_plots` command now allows comparing multiple chains.
-
-   - The Geweke and Raftery-Lewis convergence diagnostics will now also be
-     displayed when `mh_nblocks>1`.
-
-   - New `robust`, `TolGstep`, and `TolGstepRel` options to the optimizer
-     available under `mode_compute=5` (“newrat”).
-
-   - New `brooks_gelman_plotrows` option to the `estimation` command for
-     controlling the number of parameters to depict along the rows of the
-     figures depicting the Brooks and Gelman (1998) convergence diagnostics.
-
-   - New `mh_init_scale_factor` option to the `estimation` command tor govern
-     the overdispersion of the starting draws when initializing several Monte
-     Carlo Markov Chains. This option supersedes the `mh_init_scale` option,
-     which is now deprecated.
-
- - Steady state computation
-
-   - Steady state computation now accounts for occasionally-binding constraints
-     of mixed-complementarity problems (as defined by `mcp` tags).
-
-   - New `tolx` option to the `steady` command for governing the termination
-     based on the step tolerance.
-
-   - New `fsolve_options` option to the `steady` command for passing options to
-     `fsolve` (in conjunction with the `solve_algo=0` option).
-
-   - New option `from_initval_to_endval` option to the `homotopy_setup` block,
-     for easily computing homotopy from initial to terminal steady state (when
-     the former is already computed).
-
-   - New `non_zero` option to `resid` command to restrict display to non-zero
-     residuals.
-
- - Perfect foresight
-
-   - Significant performance improvement of the `stack_solve_algo=1` option to
-     the `perfect_foresight_solver` command (Laffargue-Boucekkine-Juillard
-     algorithm) when used in conjunction with options `block` and/or `bytecode`
-     of the `model` block.
-
-   - New `relative_to_initval` option to the `mshocks` block, to use the
-     initial steady state as a basis for the multiplication when there is an
-     `endval` block.
-
-   - New `static_mfs` option to the `model` block (and to the `model_options`
-     command), for controlling the minimum feedback set computation for the
-     static model. It defaults to `0` (corresponding to the behavior in Dynare
-     version 5).
-
-   - Various improvements to homotopy
-
-     - New `endval_steady` option to the `perfect_foresight_setup` command for
-       computing the terminal steady state at the same time as the transitory
-       dynamics (and new options `steady_solve_algo`, `steady_tolf`,
-       `steady_tolx`, `steady_maxit` and `steady_markowitz` for controlling the
-       steady state nonlinear solver).
-
-     - New `homotopy_linearization_fallback` and
-       `homotopy_marginal_linearization_fallback` options to the
-       `perfect_foresight_solver` command to get an approximate solution when
-       homotopy fails to go to 100%.
-
-     - New `homotopy_initial_step_size`, `homotopy_min_step_size`,
-       `homotopy_step_size_increase_success_count` and
-       `homotopy_max_completion_share` options to the
-       `perfect_foresight_solver` command to fine tune the homotopy behavior.
-
-     - Purely backward, forward and static models are now supported by the
-       homotopy procedure.
-
-   - The `stack_solve_algo=1` and `stack_solve_algo=6` options of the
-     `perfect_foresight_solver` command were merged and are now synonymous.
-     They both provide the Laffargue-Boucekkine-Juillard algorithm and work
-     with and without the `block` and `bytecode` options of the `model` block.
-     Using `stack_solve_algo=1` is now recommended, but `stack_solve_algo=6` is
-     kept for backward compatibility.
-
- - OccBin
-
-   - New `simul_reset_check_ahead_periods` option to the `occbin_setup` and
-     `occbin_solver` commands, for resetting `check_ahead_periods` in each
-     simulation period.
-
-   - new `simul_max_check_ahead_periods`, `likelihood_max_check_ahead_periods`,
-     and `smoother_max_check_ahead_periods` options to the `occbin_setup`
-     command, for truncating the number of periods for which agents check ahead
-     which regime is present.
-
- - Optimal policy
-
-   - The `osr` command now accepts the `analytic_derivation` and
-     `analytic_derivation_mode` options.
-
-   - The `evaluate_planner_objective` command now computes the unconditional
-     welfare for higher-order approximations (⩾ 3).
-
-   - New `periods` and `drop` options to the `evaluate_planner_objective`
-     command.
-
- - Semi-structural models
-
-   - New `pac_target_info` block for decomposing the PAC target into an
-     arbitrary number of components. Furthermore, in the presence of such a
-     block, the new `pac_target_nonstationary` operator can be used to select
-     the non stationary part of the target (typically useful in the error
-     correction term of the PAC equation).
-
-   - New `kind` option to the `pac_model` command. This option allows the user
-     to select the formula used to compute the weights on the VAR companion
-     matrix variables that are used to form PAC expectations.
-
-   - Performance improvement to `solve_algo=12` and `solve_algo=14`, which
-     significantly accelerates the simulation of purely backward, forward and
-     static models with the `perfect_foresight_solver` command and the routines
-     for semi-structural models.
-
- - dseries classes
-
-   - The `remove` and `remove_` methods now accept a list of variables (they
-     would previously only accept a single variable).
-
-   - New MATLAB/Octave command `dplot` to plot mathematical expressions
-     generated from variables fetched from (different) dseries objects.
-
- - Misc
-
-   - New `display_parameter_values` command to print the parameter values in
-     the command window.
-
-   - New `collapse_figures_in_tabgroup` command to dock all figures.
-
-   - Performance improvement for the `use_dll` option of the `model` block. The
-     preprocessor now takes advantage of parallelization when compiling the MEX
-     files.
-
-   - New mathematical primitives available: complementary error function
-     (`erfc`), hyperbolic functions (`cosh`, `sinh`, `tanh`, `acosh`, `asinh`,
-     `atanh`).
-
-   - New `last_simulation_period` option to the `initval_file` command.
-
-   - The `calib_smoother` command now accepts the `nobs` and
-     `heteroskedastic_filter` options.
-
-   - Under the MATLAB Desktop, autocompletion is now available for the `dynare`
-     command and other CLI commands (thanks to Eduard Benet Cerda from
-     MathWorks).
-
-   - Model debugging: The preprocessor now creates files for evaluating the
-     left- and right-hand sides of model equations separately. For a model file
-     called `ramst.mod`, you can call
-     `[lhs,rhs]=ramst.debug.static_resid(y,x,params);` (for the static model)
-     and `[lhs,rhs]=ramst.debug.dynamic_resid(y,x,params,steady_state);` (for
-     the dynamic model), where `y` are the endogenous, `x` the exogenous,
-     `params` the parameters, and `steady_state` is self-explanatory. NB: In
-     the dynamic case, the vector `y` of endogenous must have 3n elements
-     where n is the number of endogenous (including auxiliary ones); the
-     first n elements correspond to the lagged values, the middle n
-     elements to the contemporaneous values, and the last n elements to the
-     lead values.
-
-   - New interactive MATLAB/Octave command `search` for listing the equations
-     in which given variable(s) appear (requires `json` command line option).
-
-   - The `model_info` command allows to print the block decomposition even if
-     the `block` option of the `model` block has not been used, by specifying
-     the new options `block_static` and `block_dynamic`.
-
-   - There is now a default value for the global initialization file
-     (`GlobalInitFile` option of the configuration file): the `global_init.m`
-     in the Dynare configuration directory (typically
-     `$HOME/.config/dynare/global_init.m` under Linux and macOS, and
-     `c:\Users\USERNAME\AppData\Roaming\dynare\global_init.m` under Windows).
-
-   - For those compiling Dynare from source, the build system has been entirely
-     rewritten and now uses Meson; as a consequence, it is now faster and
-     easier to understand.
-
-- References:
-
-  - Andreasen, Martin M., Jesús Fernández-Villaverde, and Juan Rubio-Ramírez
-    (2018): “The Pruned State-Space System for Non-Linear DSGE Models: Theory
-    and Empirical Applications,” *Review of Economic Studies*, 85(1), 1-49.
-  - Brooks, Stephen P., and Andrew Gelman (1998): “General methods for
-    monitoring convergence of iterative simulations,” *Journal of Computational
-    and Graphical Statistics*, 7, pp. 434–455.
-  - Christiano, Eichenbaum and Charles L. Evans (2005): “Nominal Rigidities and
-    the Dynamic Effects of a Shock to Monetary Policy,” *Journal of Political
-    Economy*, 113(1), 1–45.
-  - Christiano, Lawrence J., Mathias Trabandt, and Karl Walentin (2010): “DSGE
-    Models for Monetary Policy Analysis,” In: *Handbook of Monetary Economics
-    3*, 285–367.
-  - Herbst, Edward and Schorfheide, Frank (2014): "Sequential Monte Carlo
-    Sampling for DSGE Models," *Journal of Applied Econometrics*, 29,
-    1073-1098.
-
-Incompatible changes
---------------------
-
- - The default value of the `mode_compute` option of the `estimation` command
-   has been changed to `5` (it was previously `4`).
-
- - When using block decomposition (with the `block` option of the `model`
-   block), the option `mfs` now defaults to `1`. This setting should deliver
-   better performance in perfect foresight simulation on most models.
-
- - The default location for the configuration file has changed. On Linux and
-   macOS, the configuration file is now searched by default under
-   `dynare/dynare.ini` in the configuration directories defined by the XDG
-   specification (typically `$HOME/.config/dynare/dynare.ini` for the
-   user-specific configuration and `/etc/xdg/dynare/dynare.ini` for the
-   system-wide configuration, the former having precedence over the latter).
-   Under Windows, the configuration file is now searched by default in
-   `%APPDATA%\dynare\dynare.ini` (typically
-   `c:\Users\USERNAME\AppData\Roaming\dynare\dynare.ini`).
-
- - The information stored in `oo_.endo_simul, oo_.exo_simul`, and `oo_.irfs` is
-   no longer duplicated in the base workspace. New helper functions
-   `send_endogenous_variables_to_workspace`,
-   `send_exogenous_variables_to_workspace`, and `send_irfs_to_workspace` have
-   been introduced to explicitly request these outputs and to mimic the old
-   behavior.
-
- - The `dynare_sensitivity` command has been renamed `sensitivity`. The old
-   name is still accepted but triggers a warning.
-
- - The syntax `resid(1)` is no longer supported.
-
- - The `mode_compute=6` option to the `estimation` command now recursively
-   updates the covariance matrix across the `NumberOfMh` Metropolis-Hastings
-   runs, starting with the `InitialCovarianceMatrix` in the first run, instead
-   of computing it from scratch in every Metropolis-Hastings run.
-
- - The `periods` command has been removed.
-
- - The `Sigma_e` command has been removed.
-
- - The `block` option of the `model` block no longer has an effect when used in
-   conjunction with `stoch_simul` or `estimation` commands.
-
- - The Dynare++ executable is no longer distributed since almost all of its
-   functionalities have been integrated inside Dynare for MATLAB/Octave.
-
- - A macro-processor variable defined without a value (such as `@#define var`
-   in the `.mod` file or alternatively `-Dvar` on the `dynare` command line) is
-   now assigned the `true` logical value (it was previously assigned `1`).
-
- - The `parallel_slave_open_mode` option of the `dynare` command has been
-   renamed `parallel_follower_open_mode`.
-
- - The `static` option of the `model_info` command is now deprecated and is
-   replaced by the `block_static` option.
-
-Bugs that were present in 5.5 and that have been fixed in 6.0
--------------------------------------------------------------
-
-* The `mh_initialize_from_previous_mcmc` option of the `estimation` command
-  would not work if estimation was conducted with a different prior and the
-  last draw in the previous MCMC fell outside the new prior bounds
-* When specifying a generalized inverse Gamma prior, the hyperparameter
-  computation would erroneously ignore the resulting mean shift
-* When using the `mh_recover` option of the `estimation` command, the status
-  bar always started at zero instead of showing the overall progress of the
-  recovered chain
-* The `model_diagnostics` command would fail to check the correctness of
-  user-defined steady state files
-* GSA: LaTeX output was not working as expected
-* Forecasts and filtered variables could not be retrieved with the
- `heteroskedastic_shocks` block
-* The OccBin smoother would potentially not display all smoothed shocks with
-  `heteroskedastic_filter` option
-* The OccBin smoother would crash if the number of requested periods was
-  smaller than the data length
-* The multivariate OccBin smoother would return wrong results if the constraint
-  was binding in the first period
-* The `plot_shock_decomposition` command would fail with the `init2shocks`
-  block if the `initial_condition_decomposition` was not run before
-* LaTeX output under Windows failed to compile for `plot_priors=1` option of
-  the `estimation` command and Brooks and Gelman (1998) convergence diagnostics
-* The plot produced by the `shock_decomposition` command was too big, making
-  the close button inaccessible
-* Monthly dates for October, November and December (*i.e.* with a 2-digit month
-  number) were not properly interpreted by the preprocessor
-* Theoretical moments computed by `stoch_simul` at `order=2` with `pruning`
-  would not contain unconditional and conditional variance decomposition
-
-
 Announcement for Dynare 5.5 (on 2023-10-23)
 ===========================================
 

README.md

@@ -149,10 +149,9 @@ Note that running the testsuite with Octave requires the additional packages `ps
 
 Often, it does not make sense to run the complete testsuite. For instance, if you modify codes only related to the perfect foresight model solver, you can decide to run only a subset of the integration tests, with:
 ```sh
-meson test -C <builddir> --suite deterministic_simulations
+meson test -C <builddir> deterministic_simulations
 ```
 This will run all the integration tests in `tests/deterministic_simulations`.
-This syntax also works with a nested directory (e.g. `--suite deterministic_simulations/purely_forward`).
 
 Finally if you want to run a single integration test, e.g. `deterministic_simulations/lbj/rbc.mod`:
 ```sh

doc/gsa/gsa.tex

@@ -22,7 +22,7 @@
 \begin{document}
 
 % ----------------------------------------------------------------
-\title{Sensitivity Analysis Toolbox for Dynare\thanks{Copyright \copyright~2012-2024 Dynare
+\title{Sensitivity Analysis Toolbox for DYNARE\thanks{Copyright \copyright~2012 Dynare
     Team. Permission is granted to copy, distribute and/or modify
     this document under the terms of the GNU Free Documentation
     License, Version 1.3 or any later version published by the Free
@@ -32,9 +32,9 @@
 
 \author{Marco Ratto\\
 European Commission, Joint Research Centre \\
-TP581\\21027 Ispra
+TP361, IPSC, \\21027 Ispra
 (VA) Italy\\
-\texttt{Marco.Ratto@ec.europa.eu}
+\texttt{marco.ratto@jrc.ec.europa.eu}
 \thanks{The author gratefully thanks Christophe Planas, Kenneth Judd, Michel Juillard,
 Alessandro Rossi, Frank Schorfheide and the participants to the
 Courses on Global Sensitivity Analysis for Macroeconomic
@@ -52,21 +52,21 @@ helpful suggestions.}}
 
 %-----------------------------------------------------------------------
 \begin{abstract}
-\noindent The Sensitivity Analysis Toolbox for Dynare is a set of
+\noindent The Sensitivity Analysis Toolbox for DYNARE is a set of
 MATLAB routines for the analysis of DSGE models with global
 sensitivity analysis. The routines are thought to be used within
-the Dynare 6 environment.
+the DYNARE v4 environment.
 
 
 \begin{description}
   \item \textbf{Keywords}: Stability Mapping , Reduced form solution, DSGE models,
-  Monte Carlo filtering, Global Sensitivity Analysis.
+  Monte Carlo filtering,   Global Sensitivity Analysis.
 \end{description}
 \end{abstract}
 \newpage
 % ----------------------------------------------------------------
 \section{Introduction} \label{s:intro}
-The Sensitivity Analysis Toolbox for Dynare is a collection of
+The Sensitivity Analysis Toolbox for DYNARE is a collection of
 MATLAB routines implemented to answer the following questions: (i)
 Which is the domain of structural coefficients assuring the
 stability and determinacy of a DSGE model? (ii) Which parameters
@@ -81,18 +81,20 @@ described in \cite{Ratto_CompEcon_2008}.
 
 
 \section{Use of the Toolbox}
-The Dynare parser now recognizes sensitivity analysis commands.
+The DYNARE parser now recognizes sensitivity analysis commands.
 The syntax is based on a single command:
 \vspace{0.5cm}
 
-\verb"sensitivity(option1=<opt1_val>,option2=<opt2_val>,...)"
+\verb"dynare_sensitivity(option1=<opt1_val>,option2=<opt2_val>,...)"
 
 \vspace{0.5cm} \noindent with a list of options described in the
 next section.
 
-In order to work properly, the sensitivity analysis Toolbox does not need
+With respect to the previous version of the toolbox, in order to
-a Dynare estimation environment to be set up. Rather, \verb"sensitivity"
+work properly, the sensitivity analysis Toolbox \emph{no longer}
-is the only command to run to
+needs that the DYNARE estimation environment is set-up.
+
+Therefore, \verb"dynare_sensitivity" is the only command to run to
 make a sensitivity analysis on a DSGE model\footnote{Of course,
 when the user needs to perform the mapping of the fit with a
 posterior sample, a Bayesian estimation has to be performed
@@ -206,17 +208,16 @@ a multivariate normal MC sample, with covariance matrix based on
 the inverse Hessian at the optimum: this analysis is useful when
 ML estimation is done (i.e. no Bayesian estimation);
 \item when \verb"ppost=1" the Toolbox analyses
-the RMSE's for the posterior sample obtained by Dynare's
+the RMSE's for the posterior sample obtained by DYNARE's
 Metropolis procedure.
 \end{enumerate}
 
-The use of cases 2. and 3. require an estimation step beforehand!
+The use of cases 2. and 3. requires an estimation step beforehand!
 To facilitate the sensitivity analysis after estimation, the
-\verb"sensitivity" command also allows to indicate some
+\verb"dynare_sensitivity" command also allows to indicate some
-options of \verb"estimation". These are:
+options of \verb"dynare_estimation". These are:
 \begin{itemize}
   \item \verb"datafile"
-  \item \verb"diffuse_filter"
   \item \verb"mode_file"
   \item \verb"first_obs"
   \item \verb"lik_init"
@@ -277,10 +278,10 @@ identifiable.
 \end{tabular}
 
 \vspace{1cm}
-\noindent For example, the following commands in the Dynare model file
+\noindent For example, the following commands in the DYNARE model file
 
 \vspace{1cm}
-\noindent\verb"sensitivity(identification=1, morris=2);"
+\noindent\verb"dynare_sensitivity(identification=1, morris=2);"
 
 \vspace{1cm}
 \noindent trigger the identification analysis using \cite{Iskrev2010,Iskrev2011}, jointly with the mapping of the acceptable region.
@@ -292,75 +293,75 @@ Sensitivity analysis results are saved on the hard-disk of the
 computer. The Toolbox uses a dedicated folder called \verb"GSA",
 located in \\
 \\
-\verb"<Dynare_file>\GSA", \\
+\verb"<DYNARE_file>\GSA", \\
 \\
-where \verb"<Dynare_file>.mod" is the name of the Dynare model
+where \verb"<DYNARE_file>.mod" is the name of the DYNARE model
 file.
 
 \subsection{Binary data files}
 A set of binary data files is saved in the \verb"GSA" folder:
 \begin{description}
-\item[]\verb"<Dynare_file>_prior.mat": this file stores
+\item[]\verb"<DYNARE_file>_prior.mat": this file stores
 information about the analyses performed sampling from the prior
 ranges, i.e. \verb"pprior=1" and \verb"ppost=0";
-\item[]\verb"<Dynare_file>_mc.mat": this file stores
+\item[]\verb"<DYNARE_file>_mc.mat": this file stores
 information about the analyses performed sampling from
 multivariate normal, i.e. \verb"pprior=0" and \verb"ppost=0";
-\item[]\verb"<Dynare_file>_post.mat": this file stores information
+\item[]\verb"<DYNARE_file>_post.mat": this file stores information
 about analyses performed using the Metropolis posterior sample,
 i.e. \verb"ppost=1".
 \end{description}
 
 \begin{description}
-\item[]\verb"<Dynare_file>_prior_*.mat": these files store
+\item[]\verb"<DYNARE_file>_prior_*.mat": these files store
 the filtered and smoothed variables for the prior MC sample,
 generated when doing  RMSE analysis (\verb"pprior=1" and
 \verb"ppost=0");
-\item[]\verb"<Dynare_file>_mc_*.mat": these files store
+\item[]\verb"<DYNARE_file>_mc_*.mat": these files store
 the filtered and smoothed variables for the multivariate normal MC
 sample, generated when doing  RMSE analysis (\verb"pprior=0" and
 \verb"ppost=0").
 \end{description}
 
 \subsection{Stability analysis}
-Figure files \verb"<Dynare_file>_prior_*.fig" store results for
+Figure files \verb"<DYNARE_file>_prior_*.fig" store results for
 the stability mapping from prior MC samples:
 \begin{description}
-\item[]\verb"<Dynare_file>_prior_stab_SA_*.fig": plots of the Smirnov
+\item[]\verb"<DYNARE_file>_prior_stab_SA_*.fig": plots of the Smirnov
-test analyses confronting the CDF of the sample fulfilling
+test analyses confronting the cdf of the sample fulfilling
-Blanchard-Kahn conditions with the CDF of the rest of the sample;
+Blanchard-Kahn conditions with the cdf of the rest of the sample;
-\item[]\verb"<Dynare_file>_prior_stab_indet_SA_*.fig": plots of the Smirnov
+\item[]\verb"<DYNARE_file>_prior_stab_indet_SA_*.fig": plots of the Smirnov
-test analyses confronting the CDF of the sample producing
+test analyses confronting the cdf of the sample producing
-indeterminacy with the CDF of the original prior sample;
+indeterminacy with the cdf of the original prior sample;
-\item[]\verb"<Dynare_file>_prior_stab_unst_SA_*.fig": plots of the Smirnov
+\item[]\verb"<DYNARE_file>_prior_stab_unst_SA_*.fig": plots of the Smirnov
-test analyses confronting the CDF of the sample producing unstable
+test analyses confronting the cdf of the sample producing unstable
-(explosive roots) behaviour with the CDF of the original prior
+(explosive roots) behaviour with the cdf of the original prior
 sample;
-\item[]\verb"<Dynare_file>_prior_stable_corr_*.fig": plots of
+\item[]\verb"<DYNARE_file>_prior_stable_corr_*.fig": plots of
 bivariate projections of the sample fulfilling Blanchard-Kahn
 conditions;
-\item[]\verb"<Dynare_file>_prior_indeterm_corr_*.fig": plots of
+\item[]\verb"<DYNARE_file>_prior_indeterm_corr_*.fig": plots of
 bivariate projections of the sample producing indeterminacy;
-\item[]\verb"<Dynare_file>_prior_unstable_corr_*.fig":  plots of
+\item[]\verb"<DYNARE_file>_prior_unstable_corr_*.fig":  plots of
 bivariate projections of the sample producing instability;
-\item[]\verb"<Dynare_file>_prior_unacceptable_corr_*.fig": plots of
+\item[]\verb"<DYNARE_file>_prior_unacceptable_corr_*.fig": plots of
 bivariate projections of the sample producing unacceptable
 solutions, i.e. either instability or indeterminacy or the
 solution could not be found (e.g. the steady state solution could
 not be found by the solver).
 \end{description}
-Similar conventions apply for \verb"<Dynare_file>_mc_*.fig" files,
+Similar conventions apply for \verb"<DYNARE_file>_mc_*.fig" files,
 obtained when samples from multivariate normal are used.
 
 \subsection{RMSE analysis}
-Figure files \verb"<Dynare_file>_rmse_*.fig" store results for the
+Figure files \verb"<DYNARE_file>_rmse_*.fig" store results for the
 RMSE analysis.
 \begin{description}
-\item[]\verb"<Dynare_file>_rmse_prior*.fig": save results for
+\item[]\verb"<DYNARE_file>_rmse_prior*.fig": save results for
 the analysis using prior MC samples;
-\item[]\verb"<Dynare_file>_rmse_mc*.fig": save results for
+\item[]\verb"<DYNARE_file>_rmse_mc*.fig": save results for
 the analysis using multivariate normal MC samples;
-\item[]\verb"<Dynare_file>_rmse_post*.fig": save results for
+\item[]\verb"<DYNARE_file>_rmse_post*.fig": save results for
 the analysis using Metropolis posterior samples.
 \end{description}
 
@@ -368,33 +369,33 @@ The following types of figures are saved (we show prior sample to
 fix ideas, but the same conventions are used for multivariate
 normal and posterior):
 \begin{description}
-\item[]\verb"<Dynare_file>_rmse_prior_*.fig": for each parameter, plots the CDF's
+\item[]\verb"<DYNARE_file>_rmse_prior_*.fig": for each parameter, plots the cdf's
 corresponding to the best 10\% RMES's of each observed series;
-\item[]\verb"<Dynare_file>_rmse_prior_dens_*.fig": for each parameter, plots the pdf's
+\item[]\verb"<DYNARE_file>_rmse_prior_dens_*.fig": for each parameter, plots the pdf's
 corresponding to the best 10\% RMES's of each observed series;
-\item[]\verb"<Dynare_file>_rmse_prior_<name of observedseries>_corr_*.fig": for each observed series plots the
+\item[]\verb"<DYNARE_file>_rmse_prior_<name of observedseries>_corr_*.fig": for each observed series plots the
 bi-dimensional projections of samples with the best 10\% RMSE's,
 when the correlation is significant;
-\item[]\verb"<Dynare_file>_rmse_prior_lnlik*.fig": for each observed
+\item[]\verb"<DYNARE_file>_rmse_prior_lnlik*.fig": for each observed
-series, plots \emph{in red} the CDF of the log-likelihood
+series, plots \emph{in red} the cdf of the log-likelihood
-corresponding to the best 10\% RMSE's, \emph{in green} the CDF of
+corresponding to the best 10\% RMSE's, \emph{in green} the cdf of
-the rest of the sample and \emph{in blue }the CDF of the full
+the rest of the sample and \emph{in blue }the cdf of the full
 sample; this allows to see the  presence of some idiosyncratic
 behaviour;
-\item[]\verb"<Dynare_file>_rmse_prior_lnpost*.fig": for each observed
+\item[]\verb"<DYNARE_file>_rmse_prior_lnpost*.fig": for each observed
-series, plots \emph{in red} the CDF of the log-posterior
+series, plots \emph{in red} the cdf of the log-posterior
-corresponding to the best 10\% RMSE's, \emph{in green} the CDF of
+corresponding to the best 10\% RMSE's, \emph{in green} the cdf of
-the rest of the sample and \emph{in blue }the CDF of the full
+the rest of the sample and \emph{in blue }the cdf of the full
 sample; this allows to see idiosyncratic behaviour;
-\item[]\verb"<Dynare_file>_rmse_prior_lnprior*.fig": for each observed
+\item[]\verb"<DYNARE_file>_rmse_prior_lnprior*.fig": for each observed
-series, plots \emph{in red} the CDF of the log-prior corresponding
+series, plots \emph{in red} the cdf of the log-prior corresponding
-to the best 10\% RMSE's, \emph{in green} the CDF of the rest of
+to the best 10\% RMSE's, \emph{in green} the cdf of the rest of
-the sample and \emph{in blue }the CDF of the full sample; this
+the sample and \emph{in blue }the cdf of the full sample; this
 allows to see idiosyncratic behaviour;
-\item[]\verb"<Dynare_file>_rmse_prior_lik_SA_*.fig": when
+\item[]\verb"<DYNARE_file>_rmse_prior_lik_SA_*.fig": when
 \verb"lik_only=1", this shows the Smirnov tests for the filtering
 of the best 10\% log-likelihood values;
-\item[]\verb"<Dynare_file>_rmse_prior_post_SA_*.fig": when
+\item[]\verb"<DYNARE_file>_rmse_prior_post_SA_*.fig": when
 \verb"lik_only=1", this shows the Smirnov test for the filtering
 of the best 10\% log-posterior values.
 \end{description}
@@ -404,19 +405,19 @@ In the case of the mapping of the reduced form solution, synthetic
 figures are saved in the \verb"\GSA" folder:
 
 \begin{description}
-\item[]\verb"<Dynare_file>_redform_<endo name>_vs_lags_*.fig":
+\item[]\verb"<DYNARE_file>_redform_<endo name>_vs_lags_*.fig":
 shows bar charts of the sensitivity indices for the \emph{ten most
 important} parameters driving the reduced form coefficients of the
 selected endogenous variables (\verb"namendo") versus lagged
 endogenous variables (\verb"namlagendo"); suffix \verb"log"
 indicates the results for  log-transformed entries;
-\item[]\verb"<Dynare_file>_redform_<endo name>_vs_shocks_*.fig":
+\item[]\verb"<DYNARE_file>_redform_<endo name>_vs_shocks_*.fig":
 shows bar charts of the sensitivity indices for the \emph{ten most
 important} parameters driving the reduced form coefficients of the
 selected endogenous variables (\verb"namendo") versus exogenous
 variables (\verb"namexo"); suffix \verb"log" indicates the results
 for  log-transformed entries;
-\item[]\verb"<Dynare_file>_redform_GSA(_log).fig": shows bar chart of
+\item[]\verb"<DYNARE_file>_redform_GSA(_log).fig": shows bar chart of
 all sensitivity indices for each  parameter: this allows to notice
 parameters that have a minor effect for \emph{any} of the reduced
 form coefficients,
@@ -448,24 +449,24 @@ without the need of any user's intervention.
 \subsection{Screening analysis}
 The results of the screening analysis with Morris sampling design
 are stored in the subfolder \verb"\GSA\SCREEN". The data file
-\verb"<Dynare_file>_prior" stores all the information of the
+\verb"<DYNARE_file>_prior" stores all the information of the
 analysis (Morris sample, reduced form coefficients, etc.).
 
 Screening analysis merely concerns reduced form coefficients.
 Similar synthetic bar charts as for the reduced form analysis with
 MC samples are saved:
 \begin{description}
-\item[]\verb"<Dynare_file>_redform_<endo name>_vs_lags_*.fig":
+\item[]\verb"<DYNARE_file>_redform_<endo name>_vs_lags_*.fig":
 shows bar charts of the elementary effect tests for the \emph{ten
 most important} parameters driving the reduced form coefficients
 of the selected endogenous variables (\verb"namendo") versus
 lagged endogenous variables (\verb"namlagendo");
-\item[]\verb"<Dynare_file>_redform_<endo name>_vs_shocks_*.fig":
+\item[]\verb"<DYNARE_file>_redform_<endo name>_vs_shocks_*.fig":
 shows bar charts of the elementary effect tests for the \emph{ten
 most important} parameters driving the reduced form coefficients
 of the selected endogenous variables (\verb"namendo") versus
 exogenous variables (\verb"namexo");
-\item[]\verb"<Dynare_file>_redform_screen.fig": shows bar chart of
+\item[]\verb"<DYNARE_file>_redform_screen.fig": shows bar chart of
 all elementary effect tests for each  parameter: this allows to
 identify parameters that have a minor effect for \emph{any} of the
 reduced form coefficients.

doc/manual/source/bibliography.rst

@@ -16,8 +16,8 @@ Bibliography
 * Bini, Dario A., Guy Latouche, and Beatrice Meini (2002): “Solving matrix polynomial equations arising in queueing problems,” *Linear Algebra and its Applications*, 340, 225–244.
 * Born, Benjamin and Johannes Pfeifer (2014): “Policy risk and the business cycle”, *Journal of Monetary Economics*, 68, 68-85.
 * Boucekkine, Raouf (1995): “An alternative methodology for solving nonlinear forward-looking models,” *Journal of Economic Dynamics and Control*, 19, 711–734.
-* Brayton, Flint and Peter Tinsley (1996): “A Guide to FRB/US: A Macroeconomic Model of the United States,” *Finance and Economics Discussion Series*, 1996-42.
+* Brayton, Flint and Peter Tinsley (1996): "A Guide to FRB/US: A Macroeconomic Model of the United States", *Finance and Economics Discussion Series*, 1996-42.
-* Brayton, Flint, Morris Davis and Peter Tulip (2000): “Polynomial Adjustment Costs in FRB/US,” *Unpublished manuscript*.
+* Brayton, Flint, Morris Davis and Peter Tulip (2000): "Polynomial Adjustment Costs in FRB/US", *Unpublished manuscript*.
 * Brooks, Stephen P., and Andrew Gelman (1998): “General methods for monitoring convergence of iterative simulations,” *Journal of Computational and Graphical Statistics*, 7, pp. 434–455.
 * Cardoso, Margarida F., R. L. Salcedo and S. Feyo de Azevedo (1996): “The simplex simulated annealing approach to continuous non-linear optimization,” *Computers & Chemical Engineering*, 20(9), 1065-1080.
 * Chib, Siddhartha and Srikanth Ramamurthy (2010): “Tailored randomized block MCMC methods with application to DSGE models,” *Journal of Econometrics*, 155, 19–38.
@@ -29,7 +29,7 @@ Bibliography
 * Collard, Fabrice and Michel Juillard (2001a): “Accuracy of stochastic perturbation methods: The case of asset pricing models,” *Journal of Economic Dynamics and Control*, 25, 979–999.
 * Collard, Fabrice and Michel Juillard (2001b): “A Higher-Order Taylor Expansion Approach to Simulation of Stochastic Forward-Looking Models with an Application to a Non-Linear Phillips Curve,” *Computational Economics*, 17, 125–139.
 * Corana, Angelo, M. Marchesi, Claudio Martini, and Sandro Ridella (1987): “Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm”, *ACM Transactions on Mathematical Software*, 13(3), 262–280.
-* Cuba-Borda, Pablo, Luca Guerrieri, Matteo Iacoviello, and Molin Zhong (2019): “Likelihood evaluation of models with occasionally binding constraints,” Journal of Applied Econometrics, 34(7), 1073-1085
+* Cuba-Borda, Pablo, Luca Guerrieri, Matteo Iacoviello, and Molin Zhong (2019): "Likelihood evaluation of models with occasionally binding constraints", Journal of Applied Econometrics, 34(7), 1073-1085
 * Del Negro, Marco and Frank Schorfheide (2004): “Priors from General Equilibrium Models for VARs”, *International Economic Review*, 45(2), 643–673.
 * Dennis, Richard (2007): “Optimal Policy In Rational Expectations Models: New Solution Algorithms”, *Macroeconomic Dynamics*, 11(1), 31–55.
 * Duffie, Darrel and Kenneth J. Singleton (1993): “Simulated Moments Estimation of Markov Models of Asset Prices”, *Econometrica*, 61(4), 929-952.
@@ -49,7 +49,7 @@ Bibliography
 * Hansen, Lars P. (1982): “Large sample properties of generalized method of moments estimators,” Econometrica, 50(4), 1029–1054.
 * Hansen, Nikolaus and Stefan Kern (2004): “Evaluating the CMA Evolution Strategy on Multimodal Test Functions”. In: *Eighth International Conference on Parallel Problem Solving from Nature PPSN VIII*, Proceedings, Berlin: Springer, 282–291.
 * Harvey, Andrew C. and Garry D.A. Phillips (1979): “Maximum likelihood estimation of regression models with autoregressive-moving average disturbances,” *Biometrika*, 66(1), 49–58.
-* Herbst, Edward and Schorfheide, Frank (2014): “Sequential Monte Carlo Sampling for DSGE Models,” *Journal of Applied Econometrics*, 29, 1073-1098.
+* Herbst, Edward and Schorfheide, Frank (2014): "Sequential monte-carlo sampling for DSGE models," *Journal of Applied Econometrics*, 29, 1073-1098.
 * Herbst, Edward (2015): “Using the “Chandrasekhar Recursions” for Likelihood Evaluation of DSGE Models,” *Computational Economics*, 45(4), 693–705.
 * Ireland, Peter (2004): “A Method for Taking Models to the Data,” *Journal of Economic Dynamics and Control*, 28, 1205–26.
 * Iskrev, Nikolay (2010): “Local identification in DSGE models,” *Journal of Monetary Economics*, 57(2), 189–202.

doc/manual/source/conf.py

@@ -71,11 +71,12 @@ latex_elements = {
                     warningBorderColor={RGB}{255,50,50},OuterLinkColor={RGB}{34,139,34}, \
                     InnerLinkColor={RGB}{51,51,255},TitleColor={RGB}{51,51,255}',
     'papersize': 'a4paper',
+    'preamble': r'\DeclareUnicodeCharacter{200B}{}', # Part of the workaround for #1707
 }
 
 latex_documents = [
     (master_doc, 'dynare-manual.tex', u'Dynare Reference Manual',
-     u'Dynare Team', 'manual'),
+     u'Dynare team', 'manual'),
 ]
 
 man_pages = [

doc/manual/source/dynare-misc-commands.rst

@@ -8,17 +8,17 @@
 Dynare misc commands
 ####################
 
-.. matcomm:: send_endogenous_variables_to_workspace ;
+.. matcomm:: send_endogenous_variables_to_workspace
 
     Puts the simulation results for the endogenous variables stored in ``oo_.endo_simul`` 
     into vectors with the same name as the respective variables into the base workspace.
 
-.. matcomm:: send_exogenous_variables_to_workspace ;
+.. matcomm:: send_exogenous_variables_to_workspace
 
     Puts the simulation results for the exogenous variables stored in ``oo_.exo_simul`` 
     into vectors with the same name as the respective variables into the base workspace.
 
-.. matcomm:: send_irfs_to_workspace ;
+.. matcomm:: send_irfs_to_workspace
 
     Puts the IRFs stored in ``oo_.irfs`` into vectors with the same name into the base workspace.
 
@@ -230,97 +230,27 @@ Dynare misc commands
    Searches all occurrences of a variable in a model, and prints the
    equations where the variable appear in the command line window. If OPTION is
    set to `withparamvalues`, the values of the parameters (if available) are
-   displayed instead of the name of the parameters. Requires the `json` command
+   displayed instead of the name of the parameters.
-   line option to be set.
 
    *Example*
 
-      Assuming that we already ran a `.mod` file and that the workspace has not
+   Assuming that we already ran a `.mod` file and that the workspace has not
-      been cleaned after, we can search for all the equations containing variable `X`
+   been cleaned after, we can search for all the equations containing variable `X`
 
-      ::
+   ::
 
-         >> search X
+      >> search X
 
-         Y = alpha*X/(1-X)+e;
+      Y = alpha*X/(1-X)+e;
 
-         diff(X) = beta*(X(-1)-mX) + gamma1*Z + gamma2*R + u;
+      diff(X) = beta*(X(-1)-mX) + gamma1*Z + gamma2*R + u;
 
-      To replace the parameters with estimated or calibrated parameters:
+   To replace the parameters with estimated or calibrated parameters:
 
-      ::
+   ::
 
-         >> search X withparamvalues
+      >> search X withparamvalues
 
-         Y = 1.254634*X/(1-X)+e;
+      Y = 1.254634*X/(1-X)+e;
 
-         diff(X) = -0.031459*(X(-1)-mX) + 0.1*Z - 0.2*R + u;
+      diff(X) = -0.031459*(X(-1)-mX) + 0.1*Z - 0.2*R + u;
-
-   |br|
-
-
-.. matcomm:: dplot [OPTION VALUE[ ...]]
-
-   Plot expressions extracting data from different dseries objects.
-
-   *Options*
-
-   .. option:: --expression EXPRESSION
-
-      ``EXPRESSION`` is a mathematical expression involving variables
-      available in the dseries objects, dseries methods, numbers or
-      parameters. All the referenced objects are supposed to be
-      available in the calling workspace.
-
-   .. option:: --dseries NAME
-
-      ``NAME`` is the name of a dseries object from which the
-      variables involved in ``EXPRESSION`` will be extracted.
-
-   .. option:: --range DATE1:DATE2
-
-      This option is not mandatory and allows to plot the expressions
-      only over a sub-range. ``DATE1`` and ``DATE2`` must be dates as
-      defined in :ref:`dates in a mod file`.
-
-   .. option:: --style MATLAB_SCRIPT_NAME
-
-      Name of a Matlab script (without extension) containing Matlab
-      commands to customize the produced figure.
-
-   .. option:: --title MATLAB_STRING
-
-      Adds a title to the figure.
-
-   .. option:: --with-legend
-
-      Prints a legend below the produced plot.
-
-   *Remarks*
-
-    - More than one --expression argument is allowed, and they must come first.
-
-    - For each dseries object we plot all the expressions. We use two
-      nested loops, the outer loop is over the dseries objects and the
-      inner loop over the expressions. This determines the ordering of
-      the plotted lines.
-
-    - All dseries objects must be defined in the calling workspace, if a
-      dseries object is missing the routine throws a warning (we only
-      build the plots for the available dseries objects), if all dseries
-      objects are missing the routine throws an error.
-
-    - If the range is not provided, the expressions cannot involve leads or lags.
-
-   *Example*
-
-      ::
-
-         >> toto = dseries(randn(100,3), dates('2000Q1'), {'x','y','z'});
-         >> noddy = dseries(randn(100,3), dates('2000Q1'), {'x','y','z'});
-         >> b = 3;
-         >> dplot --expression 2/b*cumsum(x/y(-1)-1) --dseries toto --dseries noddy --range 2001Q1:2024Q1 --title 'This is my plot'
-
-      will produce plots for ``2/b*cumsum(x/y(-1)-1)``, where ``x`` and
-      ``y`` are variables in dseries objects ``toto`` and ``noddy``, in
-      the same figure.

doc/manual/source/index.rst

@@ -11,7 +11,7 @@ Currently the development team of Dynare is composed of:
 * Willi Mutschler (University of Tübingen)
 * Johannes Pfeifer (University of the Bundeswehr Munich)
 * Marco Ratto (European Commission, Joint Research Centre - JRC)
-* Normann Rion (CEPREMAP)
+* Normann Rion (CY Cergy Paris Université and CEPREMAP)
 * Sébastien Villemot (CEPREMAP)
 
 The following people used to be members of the team:

doc/manual/source/introduction.rst

@@ -94,24 +94,26 @@ Citing Dynare in your research
 You should cite Dynare if you use it in your research. The
 recommended way todo this is to cite the present manual, as:
 
-    Stéphane Adjemian, Michel Juillard, Frédéric Karamé, Willi Mutschler,
+    Stéphane Adjemian, Houtan Bastani, Michel Juillard, Frédéric Karamé,
-    Johannes Pfeifer, Marco Ratto, Normann Rion and Sébastien Villemot (2024),
+    Ferhat Mihoubi, Willi Mutschler, Johannes Pfeifer, Marco Ratto,
-    “Dynare: Reference Manual, Version 6,” *Dynare Working Papers*, 80, CEPREMAP
+    Normann Rion and Sébastien Villemot (2022), “Dynare: Reference Manual,
+    Version 5,” *Dynare Working Papers*, 72, CEPREMAP
 
 For convenience, you can copy and paste the following into your BibTeX file:
 
     .. code-block:: bibtex
 
-        @TechReport{Adjemianetal2024,
+        @TechReport{Adjemianetal2022,
-          author      = {Adjemian, St\'ephane and Juillard, Michel and
+          author      = {Adjemian, St\'ephane and Bastani, Houtan and
-                         Karam\'e, Fr\'ederic and Mutschler, Willi and
+                         Juillard, Michel and Karam\'e, Fr\'ederic and
-                         Pfeifer, Johannes and Ratto, Marco and
+                         Mihoubi, Ferhat and Mutschler, Willi
+                         and Pfeifer, Johannes and Ratto, Marco and
                          Rion, Normann and Villemot, S\'ebastien},
-          title       = {Dynare: Reference Manual, Version 6},
+          title       = {Dynare: Reference Manual Version 5},
-          year        = {2024},
+          year        = {2022},
           institution = {CEPREMAP},
           type        = {Dynare Working Papers},
-          number      = {80},
+          number      = {72},
         }
 
 If you want to give a URL, use the address of the Dynare website:

doc/manual/source/the-model-file.rst

@@ -1259,8 +1259,7 @@ command, the list of transformed model equations using the
 ``write_latex_dynamic_model command``, and the list of static model
 equations using the ``write_latex_static_model`` command.
 
-.. command:: write_latex_original_model ;
+.. command:: write_latex_original_model (OPTIONS);
-             write_latex_original_model (OPTIONS);
 
     |br| This command creates two LaTeX files: one
     containing the model as defined in the model block and one
@@ -1335,8 +1334,7 @@ equations using the ``write_latex_static_model`` command.
         See :opt:`write_equation_tags`
 
 
-.. command:: write_latex_static_model ;
+.. command:: write_latex_static_model (OPTIONS);
-             write_latex_static_model (OPTIONS);
 
     |br| This command creates two LaTeX files: one
     containing the static model and one containing the LaTeX
@@ -1371,7 +1369,7 @@ equations using the ``write_latex_static_model`` command.
 
         See :opt:`write_equation_tags`.
 
-.. command:: write_latex_steady_state_model ;
+.. command:: write_latex_steady_state_model
 
     |br| This command creates two LaTeX files: one containing the steady
     state model and one containing the LaTeX document header
@@ -2947,8 +2945,8 @@ Finding the steady state with Dynare nonlinear solver
 
            ``5``
 
-                Newton algorithm with a sparse Gaussian elimination (SPE)
+                Newton algorithm with a sparse Gaussian elimination
-                solver at each iteration (requires ``bytecode`` option, see
+                (SPE) (requires ``bytecode`` option, see
                 :ref:`model-decl`).
 
            ``6``
@@ -2966,7 +2964,7 @@ Finding the steady state with Dynare nonlinear solver
            ``8``
 
                 Newton algorithm with a Stabilized Bi-Conjugate
-                Gradient (BiCGStab) solver at each iteration (requires
+                Gradient (BICGSTAB) solver at each iteration (requires
                 bytecode and/or block option, see :ref:`model-decl`).
 
            ``9``
@@ -3003,23 +3001,19 @@ Finding the steady state with Dynare nonlinear solver
                 blocks that can be evaluated rather than solved; and evaluations
                 of the residual and Jacobian of the model are more efficient
                 because only the relevant elements are recomputed at every
-                iteration. This option is typically used with the
+                iteration.
-                ``perfect_foresight_solver`` command with purely backward,
-                forward or static models, or with routines for semi-structural
-                models, and it must *not* be combined with option ``block`` of
-                the ``model`` block. Also note that for those models, the block
-                decomposition is performed as if ``mfs=3`` had been passed to
-                the ``model`` block, and the decomposition is slightly
-                different because it is computed in a time-recursive fashion
-                (*i.e.* in such a way that the simulation is meant to be done
-                with the outer loop on periods and the inner loop on blocks;
-                while for models with both leads and lags, the outer loop is on
-                blocks and the inner loop is on periods).
 
            ``14``
 
-                Same as ``12``, except that it applies a trust region solver
+                Computes a block decomposition and then applies a trust region
-                (similar to ``4``) to the blocks.
+                solver with autoscaling on those smaller blocks rather than on
+                the full nonlinear system. This is similar to ``4``, but is
+                typically more efficient. The block decomposition is done at
+                the preprocessor level, which brings two benefits: it
+                identifies blocks that can be evaluated rather than solved; and
+                evaluations of the residual and Jacobian of the model are more
+                efficient because only the relevant elements are recomputed at
+                every iteration.
 
        |br| Default value is ``4``.
 
@@ -3686,64 +3680,60 @@ speed-up on large models.
            ``0``
 
                Use a Newton algorithm with a direct sparse LU solver at each
-               iteration, applied to the stacked system of all equations in all
+               iteration, applied on the stacked system of all the equations at
-               periods (Default).
+               every period (Default).
 
            ``1``
 
                Use the Laffargue-Boucekkine-Juillard (LBJ) algorithm proposed
-               in *Juillard (1996)* on top of a LU solver. It is slower
+               in *Juillard (1996)*. It is slower than ``stack_solve_algo=0``,
-               than ``stack_solve_algo=0``, but may be less memory consuming on
+               but may be less memory consuming on big models. Note that if the
-               big models. Note that if the ``block`` option is used (see
+               ``block`` option is used (see :ref:`model-decl`), a simple
-               :ref:`model-decl`), a simple Newton algorithm with sparse
+               Newton algorithm with sparse matrices is used for blocks which
-               matrices, applied to the stacked system of all block equations
+               are purely backward or forward (of type ``SOLVE BACKWARD`` or
-               in all periods, is used for blocks which are purely backward or
+               ``SOLVE FORWARD``, see :comm:`model_info`), since LBJ only makes
-               forward (of type ``SOLVE BACKWARD`` or ``SOLVE FORWARD``, see
+               sense on blocks with both leads and lags (of type ``SOLVE TWO
-               :comm:`model_info`), since LBJ only makes sense on blocks with
+               BOUNDARIES``).
-               both leads and lags (of type ``SOLVE TWO BOUNDARIES``).
 
            ``2``
 
-               Use a Newton algorithm with a Generalized Minimal Residual
+               Use a Newton algorithm with a Generalized Minimal
-               (GMRES) solver at each iteration, applied on the stacked system
+               Residual (GMRES) solver at each iteration (requires
-               of all equations in all periods (requires ``bytecode`` and/or
+               ``bytecode`` and/or ``block`` option, see
-               ``block`` option, see :ref:`model-decl`)
+               :ref:`model-decl`)
 
            ``3``
 
-               Use a Newton algorithm with a Stabilized Bi-Conjugate Gradient
+               Use a Newton algorithm with a Stabilized Bi-Conjugate
-               (BiCGStab) solver at each iteration, applied on the stacked
+               Gradient (BICGSTAB) solver at each iteration (requires
-               system of all equations in all periods (requires ``bytecode``
+               ``bytecode`` and/or ``block`` option, see
-               and/or ``block`` option, see :ref:`model-decl`).
+               :ref:`model-decl`).
 
            ``4``
 
-               Use a Newton algorithm with a direct sparse LU solver and an
+               Use a Newton algorithm with an optimal path length at
-               optimal path length at each iteration, applied on the stacked
+               each iteration (requires ``bytecode`` and/or ``block``
-               system of all equations in all periods (requires ``bytecode``
+               option, see :ref:`model-decl`).
-               and/or ``block`` option, see :ref:`model-decl`).
 
            ``5``
 
-               Use the Laffargue-Boucekkine-Juillard (LBJ) algorithm proposed
+               Use a Newton algorithm with a sparse Gaussian
-               in *Juillard (1996)* on top of a sparse Gaussian elimination
+               elimination (SPE) solver at each iteration (requires
-               (SPE) solver. The latter takes advantage of the similarity of
+               ``bytecode`` option, see :ref:`model-decl`).
-               the Jacobian across periods when searching for the pivots
-               (requires ``bytecode`` option, see :ref:`model-decl`).
 
            ``6``
 
-               Synonymous for ``stack_solve_algo=1``. Kept for backward
+               Synonymous for ``stack_solve_algo=1``. Kept for historical
-               compatibility.
+               reasons.
 
            ``7``
 
-               Allows the user to solve the perfect foresight model with the
+               Allows the user to solve the perfect foresight model
-               solvers available through option ``solve_algo``, applied on the
+               with the solvers available through option
-               stacked system of all equations in all periods (See
+               ``solve_algo`` (See :ref:`solve_algo <solvalg>` for a
-               :ref:`solve_algo <solvalg>` for a list of possible values, note
+               list of possible values, note that values 5, 6, 7 and
-               that values ``5``, ``6``, ``7`` and ``8``, which require ``bytecode`` and/or
+               8, which require ``bytecode`` and/or ``block`` options,
-               ``block`` options, are not allowed). For instance, the following
+               are not allowed). For instance, the following
                commands::
 
                     perfect_foresight_setup(periods=400);
@@ -3760,10 +3750,7 @@ speed-up on large models.
     .. option:: solve_algo
 
        See :ref:`solve_algo <solvalg>`. Allows selecting the solver
-       used with ``stack_solve_algo=7``. Also used for purely backward, forward
+       used with ``stack_solve_algo=7``.
-       and static models (when neither the ``block`` nor the ``bytecode`` option
-       of the ``model`` block is specified); for those models, the values
-       ``12`` and ``14`` are especially relevant.
 
     .. option:: no_homotopy
 
@@ -3850,9 +3837,9 @@ speed-up on large models.
     .. option:: lmmcp
 
        Solves the perfect foresight model with a Levenberg-Marquardt
-       mixed complementarity problem (LMMCP) solver (*Kanzow and Petra,
+       mixed complementarity problem (LMMCP) solver (*Kanzow and Petra
-       2004*), which allows to consider inequality constraints on
+       (2004)*), which allows to consider inequality constraints on
-       the endogenous variables (such as a zero lower bound, henceforth ZLB, on the nominal interest
+       the endogenous variables (such as a ZLB on the nominal interest
        rate or a model with irreversible investment). This option is
        equivalent to ``stack_solve_algo=7`` **and**
        ``solve_algo=10``. Using the LMMCP solver avoids the need for min/max 
@@ -5781,14 +5768,6 @@ All of these elements are discussed in the following.
 
         See :opt:`simul_check_ahead_periods <simul_check_ahead_periods = INTEGER>`.
 
-    .. option:: simul_reset_check_ahead_periods
-
-        See :opt:`simul_reset_check_ahead_periods`.
-
-    .. option:: simul_max_check_ahead_periods
-
-        See :opt:`simul_max_check_ahead_periods <simul_max_check_ahead_periods = INTEGER>`.
-
     .. option:: simul_curb_retrench
 
         See :opt:`simul_curb_retrench`.
@@ -6512,8 +6491,8 @@ observed variables.
 
        Do not use the kalman filter to evaluate the likelihood, but instead
        evaluate the conditional likelihood, based on the first order reduced
-       form of the model, by assuming that the initial state vector is at its 
+       form of the model, by assuming that the initial state vector is 0 for all
-       steady state. This approach requires that:
+       the endogenous variables. This approach requires that:
 
        1. The number of structural innovations be equal to the number of observed variables.
 
@@ -6528,7 +6507,7 @@ observed variables.
        Note however that the conditional likelihood is sensitive to the choice
        for the initial condition, which can be an issue if the data are
        initially far from the steady state. This option is not compatible with
-       ``analytic_derivation``.
+       ``analytical_derivation``.
 
     .. option:: conf_sig = DOUBLE
 
@@ -7490,8 +7469,7 @@ observed variables.
 
                Instructs Dynare to use the *Herbst and Schorfheide (2014)*
                version of the Sequential Monte-Carlo sampler instead of the
-               standard Random-Walk Metropolis-Hastings. Does not yet support
+               standard Random-Walk Metropolis-Hastings.
-               ``moments_varendo``, ``bayesian_irf``, and ``smoother``.
 
            ``'dsmh'``
 
@@ -11761,7 +11739,7 @@ with ``discretionary_policy`` or for optimal simple rules with ``osr``
 
     With ``discretionary_policy``, the objective function must be quadratic.
 
-.. command:: evaluate_planner_objective ;
+.. command:: evaluate_planner_objective;
              evaluate_planner_objective (OPTIONS...);
 
     This command computes, displays, and stores the value of the
@@ -14278,7 +14256,7 @@ assumed that each equation is written as ``VARIABLE = EXPRESSION`` or
 ``T(VARIABLE) = EXPRESSION`` where ``T(VARIABLE)`` stands for a transformation
 of an endogenous variable (``log`` or ``diff``). This representation, where each
 equation determines the endogenous variable on the LHS, can be exploited when
-simulating the model (see algorithms ``12`` and ``14`` in :ref:`solve_algo <solvalg>`)
+simulating the model (see algorithms 12 and 14 in :ref:`solve_algo <solvalg>`)
 and is mandatory to define auxiliary models used for computing expectations (see
 below).
 
@@ -14315,7 +14293,7 @@ a trend target to which the endogenous variables may be attracted in the long-ru
    :math:`n\times 1` vector of parameters, :math:`A_i` (:math:`i=0,\ldots,p`)
    are :math:`n\times n` matrices of parameters, and :math:`A_0` is non
    singular square matrix. Vector :math:`\mathbf{c}` and matrices :math:`A_i`
-   (:math:`i=0,\ldots,p`) are set by parsing the equations in the
+   (:math:`i=0,\ldots,p`) are set by Dynare by parsing the equations in the
    ``model`` block. Then, Dynare builds a VAR(1)-companion form model for
    :math:`\mathcal{Y}_t = (1, Y_t, \ldots, Y_{t-p+1})'` as:
 
@@ -14526,7 +14504,7 @@ up to time :math:`t-\tau`, :math:`\mathcal{Y}_{\underline{t-\tau}}`) is:
 
 In a semi-structural model, variables appearing in :math:`t+h` (*e.g.*
 the expected output gap in a dynamic IS curve or expected inflation in a
-New Keynesian Phillips curve) will be replaced by the expectation implied by an auxiliary VAR
+(New Keynesian) Phillips curve) will be replaced by the expectation implied by an auxiliary VAR
 model. Another use case is for the computation of permanent
 incomes. Typically, consumption will depend on something like:
 
@@ -14534,13 +14512,13 @@ incomes. Typically, consumption will depend on something like:
 
       \sum_{h=0}^{\infty} \beta^h y_{t+h|t-\tau}
 
-Assuming that :math:`0<\beta<1` and knowing the limit of geometric series, the conditional expectation of this variable can be evaluated based on the same auxiliary model:
+Assuming that $0<\beta<1$ and knowing the limit of geometric series, the conditional expectation of this variable can be evaluated based on the same auxiliary model:
 
    .. math ::
 
       \mathbb E \left[\sum_{h=0}^{\infty} \beta^h y_{t+h}\Biggl| \mathcal{Y}_{\underline{t-\tau}}\right] = \alpha \mathcal{C}^\tau(I-\beta\mathcal{C})^{-1}\mathcal{Y}_{t-\tau}
 
-Finite discounted sums can also be considered.
+More generally, it is possible to consider finite discounted sums.
 
 .. command:: var_expectation_model (OPTIONS...);
 
@@ -14725,7 +14703,7 @@ simply add the exogenous variables to the PAC equation (without the weight
     ``trend_component_model``, to compute the VAR based expectations for the
     expected changes in the target, *i.e.* to evaluate
     :math:`\sum_{i=0}^{\infty} d_i \Delta y^{\star}_{t+i}`. The infinite sum
-    will then be replaced by a linear combination, defined by a vector :math:`h`,  of the variables involved in
+    will then be replaced by a linear combination of the variables involved in
     the companion representation of the auxiliary model. The weights defining
     the linear combination are nonlinear functions of the
     :math:`(a_i)_{i=0}^{m-1}` coefficients in the PAC equation. This option is
@@ -14745,16 +14723,6 @@ simply add the exogenous variables to the PAC equation (without the weight
     or expression is given) is consistent with the asymptotic growth rate of the
     endogenous variable.
 
-    .. option:: kind = dd | dl
-
-    Instructs Dynare how to compute the vector :math:`h`, the weights
-    defining the linear combination of the companion VAR
-    variables. The default value ``dd`` must be used if the target
-    appears in first difference in the auxiliary model, see equation
-    (A.79) in *Brayton et alii (2000)*, while value ``dl`` must be
-    used if the target shows up in level in the auxiliary model,
-    equation (A.74) in *Brayton et alii (2000)*.
-
 
 .. operator:: pac_expectation (NAME_OF_PAC_MODEL);
 
@@ -14765,89 +14733,7 @@ simply add the exogenous variables to the PAC equation (without the weight
    the variables involved in the companion representation of the auxiliary model
    or by a recursive forward equation.
 
-
+   |br|
-The PAC equation target can be composite and defined as a weighted sum
-of stationary and non stationary components. Such a target requires an
-additional equation in the model block, with the target variable on
-the left hand-side and the components in the right hand-side. Each
-component must be an endogenous variable in the auxiliary model. The
-characteristics of each component must be described in the
-``pac_target_info`` block, see below, and the
-``pac_target_nonstationary`` operator must be used in the error
-correction term of the PAC equation to link the target to the provided
-description. Note that composite targets make only sense if the
-auxiliary model is not a trend component model (where all the
-variables are non stationary).
-
-.. block:: pac_target_info (NAME_OF_PAC_MODEL);
-
-   |br| This block enables the user to provide the properties of each
-   component of a target in PAC models with a composite target. The
-   ``NAME_OF_PAC_MODEL`` argument refers to a PAC model (must match
-   the value of option ``model_name`` in the declaration of a PAC
-   model).
-
-   On the first line of the block, the name of the composite target
-   variable must be provided using the following syntax::
-
-     target VARIABLE_NAME ;
-
-   where ``VARIABLE_NAME`` is a declared endogenous variable, its
-   associated equation is not part of the auxiliary model but all the
-   components (the variables on the right hand-side) must be defined
-   in the auxiliary model. Next, the following line declares the name
-   of the auxilary variable that will appear in the error correction
-   term, this variable contains only the non stationary components of
-   the target::
-
-     auxname_target_nonstationary NAME ;
-
-   The block should contain the following group of lines for each
-   stationary component::
-
-       component STATIONARY_VARIABLE_NAME ;
-       kind ll ;
-       auxname AUX_VAR_NAME ;
-
-
-   where ``STATIONARY_VARIABLE_NAME`` is the name of a stationary
-   variable appearing in the right hand-side of the equation defining
-   the target ``VARIABLE_NAME``. The second line instructs Dynare that
-   the component appears in levels in the auxiliary model and in the
-   PAC expectations. The third line specifies the name of the
-   auxiliary variable created by Dynare for the component of the PAC
-   expectation related to ``STATIONARY_VARIABLE_NAME``.
-
-   The block should contain the following group of lines for each
-   nonstationary component::
-
-     component NONSTATIONARY_VARIABLE_NAME ;
-     kind dd | dl ;
-     auxname AUX_VAR_NAME ;
-     growth PARAMETER_NAME | VARIABLE_NAME | EXPRESSION | DOUBLE ;
-
-   where ``NONSTATIONARY_VARIABLE_NAME`` is the name of a
-   nonstationary variable appearing in the right hand-side of the
-   equation defining the target ``VARIABLE_NAME``. The second line
-   instructs Dynare on how to calculate the weights that define the linear
-   combination of the companion VAR variables. Use value ``dd`` if the
-   target appears in first difference in the auxiliary model, or
-   ``dl`` if the target shows up in level in the auxiliary model. The
-   third line sets the name of the auxiliary variable created by
-   Dynare for the component of the PAC expectation related to
-   ``NONSTATIONARY_VARIABLE_NAME``. The fourth line is mandatory if a
-   growth neutrality correction is required. The provided value for
-   this option must be consistent with the asymptotic growth rate of
-   the PAC endogenous variable.
-
-
-.. operator:: pac_target_nonstationary (NAME_OF_PAC_MODEL);
-
-   |br| This operator is only required in presence of a composite
-   target in the PAC equation. The operator, used in the error
-   correction term of the PAC equation, selects the non stationary
-   components of the target.
-
 
 .. matcomm::  pac.initialize(NAME_OF_PAC_MODEL);
 .. matcomm::  pac.update(NAME_OF_PAC_MODEL);
@@ -14860,33 +14746,33 @@ variables are non stationary).
   the infinite sum in the MCE case).
 
 
-*Example (trend component auxiliary model)*
+*Example*
 
     ::
 
          trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']);
 
-         pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+         pac_model(auxiliary_model_name=toto, discount=beta, growth=diff(x1(-1)), model_name=pacman);
 
          model;
 
-           [name='eq:y']
+         [name='eq:y']
-           y = (1-rho_1-rho_2)*diff(x2(-1)) + rho_1*y(-1) + rho_2*y(-2) + ey;
+         y = rho_1*y(-1) + rho_2*y(-2) + ey;
 
-           [name='eq:x1']
+         [name='eq:x1']
-           diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;
+         diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;
 
-           [name='eq:x2']
+         [name='eq:x2']
-           diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;
+         diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;
 
-           [name='eq:x1bar']
+         [name='eq:x1bar']
-           x1bar = x1bar(-1) + ex1bar;
+         x1bar = x1bar(-1) + ex1bar;
 
-           [name='eq:x2bar']
+         [name='eq:x2bar']
-           x2bar = x2bar(-1) + ex2bar;
+         x2bar = x2bar(-1) + ex2bar;
 
-           [name='zpac']
+         [name='zpac']
-           diff(z) = e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+         diff(z) = e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
 
          end;
 
@@ -14895,51 +14781,6 @@ variables are non stationary).
          pac.update.expectation('pacman');
 
 
-*Example (VAR auxiliary model and composite target)*
-
-    ::
-
-       var_model(model_name=toto, eqtags=['eq:x', 'eq:y']);
-
-       pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
-
-       pac_target_info(pacman);
-
-         target v;
-         auxname_target_nonstationary vns;
-
-         component y;
-         auxname pv_y_;
-         kind ll;
-
-         component x;
-         growth diff(x(-1));
-         auxname pv_dx_;
-         kind dd;
-
-       end;
-
-       model;
-
-         [name='eq:y']
-         y = a_y_1*y(-1) + a_y_2*diff(x(-1)) + b_y_1*y(-2) + b_y_2*diff(x(-2)) + ey ;
-
-
-         [name='eq:x']
-         diff(x) = b_x_1*y(-2) + b_x_2*diff(x(-1)) + ex ;
-
-         [name='eq:v']
-         v = x + d_y*y ;  // Composite PAC target, no residuals here only variables defined in the auxiliary model.
-
-         [name='zpac']
-         diff(z) = e_c_m*(pac_target_nonstationary(pacman)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
-
-       end;
-
-       pac.initialize('pacman');
-
-       pac.update.expectation('pacman');
-
 Estimation of a PAC equation
 ----------------------------
 
@@ -16020,7 +15861,7 @@ Misc commands
     ``pdflatex`` and automatically tries to load all required
     packages. Requires the following LaTeX packages:
     ``breqn``, ``psfrag``, ``graphicx``, ``epstopdf``, ``longtable``,
-    ``booktabs``, ``caption``, ``float,`` ``amsmath``, ``amsfonts``, ``amssymb``, 
+    ``booktabs``, ``caption``, ``float,`` ``amsmath``, ``amsfonts``,
     and ``morefloats``.
 
 

doc/manual/source/time-series.rst

@@ -22,8 +22,6 @@ Dates
 =====
 .. highlight:: matlab
 
-.. _dates in a mod file:
-
 Dates in a mod file
 -------------------
 

doc/manual/utils/dynare_dom.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 
-# Copyright © 2018-2024 Dynare Team
+# Copyright © 2018-2019 Dynare Team
 #
 # This file is part of Dynare.
 #
@@ -80,7 +80,9 @@ class DynObject(ObjectDescription):
         signode += addnodes.desc_name(name, name)
 
         if self.has_arguments:
-            if arglist:
+            if not arglist:
+                signode += addnodes.desc_parameterlist()
+            else:
                 signode += addnodes.desc_addname(arglist,arglist)
         return fullname, prefix
 

doc/manual/utils/dynare_lex.py

@@ -60,7 +60,7 @@ class DynareLexer(RegexLexer):
         "addSeries","addParagraph","addVspace","write","compile")
 
     operators = (
-        "STEADY_STATE","EXPECTATION","var_expectation","pac_expectation","pac_target_nonstationary")
+        "STEADY_STATE","EXPECTATION","var_expectation","pac_expectation")
 
     macro_dirs = (
         "@#includepath", "@#include", "@#define", "@#if",
@@ -83,8 +83,7 @@ class DynareLexer(RegexLexer):
                 'osr_params_bounds','ramsey_constraints','irf_calibration',
                 'moment_calibration','identification','svar_identification',
                 'matched_moments','occbin_constraints','surprise','overwrite','bind','relax',
-                'verbatim','end','node','cluster','paths','hooks','target','pac_target_info','auxname_target_nonstationary',
+                'verbatim','end','node','cluster','paths','hooks'), prefix=r'\b', suffix=r'\s*\b'),Keyword.Reserved),
-                'component', 'growth', 'auxname', 'kind'), prefix=r'\b', suffix=r'\s*\b'),Keyword.Reserved),
 
             # FIXME: Commands following multiline comments are not highlighted properly.
             (words(commands + report_commands,

examples/pacmodel.mod

@@ -1,101 +0,0 @@
-// --+ options: json=compute, stochastic +--
-
-var y x z v;
-
-varexo ex ey ez ;
-
-parameters a_y_1 a_y_2 b_y_1 b_y_2 b_x_1 b_x_2 d_y; // VAR parameters
-
-parameters beta e_c_m c_z_1 c_z_2;               // PAC equation parameters
-
-a_y_1 =  .2;
-a_y_2 =  .3;
-b_y_1 =  .1;
-b_y_2 =  .4;
-b_x_1 = -.1;
-b_x_2 = -.2;
-d_y = .5;
-
-
-beta  =  .9;
-e_c_m =  .1;
-c_z_1 =  .7;
-c_z_2 = -.3;
-
-var_model(model_name=toto, eqtags=['eq:x', 'eq:y']);
-
-pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
-
-pac_target_info(pacman);
-  target v;
-  auxname_target_nonstationary vns;
-
-  component y;
-  auxname pv_y_;
-  kind ll;
-
-  component x;
-  growth diff(x(-1));
-  auxname pv_dx_;
-  kind dd;
-
-end;
-
-model;
-
-  [name='eq:y']
-  y = a_y_1*y(-1) + a_y_2*diff(x(-1)) + b_y_1*y(-2) + b_y_2*diff(x(-2)) + ey ;
-
-
-  [name='eq:x']
-  diff(x) = b_x_1*y(-2) + b_x_2*diff(x(-1)) + ex ;
-
-  [name='eq:v']
-  v = x + d_y*y ;  // Composite target, no residuals here only variables defined in the auxiliary VAR model.
-
-  [name='zpac']
-  diff(z) = e_c_m*(pac_target_nonstationary(pacman)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
-
-end;
-
-shocks;
-  var ex = .10;
-  var ey = .15;
-  var ez = .05;
-end;
-
-// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
-pac.initialize('pacman');
-
-// Update the parameters of the PAC expectation model (h0 and h1 vectors).
-pac.update.expectation('pacman');
-
-/*
-**
-** Simulate artificial dataset
-**
-*/
-
-// Set initial conditions to zero.
-initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
-
-// Simulate the model for 5000 periods
-TrueData = simul_backward_model(initialconditions, 5000);
-
-/*
-**
-** Estimate PAC equation (using the artificial data)
-**
-*/
-
-
-// Provide initial conditions for the estimated parameters
-clear eparams
-eparams.e_c_m   =  .9;
-eparams.c_z_1   =  .5;
-eparams.c_z_2   =  .2;
-
-edata = TrueData;                // Set the dataset used for estimation
-edata.ez = dseries(NaN, 2000Q1); // Remove residuals for the PAC equation from the database.
-
-pac.estimate.nls('zpac', eparams, edata, 2005Q1:2005Q1+4000, 'fmincon'); // Should produce a table with the estimates (close to the calibration given in lines 21-23)

license.txt

@@ -86,7 +86,7 @@ License: public-domain-aim
  Journal of Economic Dynamics and Control, 2010, vol. 34, issue 3,
  pages 472-489
 
-Files: matlab/optimization/bfgsi1.m matlab/optimization/csolve.m matlab/optimization/csminit1.m matlab/optimization/numgrad2.m
+Files: matlab/optimization/bfgsi1.m matlab/csolve.m matlab/optimization/csminit1.m matlab/optimization/numgrad2.m
  matlab/optimization/numgrad3.m matlab/optimization/numgrad3_.m matlab/optimization/numgrad5.m
  matlab/optimization/numgrad5_.m matlab/optimization/csminwel1.m matlab/+bvar/density.m
  matlab/+bvar/toolbox.m matlab/partial_information/PI_gensys.m matlab/partial_information/qzswitch.m
@@ -123,11 +123,6 @@ Copyright: 2010-2015 Alexander Meyer-Gohde
            2015-2017 Dynare Team
 License: GPL-3+
 
-Files: matlab/collapse_figures_in_tabgroup.m
-Copyright: 2023 Eduard Benet Cerda
-           2024 Dynare Team
-License: GPL-3+
-
 Files: matlab/convergence_diagnostics/raftery_lewis.m
 Copyright: 2016 Benjamin Born and Johannes Pfeifer
            2016-2017 Dynare Team
@@ -177,7 +172,7 @@ Comment: Written by Jessica Cariboni and Francesca Campolongo
 Files: matlab/+gsa/cumplot.m
        matlab/+gsa/monte_carlo_filtering.m
        matlab/+gsa/skewness.m
-       matlab/+gsa/log_transform.m
+       matlab/+gsa/log_trans_.m
        matlab/+gsa/map_calibration.m
        matlab/+gsa/map_identification.m
        matlab/+gsa/monte_carlo_filtering_analysis.m
@@ -252,7 +247,7 @@ License: BSD-2-clause
 
 Files: examples/fs2000_data.m
 Copyright: 2000-2022 Frank Schorfheide
-           2023 Dynare Team
+Copyright: 2023 Dynare Team
 License: CC-BY-SA-4.0
 
 Files: doc/*.rst doc/*.tex doc/*.svg doc/*.pdf doc/*.bib
@@ -297,6 +292,28 @@ Files: preprocessor/doc/preprocessor/*
 Copyright: 2007-2019 Dynare Team
 License: CC-BY-SA-4.0
 
+Files: contrib/jsonlab/*
+Copyright: 2011-2020 Qianqian Fang <q.fang at neu.edu>
+           2016 Bastian Bechtold
+License: GPL-3+ or BSD-3-clause
+
+Files: contrib/jsonlab/base64decode.m
+       contrib/jsonlab/base64encode.m
+       contrib/jsonlab/gzipdecode.m
+       contrib/jsonlab/gzipencode.m
+       contrib/jsonlab/zlibdecode.m
+       contrib/jsonlab/zlibencode.m
+Copyright: 2012 Kota Yamaguchi
+           2011-2020 Qianqian Fang <q.fang at neu.edu>
+License: GPL-3+ or BSD-2-clause
+
+Files: contrib/jsonlab/loadjson.m
+Copyright: 2011-2020 Qianqian Fang
+           2009 Nedialko Krouchev
+           2009 François Glineur
+           2008 Joel Feenstra
+License: GPL-3+ or BSD-2-clause or BSD-3-clause
+
 Files: contrib/ms-sbvar/utilities_dw/*
 Copyright: 1996-2011 Daniel Waggoner
 License: GPL-3+
@@ -403,6 +420,32 @@ License: BSD-2-clause
  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
  POSSIBILITY OF SUCH DAMAGE.
 
+License: BSD-3-clause
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+ .
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ .
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ .
+ * Neither the name of the copyright holder nor the names of its contributors
+   may be used to endorse or promote products derived from this software without
+   specific prior written permission.
+ .
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
+ FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
 License: GFDL-NIV-1.3+
  Permission is granted to copy, distribute and/or modify this document
  under the terms of the GNU Free Documentation License, Version 1.3 or

macOS/build.sh

@@ -1,6 +1,6 @@
 #!/usr/bin/env bash
 
-# Copyright © 2019-2024 Dynare Team
+# Copyright © 2019-2023 Dynare Team
 #
 # This file is part of Dynare.
 #
@@ -150,8 +150,8 @@ cp     "$ROOTDIR"/build-doc/*.pdf                                    "$PKGFILES"
 cp     "$ROOTDIR"/build-doc/preprocessor/doc/*.pdf                   "$PKGFILES"/doc
 cp -r  "$ROOTDIR"/build-doc/dynare-manual.html                       "$PKGFILES"/doc
 
-mkdir -p                                                             "$PKGFILES"/matlab/dseries/externals/x13/macOS/64
+mkdir -p                                                             "$PKGFILES"/matlab/modules/dseries/externals/x13/macOS/64
-cp -p  "$ROOTDIR"/macOS/deps/"$PKG_ARCH"/lib64/x13as/x13as           "$PKGFILES"/matlab/dseries/externals/x13/macOS/64
+cp -p  "$ROOTDIR"/macOS/deps/"$PKG_ARCH"/lib64/x13as/x13as           "$PKGFILES"/matlab/modules/dseries/externals/x13/macOS/64
 
 
 cd "$ROOTDIR"/macOS/pkg

macOS/deps/Makefile

@@ -1,4 +1,4 @@
-# Copyright © 2019-2024 Dynare Team
+# Copyright © 2019-2023 Dynare Team
 #
 # This file is part of Dynare.
 #
@@ -53,7 +53,7 @@ clean-all: clean-lib clean-src clean-tar
 #
 tarballs/slicot-$(SLICOT_VERSION).tar.gz:
 	mkdir -p tarballs
-	wget $(WGET_OPTIONS) -O $@ https://deb.debian.org/debian/pool/main/s/slicot/slicot_$(SLICOT_VERSION).orig.tar.xz
+	wget $(WGET_OPTIONS) -O $@ https://deb.debian.org/debian/pool/main/s/slicot/slicot_$(SLICOT_VERSION).orig.tar.gz
 
 $(DEPS_ARCH)/sources64/slicot-$(SLICOT_VERSION): tarballs/slicot-$(SLICOT_VERSION).tar.gz
 	rm -rf $(DEPS_ARCH)/sources64/slicot-*
@@ -62,7 +62,7 @@ $(DEPS_ARCH)/sources64/slicot-$(SLICOT_VERSION): tarballs/slicot-$(SLICOT_VERSIO
 	touch $@
 
 $(DEPS_ARCH)/lib64/slicot/libslicot64_pic.a: $(DEPS_ARCH)/sources64/slicot-$(SLICOT_VERSION)
-	make -C $< -f makefile_Unix FORTRAN=$(BREWDIR)/bin/gfortran LOADER=$(BREWDIR)/bin/gfortran SLICOTLIB=../libslicot64_pic.a OPTS="-O3 -fdefault-integer-8" lib -j$(NTHREADS)
+	make -C $< FORTRAN=$(BREWDIR)/bin/gfortran LOADER=$(BREWDIR)/bin/gfortran SLICOTLIB=../libslicot64_pic.a OPTS="-O3 -fdefault-integer-8" lib -j$(NTHREADS)
 	strip -S $</libslicot64_pic.a
 	mkdir -p $(dir $@)
 	cp $</libslicot64_pic.a $@

macOS/deps/versions.mk

@@ -1,2 +1,2 @@
-SLICOT_VERSION = 5.9~20240205.gita037f7e
+SLICOT_VERSION = 5.0+20101122
 X13AS_VERSION = 1-1-b60

matlab/+gsa/map_identification.m

@@ -272,8 +272,80 @@ elseif opt_gsa.morris==3
     return
 elseif opt_gsa.morris==2   % ISKREV stuff
     return
-else  
+else  % main effects analysis
-    error('gsa/map_identification: unsupported option morris=%u',opt_gsa.morris)
+    if itrans==0
+        fsuffix = '';
+    elseif itrans==1
+        fsuffix = '_log';
+    else
+        fsuffix = '_rank';
+    end
+
+    imap=1:npT;
+
+    if isempty(lpmat0)
+        x0=lpmat(istable,:);
+    else
+        x0=[lpmat0(istable,:), lpmat(istable,:)];
+    end
+    nrun=length(istable);
+    nest=min(250,nrun);
+
+    if opt_gsa.load_ident_files==0
+        try
+            EET=load([OutputDirectoryName,'/SCREEN/',fname_,'_morris_IDE'],'SAcc','ir_cc','ic_cc');
+        catch
+            EET=[];
+        end
+        ccac = gsa.standardize_columns([mss cc ac]);
+        [pcc, dd] = eig(cov(ccac(istable,:)));
+        [latent, isort] = sort(-diag(dd));
+        latent = -latent;
+        figure, bar(latent)
+        title('Eigenvalues in PCA')
+        pcc=pcc(:,isort);
+        ccac = ccac*pcc;
+        npca = max(find(cumsum(latent)./length(latent)<0.99))+1;
+        siPCA = (EET.SAcc'*abs(pcc'))';
+        siPCA = siPCA./(max(siPCA,[],2)*ones(1,npT));
+        SAcc=zeros(size(ccac,2),npT);
+        gsa_=NaN(npca);
+        for j=1:npca %size(ccac,2),
+            if itrans==0
+                y0 = ccac(istable,j);
+            elseif itrans==1
+                y0 = gsa.log_transform(ccac(istable,j));
+            else
+                y0 = trank(ccac(istable,j));
+            end
+            if ~isempty(EET)
+                imap=find(siPCA(j,:) >= (0.1.*max(siPCA(j,:))) );
+            end
+            gsa_(j) = gsa_sdp(y0(1:nest), x0(1:nest,imap), ...
+                              2, [],[],[],0,[OutputDirectoryName,'/map_cc',fsuffix,int2str(j)], pnames);
+            SAcc(j,imap)=gsa_(j).si;
+            imap_cc{j}=imap;
+        end
+        save([OutputDirectoryName,'/map_cc',fsuffix,'.mat'],'gsa_')
+        save([OutputDirectoryName,'/',fname_,'_main_eff.mat'],'imap_cc','SAcc','ccac','-append')
+    else
+        load([OutputDirectoryName,'/',fname_,'_main_eff'],'SAcc')
+    end
+
+    hh_fig=dyn_figure(options_.nodisplay,'Name',['Identifiability indices in the ',fsuffix,' moments.']);
+    bar(sum(SAcc))
+    set(gca,'xticklabel',' ','fontsize',10,'xtick',1:npT)
+    set(gca,'xlim',[0.5 npT+0.5])
+    ydum = get(gca,'ylim');
+    set(gca,'ylim',[0 ydum(2)])
+    set(gca,'position',[0.13 0.2 0.775 0.7])
+    for ip=1:npT
+        text(ip,-0.02*(ydum(2)),bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
+    end
+    xlabel(' ')
+    title(['Identifiability indices in the ',fsuffix,' moments.'],'interpreter','none')
+    dyn_saveas(hh_fig,[OutputDirectoryName,'/',fname_,'_ident_ALL',fsuffix],options_.nodisplay,options_.graph_format);
+    create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_ident_ALL',fsuffix],1,['Identifiability indices in the ',fsuffix,' moments.'],['ident_ALL',fsuffix]',1)
 end
 
 function []=create_TeX_loader(options_,figpath,ifig_number,caption,label_name,scale_factor)

matlab/+gsa/run.m

@@ -13,7 +13,7 @@ function x0=run(M_,oo_,options_,bayestopt_,estim_params_,options_gsa)
 % M. Ratto (2008), Analysing DSGE Models with Global Sensitivity Analysis, 
 % Computational Economics (2008), 31, pp. 115–139
 
-% Copyright © 2008-2024 Dynare Team
+% Copyright © 2008-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -101,9 +101,6 @@ if ~isempty(options_gsa.datafile) || isempty(bayestopt_) || options_gsa.rmse
         disp('must be specified for RMSE analysis!');
         error('Sensitivity anaysis error!')
     end
-    if isfield(options_gsa,'nobs')
-        options_.nobs=options_gsa.nobs;
-    end
     if ~isempty(options_.nobs) && length(options_.nobs)~=1
         error('dynare_sensitivity does not support recursive estimation. Please specify nobs as a scalar, not a vector.')
     end
@@ -111,6 +108,9 @@ if ~isempty(options_gsa.datafile) || isempty(bayestopt_) || options_gsa.rmse
     if isfield(options_gsa,'first_obs')
         options_.first_obs=options_gsa.first_obs;
     end
+    if isfield(options_gsa,'nobs')
+        options_.nobs=options_gsa.nobs;
+    end
     if isfield(options_gsa,'presample')
         options_.presample=options_gsa.presample;
     end
@@ -150,11 +150,6 @@ end
 
 [~,~,~,~,oo_.dr,M_.params] = dynare_resolve(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
 
-if isfield(oo_.dr,'eigval') && any(abs(oo_.dr.eigval-1)<abs(1-options_.qz_criterium)) && options_.qz_criterium<1
-    fprintf('\ngsa: The model features a unit root, but qz_criterium<1. Check whether that is intended.')
-    fprintf('\ngsa: If not, use the diffuse_filter option.\n')
-end
-
 options_gsa = set_default_option(options_gsa,'identification',0);
 if options_gsa.identification
     options_gsa.redform=0;
@@ -527,4 +522,4 @@ if options_gsa.glue
             save([OutputDirectoryName,'/',fname_,'_glue_mc.mat'], 'Out', 'Sam', 'Lik', 'Obs', 'Rem','Info', 'Exo')
         end
     end
-end
+end

matlab/+identification/get_perturbation_params_derivs.m

@@ -93,8 +93,7 @@ function DERIVS = get_perturbation_params_derivs(M_, options_, estim_params_, dr
 % This function calls
 %   * [fname,'.dynamic']
 %   * [fname,'.dynamic_params_derivs']
-%   * [fname,'.sparse.static_g1']
+%   * [fname,'.static']
-%   * [fname,'.sparse.static_g2']
 %   * [fname,'.static_params_derivs']
 %   * commutation
 %   * dyn_vech
@@ -110,7 +109,7 @@ function DERIVS = get_perturbation_params_derivs(M_, options_, estim_params_, dr
 %   * sylvester3a
 %   * get_perturbation_params_derivs_numerical_objective
 % =========================================================================
-% Copyright © 2019-2024 Dynare Team
+% Copyright © 2019-2020 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -455,13 +454,12 @@ elseif (analytic_derivation_mode == 0 || analytic_derivation_mode == 1)
         error('For analytical parameter derivatives ''dynamic_params_derivs.m'' file is needed, this can be created by putting identification(order=%d) into your mod file.',order)
     end
     %% Analytical computation of Jacobian and Hessian (wrt selected model parameters) of steady state, i.e. dYss and d2Yss
-    [g1_static, T_order_static, T_static] = feval([fname,'.sparse.static_g1'], ys, exo_steady_state', params, M_.static_g1_sparse_rowval, M_.static_g1_sparse_colval, M_.static_g1_sparse_colptr); %g1_static is [endo_nbr by endo_nbr] first-derivative (wrt all endogenous variables) of static model equations f, i.e. df/dys, in declaration order
+    [~, g1_static] = feval([fname,'.static'], ys, exo_steady_state', params); %g1_static is [endo_nbr by endo_nbr] first-derivative (wrt all endogenous variables) of static model equations f, i.e. df/dys, in declaration order
     rp_static = feval([fname,'.static_params_derivs'], ys, exo_steady_state', params); %rp_static is [endo_nbr by param_nbr] first-derivative (wrt all model parameters) of static model equations f, i.e. df/dparams, in declaration order
     dys = -g1_static\rp_static; %use implicit function theorem (equation 5 of Ratto and Iskrev (2012) to compute [endo_nbr by param_nbr] first-derivative (wrt all model parameters) of steady state for all endogenous variables analytically, note that dys is in declaration order
     d2ys = zeros(endo_nbr, param_nbr, param_nbr); %initialize in tensor notation, note that d2ys is only needed for d2flag, i.e. for g1pp
     if d2flag
-        g2_static_v = feval([fname,'.sparse.static_g2'], ys, exo_steady_state', params, T_order_static, T_static);
+        [~, ~, g2_static] = feval([fname,'.static'], ys, exo_steady_state', params); %g2_static is [endo_nbr by endo_nbr^2] second derivative (wrt all endogenous variables) of static model equations f, i.e. d(df/dys)/dys, in declaration order
-        g2_static = build_two_dim_hessian(M_.static_g2_sparse_indices, g2_static_v, endo_nbr, endo_nbr); %g2_static is [endo_nbr by endo_nbr^2] second derivative (wrt all endogenous variables) of static model equations f, i.e. d(df/dys)/dys, in declaration order
         if order < 3
             [~, g1, g2, g3] = feval([fname,'.dynamic'], ys(I), exo_steady_state', params, ys, 1); %note that g3 does not contain symmetric elements
             g3 = identification.unfold_g3(g3, yy0ex0_nbr); %add symmetric elements to g3
@@ -507,7 +505,7 @@ elseif (analytic_derivation_mode == 0 || analytic_derivation_mode == 1)
     end
     %handling of steady state for nonstationary variables
     if any(any(isnan(dys)))
-        [U,T] = schur(full(g1_static));
+        [U,T] = schur(g1_static);
         e1 = abs(ordeig(T)) < qz_criterium-1;
         k = sum(e1);       % Number of non stationary variables.
                            % Number of stationary variables: n = length(e1)-k

matlab/+identification/run.m

@@ -736,7 +736,7 @@ if iload <=0
                 end
                 run_index = 0; % reset index
             end
-            if SampleSize > 1 && mod(iteration,3)
+            if SampleSize > 1
                 dyn_waitbar(iteration/SampleSize, h, ['MC identification checks ', int2str(iteration), '/', int2str(SampleSize)]);
             end
         end

matlab/+identification/simulated_moment_uncertainty.m

@@ -12,7 +12,7 @@ function [cmm, mm] = simulated_moment_uncertainty(indx, periods, replic,options_
 %   - cmm:      [n_moments by n_moments] covariance matrix of simulated moments
 %   - mm:       [n_moments by replic] matrix of moments
 
-% Copyright © 2009-2024 Dynare Team
+% Copyright © 2009-2018 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -60,10 +60,15 @@ end
 
 oo_.dr=set_state_space(oo_.dr,M_);
 
+
 if options_.logged_steady_state %if steady state was previously logged, undo this
     oo_.dr.ys=exp(oo_.dr.ys);
     oo_.steady_state=exp(oo_.steady_state);
     options_.logged_steady_state=0;
+    logged_steady_state_indicator=1;
+    evalin('base','options_.logged_steady_state=0;')
+else
+    logged_steady_state_indicator=0;
 end
 
 [oo_.dr,info,M_.params] = compute_decision_rules(M_,options_,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
@@ -87,6 +92,7 @@ else
     end
 end
 
+
 for j=1:replic
     [ys, oo_.exo_simul] = simult(y0,oo_.dr,M_,options_);%do simulation
     oo_=disp_moments(ys, options_.varobs,M_,options_,oo_); %get moments
@@ -100,5 +106,8 @@ for j=1:replic
 end
 dyn_waitbar_close(h);
 
+if logged_steady_state_indicator
+    evalin('base','options_.logged_steady_state=1;') %reset base workspace option to conform to base oo_
+end
 cmm = cov(mm');
 disp('Simulated moment uncertainty ... done!')

matlab/+occbin/DSGE_smoother.m

@@ -479,7 +479,7 @@ if (~is_changed || occbin_smoother_debug) && nargin==12
                         fprintf(fidTeX,'\\begin{figure}[H]\n');
                         fprintf(fidTeX,'\\centering \n');
                         fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_smoothedshocks_occbin%s}\n',options_.figures.textwidth*min(j1/3,1),[GraphDirectoryName '/' M_.fname],int2str(ifig)); % don't use filesep as it will create issues with LaTeX on Windows
-                        fprintf(fidTeX,'\\caption{OccBin smoothed shocks.}');
+                        fprintf(fidTeX,'\\caption{Check plots.}');
                         fprintf(fidTeX,'\\label{Fig:smoothedshocks_occbin:%s}\n',int2str(ifig));
                         fprintf(fidTeX,'\\end{figure}\n');
                         fprintf(fidTeX,' \n');
@@ -488,7 +488,7 @@ if (~is_changed || occbin_smoother_debug) && nargin==12
             end
         end
 
-        if mod(j1,9)~=0 && j==M_.exo_nbr
+            if mod(j1,9)~=0 && j==M_.exo_nbr
                 annotation('textbox', [0.1,0,0.35,0.05],'String', 'Linear','Color','Blue','horizontalalignment','center','interpreter','none');
                 annotation('textbox', [0.55,0,0.35,0.05],'String', 'Piecewise','Color','Red','horizontalalignment','center','interpreter','none');
                 dyn_saveas(hh_fig,[GraphDirectoryName filesep M_.fname,'_smoothedshocks_occbin',int2str(ifig)],options_.nodisplay,options_.graph_format);
@@ -497,7 +497,7 @@ if (~is_changed || occbin_smoother_debug) && nargin==12
                 fprintf(fidTeX,'\\begin{figure}[H]\n');
                 fprintf(fidTeX,'\\centering \n');
                 fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_smoothedshocks_occbin%s}\n',options_.figures.textwidth*min(j1/3,1),[GraphDirectoryName '/' M_.fname],int2str(ifig)); % don't use filesep as it will create issues with LaTeX on Windows
-                fprintf(fidTeX,'\\caption{OccBin smoothed shocks.}');
+                fprintf(fidTeX,'\\caption{Check plots.}');
                 fprintf(fidTeX,'\\label{Fig:smoothedshocks_occbin:%s}\n',int2str(ifig));
                 fprintf(fidTeX,'\\end{figure}\n');
                 fprintf(fidTeX,' \n');
@@ -505,6 +505,6 @@ if (~is_changed || occbin_smoother_debug) && nargin==12
         end
         if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
             fclose(fidTeX);
-        end
     end
 end
+end

matlab/+occbin/forecast.m

@@ -1,169 +1,143 @@
-function [forecast, error_flag] = forecast(options_,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,forecast_horizon)
+function [oo_, error_flag] = forecast(options_,M_,oo_,forecast) %,hist_period)
-% [forecast, error_flag] = forecast(options_,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,forecast_horizon)
+%function oo_ = forecast(options_,M_,oo_,forecast)
-% Occbin forecasts
+% forecast
-%
-% INPUTS
-% - options_                [structure]     Matlab's structure describing the current options
-% - M_                      [structure]     Matlab's structure describing the model
-% - dr_in                   [structure]     model information structure
-% - endo_steady_state       [double]        steady state value for endogenous variables
-% - exo_steady_state        [double]        steady state value for exogenous variables
-% - exo_det_steady_state    [double]        steady state value for exogenous deterministic variables                                    
-% - forecast_horizon        [integer]       forecast horizon
-%
-% OUTPUTS
-% - forecast                [structure]     forecast results
-% - error_flag              [integer]       error code
-%
-% SPECIAL REQUIREMENTS
-%   none.
-
-% Copyright © 2022-2023 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
 opts = options_.occbin.forecast;
 
 options_.occbin.simul.maxit = opts.maxit;
 options_.occbin.simul.check_ahead_periods = opts.check_ahead_periods;
-options_.occbin.simul.periods = forecast_horizon;
+options_.occbin.simul.periods = forecast;
-shocks_input = opts.SHOCKS0;
+SHOCKS0 = opts.SHOCKS0;
-
+if ~isempty(SHOCKS0)
-if ~isempty(shocks_input)
+    if iscell(SHOCKS0)
-    n_shocks=size(shocks_input,1);
+        for j=1:length(SHOCKS0)
-    if iscell(shocks_input)
+            sname = SHOCKS0{j}{1};
-        inds=NaN(n_shocks,1);
+            inds(j)=strmatch(sname,M_.exo_names);
-        periods=length(shocks_input{1}{2});
+            SHOCKS1(j,:)=SHOCKS0{j}{2};
-        shock_mat=NaN(n_shocks,periods);
-        for j=1:n_shocks
-            exo_pos=strmatch(shocks_input{j}{1},M_.exo_names,'exact');
-            if isempty(exo_pos)
-        	    error('occbin.forecast: unknown exogenous shock %s',shocks_input{j}{1})
-            else
-                inds(j)=exo_pos;
-            end
-            if length(shocks_input{j}{2})~=periods
-        	    error('occbin.forecast: unknown exogenous shock %s',shocks_input{j}{1})
-            else
-                shock_mat(j,:)=shocks_input{j}{2};
-            end
         end
-    elseif isreal(shocks_input)
+    elseif isreal(SHOCKS0)
-        shock_mat=shocks_input;
+        SHOCKS1=SHOCKS0;
-        inds = (1:M_.exo_nbr)';
+        inds = 1:M_.exo_nbr;
     end
 end
 
-options_.occbin.simul.endo_init = M_.endo_histval(:,1)-endo_steady_state; %initial condition
-options_.occbin.simul.init_regime = opts.frcst_regimes;
-options_.occbin.simul.init_binding_indicator = [];
-
-shocks_base = zeros(forecast_horizon,M_.exo_nbr);
-if ~isempty(shocks_input)
-    for j=1:n_shocks
-        shocks_base(:,inds(j))=shock_mat(j,:);
-    end
-end
 
 if opts.replic
     h = dyn_waitbar(0,'Please wait occbin forecast replic ...');
     ishock = find(sqrt(diag((M_.Sigma_e))));
-    options_.occbin.simul.exo_pos=ishock;
     effective_exo_nbr=  length(ishock);
-    effective_Sigma_e = M_.Sigma_e(ishock,ishock);  % does not take heteroskedastic shocks into account
+    effective_exo_names = M_.exo_names(ishock);
+    effective_Sigma_e = M_.Sigma_e(ishock,ishock);
     [U,S] = svd(effective_Sigma_e);
-    % draw random shocks
     if opts.qmc
         opts.replic =2^(round(log2(opts.replic+1)))-1;
-        SHOCKS_add = qmc_sequence(forecast_horizon*effective_exo_nbr, int64(1), 1, opts.replic);
+        SHOCKS_ant =   qmc_sequence(forecast*effective_exo_nbr, int64(1), 1, opts.replic)';
     else
-        SHOCKS_add = randn(forecast_horizon*effective_exo_nbr,opts.replic);
+        SHOCKS_ant = randn(forecast*effective_exo_nbr,opts.replic)';
     end
-    SHOCKS_add=reshape(SHOCKS_add,effective_exo_nbr,forecast_horizon,opts.replic);
+    zlin0=zeros(forecast,M_.endo_nbr,opts.replic);
-    z.linear=NaN(forecast_horizon,M_.endo_nbr,opts.replic);
+    zpiece0=zeros(forecast,M_.endo_nbr,opts.replic);
-    z.piecewise=NaN(forecast_horizon,M_.endo_nbr,opts.replic);
-    error_flag=true(opts.replic,1);
-    simul_SHOCKS=NaN(forecast_horizon,M_.exo_nbr,opts.replic);
     for iter=1:opts.replic
-        options_.occbin.simul.SHOCKS = shocks_base+transpose(U*sqrt(S)*SHOCKS_add(:,:,iter));
+
-        options_.occbin.simul.waitbar=0;
+        if ~isempty(SHOCKS0)
-        [~, out] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
+            for j=1:length(SHOCKS0)
-        error_flag(iter)=out.error_flag;
+                SHOCKS(:,inds(j))=SHOCKS1(j,:);
-        if ~error_flag(iter)
-            z.linear(:,:,iter)=out.linear;
-            z.piecewise(:,:,iter)=out.piecewise;
-            frcst_regime_history(iter,:)=out.regime_history;
-            error_flag(iter)=out.error_flag;
-            simul_SHOCKS(:,:,iter) = shocks_base;
-        else
-            if options_.debug
-                save('Occbin_forecast_debug','simul_SHOCKS','z','iter','frcst_regime_history','error_flag','out','shocks_base')
             end
         end
+
+        error_flagx=1;
+        % while error_flagx,
+        % SHOCKS=transpose(sqrt(diag(diag(effective_Sigma_e)))*(reshape(SHOCKS_ant(iter,:),forecast,effective_exo_nbr))');
+        SHOCKS=transpose(U*sqrt(S)*(reshape(SHOCKS_ant(iter,:),forecast,effective_exo_nbr))');
+        %             SHOCKS=transpose(U*sqrt(S)*randn(forecast,M_.exo_nbr)');   %realized shocks
+        options_.occbin.simul.endo_init = M_.endo_histval(:,1)-oo_.steady_state;
+        options_.occbin.simul.init_regime = opts.frcst_regimes;
+        options_.occbin.simul.init_binding_indicator = [];
+        options_.occbin.simul.exo_pos=ishock;
+        options_.occbin.simul.SHOCKS = SHOCKS;
+        options_.occbin.simul.waitbar=0;
+        [~, out] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
+        zlin0(:,:,iter)=out.linear;
+        zpiece0(:,:,iter)=out.piecewise;
+        ys=out.ys;
+        frcst_regime_history(iter,:)=out.regime_history;
+        error_flag(iter)=out.error_flag;
+        error_flagx = error_flag(iter);
+        % end
+        simul_SHOCKS(:,:,iter) = SHOCKS;
+
+        if error_flag(iter) && debug_flag
+            %             display('no solution')
+
+%             keyboard;
+            save no_solution SHOCKS zlin0 zpiece0 iter frcst_regime_history
+        end
         dyn_waitbar(iter/opts.replic,h,['OccBin MC forecast replic ',int2str(iter),'/',int2str(opts.replic)])
     end
     dyn_waitbar_close(h);
-    if options_.debug
+    save temp zlin0 zpiece0 simul_SHOCKS error_flag
-         save('Occbin_forecast_debug','simul_SHOCKS','z','iter','frcst_regime_history','error_flag')
-    end
     inx=find(error_flag==0);
-    z.linear=z.linear(:,:,inx);
+    zlin0=zlin0(:,:,inx);
-    z.piecewise=z.piecewise(:,:,inx);
+    zpiece0=zpiece0(:,:,inx);
-    z.min.piecewise = min(z.piecewise,[],3);
+    zlin   = mean(zlin0,3);
-    z.max.piecewise = max(z.piecewise,[],3);
+    zpiece = mean(zpiece0,3);
-    z.min.linear = min(z.linear,[],3);
+    zpiecemin = min(zpiece0,[],3);
-    z.max.linear = max(z.linear,[],3);
+    zpiecemax = max(zpiece0,[],3);
-    
+    zlinmin = min(zlin0,[],3);
-    field_names={'linear','piecewise'};
+    zlinmax = max(zlin0,[],3);
-    post_mean=NaN(forecast_horizon,1);
+
-    post_median=NaN(forecast_horizon,1);
+    for i=1:M_.endo_nbr
-    post_var=NaN(forecast_horizon,1);
+        for j=1:forecast
-    hpd_interval=NaN(forecast_horizon,2);
+            [post_mean(j,1), post_median(j,1), post_var(j,1), hpd_interval(j,:), post_deciles(j,:)] = posterior_moments(squeeze(zlin0(j,i,:)),options_.forecasts.conf_sig);
-    post_deciles=NaN(forecast_horizon,9);
-    for field_iter=1:2
-        for i=1:M_.endo_nbr
-            for j=1:forecast_horizon
-                [post_mean(j,1), post_median(j,1), post_var(j,1), hpd_interval(j,:), post_deciles(j,:)] = posterior_moments(squeeze(z.(field_names{field_iter})(j,i,:)),options_.forecasts.conf_sig);
-            end
-            forecast.(field_names{field_iter}).Mean.(M_.endo_names{i})=post_mean;
-            forecast.(field_names{field_iter}).Median.(M_.endo_names{i})=post_median;
-            forecast.(field_names{field_iter}).Var.(M_.endo_names{i})=post_var;
-            forecast.(field_names{field_iter}).HPDinf.(M_.endo_names{i})=hpd_interval(:,1);
-            forecast.(field_names{field_iter}).HPDsup.(M_.endo_names{i})=hpd_interval(:,2);
-            forecast.(field_names{field_iter}).Deciles.(M_.endo_names{i})=post_deciles;
-            forecast.(field_names{field_iter}).Min.(M_.endo_names{i})=z.min.(field_names{field_iter})(:,i);
-            forecast.(field_names{field_iter}).Max.(M_.endo_names{i})=z.max.(field_names{field_iter})(:,i);
         end
-    end
+        oo_.occbin.linear_forecast.Mean.(M_.endo_names{i})=post_mean;
-else
+        oo_.occbin.linear_forecast.Median.(M_.endo_names{i})=post_median;
-    options_.occbin.simul.irfshock = M_.exo_names;
+        oo_.occbin.linear_forecast.Var.(M_.endo_names{i})=post_var;
-    options_.occbin.simul.SHOCKS = shocks_base;
+        oo_.occbin.linear_forecast.HPDinf.(M_.endo_names{i})=hpd_interval(:,1);
-    [~, out] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
+        oo_.occbin.linear_forecast.HPDsup.(M_.endo_names{i})=hpd_interval(:,2);
-    error_flag=out.error_flag;
+        oo_.occbin.linear_forecast.Deciles.(M_.endo_names{i})=post_deciles;
-    if out.error_flag
+        oo_.occbin.linear_forecast.Min.(M_.endo_names{i})=zlinmin(:,i);
-        fprintf('occbin.forecast: forecast simulation failed.')
+        oo_.occbin.linear_forecast.Max.(M_.endo_names{i})=zlinmax(:,i);
-        return;
+        for j=1:forecast
+            [post_mean(j,1), post_median(j,1), post_var(j,1), hpd_interval(j,:), post_deciles(j,:)] = posterior_moments(squeeze(zpiece0(j,i,:)),options_.forecasts.conf_sig);
+        end
+        oo_.occbin.forecast.Mean.(M_.endo_names{i})=post_mean;
+        oo_.occbin.forecast.Median.(M_.endo_names{i})=post_median;
+        oo_.occbin.forecast.Var.(M_.endo_names{i})=post_var;
+        oo_.occbin.forecast.HPDinf.(M_.endo_names{i})=hpd_interval(:,1);
+        oo_.occbin.forecast.HPDsup.(M_.endo_names{i})=hpd_interval(:,2);
+        oo_.occbin.forecast.Deciles.(M_.endo_names{i})=post_deciles;
+        oo_.occbin.forecast.Min.(M_.endo_names{i})=zpiecemin(:,i);
+        oo_.occbin.forecast.Max.(M_.endo_names{i})=zpiecemax(:,i);
+        %   eval([M_.endo_names{i},'_ss=zdatass(i);']);
     end
 
+else
+    SHOCKS = zeros(forecast,M_.exo_nbr);
+    if ~isempty(SHOCKS0)
+        for j=1:length(SHOCKS0)
+            SHOCKS(:,inds(j))=SHOCKS1(j,:);
+        end
+    end
+    options_.occbin.simul.endo_init = M_.endo_histval(:,1)-oo_.steady_state;
+    options_.occbin.simul.init_regime = opts.frcst_regimes;
+    options_.occbin.simul.init_violvecbool = [];
+    options_.occbin.simul.irfshock = M_.exo_names;
+    options_.occbin.simul.SHOCKS = SHOCKS;
+    [~, out] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
+
+    zlin=out.linear;
+    zpiece=out.piecewise;
     frcst_regime_history=out.regime_history;
     error_flag=out.error_flag;
     for i=1:M_.endo_nbr
-        forecast.linear.Mean.(M_.endo_names{i})= out.linear(:,i);
+        oo_.occbin.linear_forecast.Mean.(M_.endo_names{i})= zlin(:,i);
-        forecast.piecewise.Mean.(M_.endo_names{i})= out.piecewise(:,i);
+        oo_.occbin.forecast.Mean.(M_.endo_names{i})= zpiece(:,i);
+        oo_.occbin.forecast.HPDinf.(M_.endo_names{i})= nan;
+        oo_.occbin.forecast.HPDsup.(M_.endo_names{i})= nan;
     end
+
 end
 
-forecast.regimes=frcst_regime_history;
+oo_.occbin.forecast.regimes=frcst_regime_history;
+

matlab/+occbin/irf.m

@@ -1,124 +1,140 @@
-function irfs = irf(M_,oo_,options_)
+function [oo_] = irf(M_,oo_,options_)
-% irfs = irf(M_,oo_,options_)
-% Calls a minimizer
-%
-% INPUTS
-% - M_                  [structure]     Matlab's structure describing the model
-% - oo_                 [structure]     Matlab's structure containing the results
-% - options_            [structure]     Matlab's structure describing the current options
-%
-% OUTPUTS
-% - irfs                [structure]     IRF results
-%
-% SPECIAL REQUIREMENTS
-%   none.
-%
-%
-% Copyright © 2022-2023 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
 shocknames = options_.occbin.irf.exo_names;
-shocksigns = options_.occbin.irf.shocksigns; %'pos','neg'
+shocksigns = options_.occbin.irf.shocksigns;
 shocksize = options_.occbin.irf.shocksize;
-t_0 = options_.occbin.irf.t0;
+t0 = options_.occbin.irf.t0;
-
-%% set simulation options based on IRF options
 options_.occbin.simul.init_regime = options_.occbin.irf.init_regime;
 options_.occbin.simul.check_ahead_periods = options_.occbin.irf.check_ahead_periods;
 options_.occbin.simul.maxit = options_.occbin.irf.maxit;
 options_.occbin.simul.periods = options_.irf;
 
-%% Run initial conditions + other shocks
+% Run inital conditions + other shocks
-if t_0 == 0
+if t0 == 0
-    shocks_base = zeros(options_.occbin.simul.periods+1,M_.exo_nbr);
+    shocks0= zeros(options_.occbin.simul.periods+1,M_.exo_nbr);
     options_.occbin.simul.endo_init = [];
 else
-    if ~isfield(oo_.occbin,'smoother')
+    % girf conditional to smoothed states in t0 and shocks in t0+1
-        error('occbin.irfs: smoother must be run before requesting GIRFs based on smoothed results')
+    shocks0= [oo_.occbin.smoother.etahat(:,t0+1)'; zeros(options_.occbin.simul.periods,M_.exo_nbr)];
-    end
+    options_.occbin.simul.SHOCKS=shocks0;
-    % GIRF conditional on smoothed states in t_0 and shocks in t_0+1
+    options_.occbin.simul.endo_init = oo_.occbin.smoother.alphahat(oo_.dr.inv_order_var,t0);
-    shocks_base= [oo_.occbin.smoother.etahat(:,t_0+1)'; zeros(options_.occbin.simul.periods,M_.exo_nbr)];
-    options_.occbin.simul.SHOCKS=shocks_base;
-    options_.occbin.simul.endo_init = oo_.occbin.smoother.alphahat(oo_.dr.inv_order_var,t_0);
 end
-options_.occbin.simul.SHOCKS=shocks_base;
+options_.occbin.simul.SHOCKS=shocks0;
+[~, out0] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
+zlin0 = out0.linear;
+zpiece0 = out0.piecewise;
 
-[~, out_base] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
+% Select shocks of interest
-if out_base.error_flag
+jexo_all =zeros(size(shocknames,1),1);
-    error('occbin.irfs: could not compute the solution')
-end
 
-irfs.linear = struct();
-irfs.piecewise = struct();
-
-% Get indices of shocks of interest
-exo_index =zeros(size(shocknames,1),1);
 for i=1:length(shocknames)
-    exo_index(i) = strmatch(shocknames{i},M_.exo_names,'exact');
+    jexo_all(i) = strmatch(shocknames{i},M_.exo_names,'exact');
 end
 
-% cs=get_lower_cholesky_covariance(M_.Sigma_e,options_.add_tiny_number_to_cholesky);
+oo_.occbin.linear_irfs = struct();
-% irf_shocks_indx = getIrfShocksIndx(M_, options_);
+oo_.occbin.irfs = struct();
-
 % Set shock size
 if isempty(shocksize)
-    shocksize = sqrt(diag(M_.Sigma_e(exo_index,exo_index)));
+    %         if isfield(oo_.posterior_mode.shocks_std,M_.exo_names{jexo})
+    shocksize = sqrt(diag(M_.Sigma_e(jexo_all,jexo_all))); %oo_.posterior_mode.shocks_std.(M_.exo_names{jexo});
     if any(shocksize < 1.e-9)
         shocksize(shocksize < 1.e-9) = 0.01;
     end
 end
-
 if numel(shocksize)==1
     shocksize=repmat(shocksize,[length(shocknames),1]);
 end
 
 % Run IRFs
-for sign_iter=1:length(shocksigns)
+for counter = 1:length(jexo_all)
-    for IRF_counter = 1:length(exo_index)
+  
-        jexo = exo_index(IRF_counter);
+    jexo = jexo_all(counter);
-        if ~options_.noprint && options_.debug
+    
-            fprintf('occbin.irf: Producing GIRFs for shock %s. Simulation %d out of %d. \n',M_.exo_names{jexo},IRF_counter,size(exo_index,1));
+    if ~options_.noprint
-        end
+        fprintf('Producing GIRFs for shock %s. Simulation %d out of %d. \n',M_.exo_names{jexo},counter,size(jexo_all,1));
-        shocks1=shocks_base;
+    end 
-        if ismember('pos',shocksigns{sign_iter})
+    
-            shocks1(1,jexo)=shocks_base(1,jexo)+shocksize(IRF_counter);
+    if ismember('pos',shocksigns)
-        elseif ismember('neg',shocksigns{sign_iter})
+        % (+) shock
-            shocks1(1,jexo)=shocks_base(1,jexo)-shocksize(IRF_counter);
+        shocks1=shocks0;
-        end
+        shocks1(1,jexo)=shocks0(1,jexo)+shocksize(counter);
-        options_.occbin.simul.SHOCKS=shocks1;
+        if t0 == 0
-        if t_0 == 0
+            options_.occbin.simul.SHOCKS=shocks1;
             options_.occbin.simul.endo_init = [];
+            [~, out_pos] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
         else
-            options_.occbin.simul.endo_init = oo_.occbin.smoother.alphahat(oo_.dr.inv_order_var,t_0);
+            options_.occbin.simul.SHOCKS=shocks1;
+            options_.occbin.simul.endo_init = oo_.occbin.smoother.alphahat(oo_.dr.inv_order_var,t0);
+            [~, out_pos] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
         end
-        [~, out_sim] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
+        if out_pos.error_flag
-        if out_sim.error_flag
+            warning('Occbin error.')
-            warning('occbin.irfs: simulation failed')
+            return
-            skip
         end
+        zlin_pos = out_pos.linear;
+        zpiece_pos = out_pos.piecewise;
         % Substract inital conditions + other shocks
-        zdiff.linear.(shocksigns{sign_iter}) = out_sim.linear-out_base.linear;
+        zlin_pos_diff       = zlin_pos-zlin0;
-        zdiff.piecewise.(shocksigns{sign_iter}) = out_sim.piecewise-out_base.piecewise;
+        zpiece_pos_diff      = zpiece_pos-zpiece0;
-
-        for j_endo=1:M_.endo_nbr
-            if ismember('pos',shocksigns)
-                irfs.piecewise.([M_.endo_names{j_endo} '_' M_.exo_names{jexo} '_' shocksigns{sign_iter}])   = zdiff.piecewise.(shocksigns{sign_iter})(:,j_endo);
-                irfs.linear.([M_.endo_names{j_endo} '_' M_.exo_names{jexo} '_' shocksigns{sign_iter}])   = zdiff.linear.(shocksigns{sign_iter})(:,j_endo);
-            end
-        end
     end
-end
+    
+    if ismember('neg',shocksigns)
+        % (-) shock
+        shocks_1=shocks0;
+        shocks_1(1,jexo)=shocks0(1,jexo)-shocksize(counter);
+        if t0 == 0
+            options_.occbin.simul.SHOCKS=shocks_1;
+            options_.occbin.simul.endo_init = [];
+            [~, out_neg] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
+        else
+            options_.occbin.simul.SHOCKS=shocks_1;
+            options_.occbin.simul.endo_init = oo_.occbin.smoother.alphahat(oo_.dr.inv_order_var,t0);
+            [~, out_neg] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
+        end
+        if out_neg.error_flag
+            warning('Occbin error.')
+            return
+        end
+        zlin_neg    = out_neg.linear;
+        zpiece_neg  = out_neg.piecewise;
+        zlin_neg_diff    = zlin_neg-zlin0;
+        zpiece_neg_diff  = zpiece_neg-zpiece0;
+    end
+
+    % Save
+    if ~isfield(oo_.occbin,'linear_irfs')
+        oo_.occbin.linear_irfs = struct();
+    end
+    if ~isfield(oo_.occbin,'irfs')
+        oo_.occbin.irfs = struct();
+    end
+
+    for jendo=1:M_.endo_nbr
+%         oo_.occbin.irfs.([M_.endo_names{jendo} '_' M_.exo_names{jexo} '1'])   = zpiece_pos (:,jendo);
+%         oo_.occbin.irfs.([M_.endo_names{jendo} '_' M_.exo_names{jexo} '_1'])  = zpiece_neg (:,jendo);
+%         oo_.occbin.linear_irfs.([M_.endo_names{jendo} '_' M_.exo_names{jexo} '1'])   = zlin_pos (:,jendo);
+%         oo_.occbin.linear_irfs.([M_.endo_names{jendo} '_' M_.exo_names{jexo} '_1'])  = zlin_neg(:,jendo);
+   
+        if ismember('pos',shocksigns)
+        oo_.occbin.irfs.([M_.endo_names{jendo} '_' M_.exo_names{jexo} '_pos'])   = zpiece_pos_diff (:,jendo);
+        oo_.occbin.linear_irfs.([M_.endo_names{jendo} '_' M_.exo_names{jexo} '_pos'])   = zlin_pos_diff (:,jendo);
+        end
+        
+        if ismember('neg',shocksigns)
+            oo_.occbin.irfs.([M_.endo_names{jendo} '_' M_.exo_names{jexo} '_neg'])  = zpiece_neg_diff (:,jendo);
+            oo_.occbin.linear_irfs.([M_.endo_names{jendo} '_' M_.exo_names{jexo} '_neg'])  = zlin_neg_diff (:,jendo);
+        end
+        
+% %         
+%         oo_.occbin.irfs0.([M_.endo_names{jendo} '_' M_.exo_names{jexo} '1'])   = zpiece0(:,jendo);
+%         oo_.occbin.linear_irfs0.([M_.endo_names{jendo} '_' M_.exo_names{jexo} '1'])  = zlin0(:,jendo);
+%         oo_.occbin.irfs0.([M_.endo_names{jendo} '_' M_.exo_names{jexo} '_1'])   = zpiece0(:,jendo);
+%         oo_.occbin.linear_irfs0.([M_.endo_names{jendo} '_' M_.exo_names{jexo} '_1'])  = zlin0(:,jendo);
+
+    end
+end
+
+
+
+end
+

matlab/+occbin/plot_irfs.m

@@ -1,74 +1,34 @@
-function plot_irfs(M_,irfs,options_,var_list)
+function  plot_irfs(M_,oo_,options_,irf3,irf4)
-% plot_irfs(M_,irfs,options_,var_list)
-%
-% INPUTS
-% - M_                      [structure]     Matlab's structure describing the model
-% - irfs                    [structure]     IRF results
-% - options_                [structure]     Matlab's structure describing the current options
-% - var_list                [character array]  list of endogenous variables specified
-%
-% OUTPUTS
-%   none
-%
-% SPECIAL REQUIREMENTS
-%   none.
 
-% Copyright © 2022-2023 Dynare Team
+shocknames = options_.occbin.plot_irf.exo_names;
-%
+simulname = options_.occbin.plot_irf.simulname;
-% This file is part of Dynare.
+if isempty(simulname)
-%
+   simulname_ = simulname;
-% Dynare is free software: you can redistribute it and/or modify
+else 
-% it under the terms of the GNU General Public License as published by
+   simulname_ = [ simulname '_' ];
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
-
-if (isfield(options_,'irf_shocks')==0)
-    shock_names  = M_.exo_names;
-else
-    shock_names  = options_.irf_shocks;
 end
-
+vars_irf = options_.occbin.plot_irf.endo_names;
-simul_name = options_.occbin.plot_irf.simulname;
+endo_names_long = options_.occbin.plot_irf.endo_names_long;
-if isempty(simul_name)
-    save_name = simul_name;
-else
-    save_name = [ simul_name '_' ];
-end
-
-if isempty(var_list)
-    var_list = M_.endo_names(1:M_.orig_endo_nbr);
-end
-
-[i_var, ~, index_uniques] = varlist_indices(var_list, M_.endo_names);
-vars_irf=var_list(index_uniques);
-
 endo_scaling_factor = options_.occbin.plot_irf.endo_scaling_factor;
-length_irf = options_.irf;
+length_irf = options_.occbin.plot_irf.tplot;
+if isempty(length_irf)
+    length_irf = options_.irf;
+end
 
-steps_irf = 1;
+irflocation_lin   = oo_.occbin.linear_irfs;
+irflocation_piece = oo_.occbin.irfs;
+
+
+steps_irf = 1; 
+warning('off','all')
 
 DirectoryName = CheckPath('graphs',M_.dname);
-latexFolder = CheckPath('latex',M_.dname);
-if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
-    fidTeX = fopen([latexFolder '/' M_.fname '_occbin_irfs.tex'],'w');
-    fprintf(fidTeX,'%% TeX eps-loader file generated by occbin.plot_irfs.m (Dynare).\n');
-    fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
-    fprintf(fidTeX,' \n');
-end
 
 iexo=[];
-for var_iter=1:size(shock_names,1)
+for i=1:size(shocknames,1)
-    itemp = strmatch(shock_names{var_iter},M_.exo_names,'exact');
+    itemp = strmatch(shocknames{i},M_.exo_names,'exact');
     if isempty(itemp)
-        error(['Shock ',shock_names{var_iter},' is not defined!'])
+        error(['Shock ',shocknames{i},' is not defined!'])
     else
         iexo=[iexo, itemp];
     end
@@ -78,102 +38,104 @@ ncols     = options_.occbin.plot_irf.ncols;
 nrows     = options_.occbin.plot_irf.nrows;
 npan      = ncols*nrows;
 
-shocksigns = options_.occbin.plot_irf.shocksigns;
+plot_grid = options_.occbin.plot_irf.grid;
-threshold = options_.impulse_responses.plot_threshold;
+shocksigns = options_.occbin.plot_irf.shocksigns; 
+threshold = options_.occbin.plot_irf.threshold; 
+
+% Add steady_state
+if options_.occbin.plot_irf.add_steadystate
+    add_stst = options_.occbin.plot_irf.add_steadystate;
+else
+    add_stst = 0;
+end 
+for sss = 1:numel(shocksigns)
+    
+    shocksign = shocksigns{sss};
+    
+    for j=1:size(shocknames,1)
+        %shocknames = M_.exo_names{j};
 
-for shock_sign_iter = 1:numel(shocksigns)
-    shocksign = shocksigns{shock_sign_iter};
-    if strcmp(shocksign,'pos')
-        plot_title_sign='positive';
-    elseif strcmp(shocksign,'neg')
-        plot_title_sign='negative';
-    else
-       error('Unknown shock sign %s',shocksign);
-    end
-        
-    for shock_iter=1:size(shock_names,1)
         j1   = 0;
         isub = 0;
         ifig = 0;
+
         % Variables
-        for var_iter = 1:length(vars_irf)
+        % ----------------------
+        for i = 1:length(vars_irf)
+
             j1=j1+1;
             if mod(j1,npan)==1
-                % vector corresponds to [left bottom width height]. 680 and 678 for the left and bottom elements correspond to the default values used by MATLAB while creating a figure and width, .
+               % vector corresponds to [left bottom width height]. 680 and 678 for the left and bottom elements correspond to the default values used by MATLAB while creating a figure and width, .
-                screensize = get( groot, 'Screensize' );
+                hfig = dyn_figure(options_.nodisplay,'name',['OccbinIRFs ' shocknames{j} ' ' simulname ' ' shocksign],'PaperPositionMode', 'auto','PaperType','A4','PaperOrientation','portrait','renderermode','auto','position',[10 10 950 650]);
-                hfig = dyn_figure(options_.nodisplay,'name',['OccBin IRFs to ' plot_title_sign ' ' shock_names{shock_iter} ' shock ' simul_name],'OuterPosition' ,[50 50 min(1000,screensize(3)-50) min(750,screensize(4)-50)]);
                 ifig=ifig+1;
                 isub=0;
             end
-            isub=isub+1;
+            isub=isub+1;      
+            
             if isempty(endo_scaling_factor)
                 exofactor = 1;
-            else
+            else               
-                exofactor = endo_scaling_factor{var_iter};
+                exofactor = endo_scaling_factor{i};
-            end
+            end 
 
-            subplot(nrows,ncols,isub)
+            subplot(nrows,ncols,isub)            
-            irf_field   = strcat(vars_irf{var_iter},'_',shock_names{shock_iter},'_',shocksign);
+            irf_field   = strcat(vars_irf{i,1},'_',shocknames{j},'_',shocksign);
-
+            
-            %%linear IRFs
+            irfvalues   = irflocation_lin.(irf_field);
-            if ~isfield(irfs.linear,irf_field)
+            if add_stst
-                warning('occbin.plot_irfs: no linear IRF for %s to %s detected',vars_irf{var_iter,1},shock_names{shock_iter})
+                irfvalues = irfvalues + get_mean(vars_irf{i,1});
-            else
+            end          
-                irfvalues   = irfs.linear.(irf_field);
+            
-                irfvalues(abs(irfvalues) <threshold) = 0;
+            irfvalues(abs(irfvalues) <threshold) = 0;
-                if options_.occbin.plot_irf.add_steadystate
+            
-                    irfvalues = irfvalues + get_mean(vars_irf{var_iter,1});
+            plot(irfvalues(1:steps_irf:length_irf)*exofactor,'linewidth',2);
-                end
-                max_irf_length_1=min(length_irf,length(irfvalues));
-                plot(irfvalues(1:steps_irf:max_irf_length_1)*exofactor,'linewidth',2);
-            end
             hold on
 
-            if ~isfield(irfs.piecewise,irf_field)
+            irfvalues   = irflocation_piece.(irf_field);  
-                warning('occbin.plot_irfs: no piecewise linear IRF for %s to %s detected',vars_irf{var_iter,1},shock_names{shock_iter})
+            if add_stst
-            else
+                irfvalues = irfvalues + get_mean(vars_irf{i,1});
-                irfvalues   = irfs.piecewise.(irf_field);
-                irfvalues(abs(irfvalues) <threshold) = 0;
-                if options_.occbin.plot_irf.add_steadystate
-                    irfvalues = irfvalues + get_mean(vars_irf{var_iter,1});
-                end
-                max_irf_length_2=min(length_irf,length(irfvalues));
-                plot(irfvalues(1:steps_irf:max_irf_length_2)*exofactor,'r--','linewidth',2);
             end
+            irfvalues(abs(irfvalues) <threshold) = 0;
+            plot(irfvalues(1:steps_irf:length_irf)*exofactor,'r--','linewidth',2);
+            
+            hold on
+            plot(irfvalues(1:steps_irf:length_irf)*0,'k-','linewidth',1.5);
+            % Optional additional IRFs
+            if nargin > 10
+                  irfvalues   = irf3.(irf_field) ;
+                  irfvalues(abs(irfvalues) <threshold) = 0;
+                  plot(irfvalues(1:steps_irf:length_irf)*exofactor,'k:','linewidth',2);
+            end
+            if nargin > 11
+                  irfvalues   = irf4.(irf_field)   ;   
+                  irfvalues(abs(irfvalues) <threshold) = 0;
+                  plot(irfvalues(1:steps_irf:length_irf)*exofactor,'g-.','linewidth',2);
+            end
+      
 
-            plot(irfvalues(1:steps_irf:max(max_irf_length_1,max_irf_length_2))*0,'k-','linewidth',1.5);
+            if plot_grid
-
-            if options_.occbin.plot_irf.grid
                 grid on
-            end     
-            xlim([1 max(max_irf_length_1,max_irf_length_2)]);
-            if options_.TeX
-                title(['$' M_.endo_names_tex{i_var(var_iter)}, '$'],'Interpreter','latex')
-            else
-                title(M_.endo_names{i_var(var_iter)},'Interpreter','none')
             end
+
+            xlim([1 (length_irf/steps_irf)]);
+            
+            % title
+            if isempty(endo_names_long)
+                title(regexprep(vars_irf{i},'_',' '))
+            else
+                title(endo_names_long{i})
+            end
+
             % Annotation Box + save figure
             % ----------------------
-            if mod(j1,npan)==0 || (mod(j1,npan)~=0 && var_iter==length(vars_irf))
+            if mod(j1,npan)==0 || (mod(j1,npan)~=0 && i==length(vars_irf))
                 annotation('textbox', [0.1,0,0.35,0.05],'String', 'Linear','Color','Blue','horizontalalignment','center','interpreter','none');
                 annotation('textbox', [0.55,0,0.35,0.05],'String', 'Piecewise','Color','Red','horizontalalignment','center','interpreter','none');
-                dyn_saveas(hfig,[DirectoryName,filesep,M_.fname,'_irf_occbin_',save_name,shock_names{shock_iter},'_',shocksign,'_',int2str(ifig)],options_.nodisplay,options_.graph_format);
+                dyn_saveas(hfig,[DirectoryName,filesep,M_.fname,'_irf_occbin_',simulname_,shocknames{j},'_',shocksign,'_',int2str(ifig)],options_.nodisplay,options_.graph_format);
-                if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
-                    fprintf(fidTeX,'\\begin{figure}[H]\n');
-                    fprintf(fidTeX,'\\centering \n');
-                    fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_irf_occbin_%s}\n',options_.figures.textwidth*min((j1-1)/ncols,1),...
-                        [DirectoryName '/' ,M_.fname],[save_name,shock_names{shock_iter},'_',shocksign,'_',int2str(ifig)]);
-                    fprintf(fidTeX,'\\caption{OccBin IRFs to  %s shock to %s}\n',plot_title_sign,shock_names{shock_iter});
-                    fprintf(fidTeX,'\\label{Fig:occbin_irfs:%s}\n',[save_name,shock_names{shock_iter},'_',shocksign,'_',int2str(ifig)]);
-                    fprintf(fidTeX,'\\end{figure}\n');
-                    fprintf(fidTeX,'\n');
-                end
             end
-        end
+        end 
     end
 end
 
-if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
+warning('on','all')
-    fprintf(fidTeX,'%% End Of TeX file.');
-    fclose(fidTeX);
 end

matlab/+occbin/plot_regimes.m

@@ -1,26 +1,4 @@
 function plot_regimes(regimes,M_,options_)
-% plot_regimes(regimes,M_,options_)
-% Inputs: 
-% - regimes     	[structure]     OccBin regime information
-% - M_              [structure]     Matlab's structure describing the model
-% - options_        [structure]     Matlab's structure containing the options
-
-% Copyright © 2021-2023 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
 nperiods = size(regimes,2);
 nconstr = length(fieldnames(regimes(1)))/2;
@@ -35,15 +13,9 @@ else
 end    
 
 GraphDirectoryName = CheckPath('graphs',M_.dname);
-fhandle = dyn_figure(options_.nodisplay,'Name',[M_.fname ': OccBin regimes']);
 
-latexFolder = CheckPath('latex',M_.dname);
+fhandle = dyn_figure(options_.nodisplay,'Name',[M_.fname ' occbin regimes']);
-if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
+
-    fidTeX = fopen([latexFolder '/' M_.fname '_occbin_regimes.tex'],'w');
-    fprintf(fidTeX,'%% TeX eps-loader file generated by occbin.plot_regimes.m (Dynare).\n');
-    fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
-    fprintf(fidTeX,' \n');
-end
 
 for k=1:nconstr
     subplot(nconstr,1,k)
@@ -64,23 +36,12 @@ for k=1:nconstr
             end
         end
     end
-    xlim([1 nperiods])
+    title(['regime ' int2str(k)])
-    title(['Regime ' int2str(k)])
+    xlabel('historic period')
-    xlabel('Historic period')
+    ylabel('regime expected start')
-    ylabel('Regime: expected start')
 end
-annotation('textbox',[.25,0,.15,.05],'String','Slack','Color','blue');
+annotation('textbox',[.25,0,.15,.05],'String','Unbinding','Color','blue');
-annotation('textbox',[.65,0,.2,.05],'String','Binding','Color','red');
+annotation('textbox',[.65,0,.15,.05],'String','Binding','Color','red');
+
 
 dyn_saveas(fhandle,[GraphDirectoryName, filesep, M_.fname '_occbin_regimes'],options_.nodisplay,options_.graph_format);
-
-if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
-    fprintf(fidTeX,'\\begin{figure}[H]\n');
-    fprintf(fidTeX,'\\centering \n');
-    fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_occbin_regimes}\n',options_.figures.textwidth,[GraphDirectoryName '/' M_.fname]);
-    fprintf(fidTeX,'\\caption{OccBin: regime evolution over time.}\n');
-    fprintf(fidTeX,'\\label{Fig:occbin_regimes}\n');
-    fprintf(fidTeX,'\\end{figure}\n');
-    fprintf(fidTeX,'\n');
-    fclose(fidTeX);
-end

matlab/+occbin/set_default_options.m

@@ -55,7 +55,9 @@ if ismember(flag,{'forecast','all'})
     options_occbin_.forecast.maxit=30;
     options_occbin_.forecast.qmc=0;
     options_occbin_.forecast.replic=0;
+    options_occbin_.forecast.sepath=0;    
     options_occbin_.forecast.SHOCKS0=[];
+    options_occbin_.forecast.treepath=1; % number of branches
 end
 
 if ismember(flag,{'irf','all'})    
@@ -96,12 +98,17 @@ end
 
 if ismember(flag,{'plot_irf','all'})
     options_occbin_.plot_irf.add_steadystate = 0;
+    options_occbin_.plot_irf.exo_names = [];
+    options_occbin_.plot_irf.endo_names = M_.endo_names;
+    options_occbin_.plot_irf.endo_names_long = [];
     options_occbin_.plot_irf.endo_scaling_factor = [];
     options_occbin_.plot_irf.grid            = true;
     options_occbin_.plot_irf.ncols            = 3;
     options_occbin_.plot_irf.nrows            = 3;
     options_occbin_.plot_irf.shocksigns = {'pos','neg'}; 
     options_occbin_.plot_irf.simulname='';
+    options_occbin_.plot_irf.threshold = 10^-6;
+    options_occbin_.plot_irf.tplot = [];
 end
 
 if ismember(flag,{'plot_shock_decomp','all'})

matlab/+pac/+update/parameters.m

@@ -13,7 +13,7 @@ function M_ = parameters(pacname, M_, oo_, verbose)
 % SPECIAL REQUIREMENTS
 %    none
 
-% Copyright © 2018-2024 Dynare Team
+% Copyright © 2018-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -78,11 +78,6 @@ else
     numberofcomponents = 0;
 end
 
-% Makes no sense to have a composite PAC target with a trend component auxiliary model (where all the variables are non stationnary)
-if numberofcomponents>0 && strcmp(M_.pac.pacman.auxiliary_model_type, 'trend_component')
-    error('Composite PAC target not allowed with trend component model.')
-end
-
 % Build the vector of PAC parameters (ECM parameter + autoregressive parameters).
 pacvalues = M_.params([pacmodel.ec.params; pacmodel.ar.params(1:pacmodel.max_lag)']);
 
@@ -95,7 +90,7 @@ if numberofcomponents
             % Find the auxiliary variables if any
             ad = find(cell2mat(cellfun(@(x) isauxiliary(x, [8 10]), varmodel.list_of_variables_in_companion_var, 'UniformOutput', false)));
             if isempty(ad)
-                error('Cannot find the trend variable in the Companion VAR model.')
+                error('Cannot find the trend variable in the Companion VAR/VECM model.')
             else
                 for j=1:length(ad)
                     auxinfo = M_.aux_vars(get_aux_variable_id(varmodel.list_of_variables_in_companion_var{ad(j)}));
@@ -110,7 +105,7 @@ if numberofcomponents
                 end
             end
             if isempty(id{i})
-                error('Cannot find the trend variable in the Companion VAR model.')
+                error('Cannot find the trend variable in the Companion VAR/VECM model.')
             end
         end
     end
@@ -120,7 +115,7 @@ else
         % Find the auxiliary variables if any
         ad = find(cell2mat(cellfun(@(x) isauxiliary(x, [8 10]), varmodel.list_of_variables_in_companion_var, 'UniformOutput', false)));
         if isempty(ad)
-            error('Cannot find the trend variable in the auxiliary VAR / Trend component model.')
+            error('Cannot find the trend variable in the Companion VAR/VECM model.')
         else
             for i=1:length(ad)
                 auxinfo = M_.aux_vars(get_aux_variable_id(varmodel.list_of_variables_in_companion_var{ad(i)}));
@@ -135,7 +130,7 @@ else
             end
         end
         if isempty(id{1})
-            error('Cannot find the trend variable in the auxiliary VAR / Trend component model.')
+            error('Cannot find the trend variable in the Companion VAR/VECM model.')
         end
     end
 end

matlab/backward/simul_static_model.m

@@ -15,7 +15,7 @@ function simulation = simul_static_model(samplesize, innovations)
 % [2] The last input argument is not mandatory. If absent we use random draws and rescale them with the informations provided
 %     through the shocks block.
 
-% Copyright © 2019-2024 Dynare Team
+% Copyright © 2019-2022 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -83,17 +83,12 @@ else
     oo_.exo_simul = Innovations;
 end
 
-static_resid = str2func(sprintf('%s.sparse.static_resid', M_.fname));
+staticmodel = str2func(sprintf('%s.static', M_.fname));
-static_g1 = str2func(sprintf('%s.sparse.static_g1', M_.fname));
-function [resid, g1] = staticmodel(y, x, params)
-    [resid, T_order, T] = static_resid(y, x, params);
-    g1 = static_g1(y, x, params, M_.static_g1_sparse_rowval, M_.static_g1_sparse_colval, M_.static_g1_sparse_colptr, T_order, T);
-end
 
 % Simulations (call a Newton-like algorithm for each period).
 for t=1:samplesize
     y = zeros(M_.endo_nbr, 1);
-    [oo_.endo_simul(:,t), errorflag, ~, ~, errorcode] = dynare_solve(@staticmodel, y, options_.simul.maxit, options_.dynatol.f, options_.dynatol.x, options_, oo_.exo_simul(t,:), M_.params);
+    [oo_.endo_simul(:,t), errorflag, ~, ~, errorcode] = dynare_solve(staticmodel, y, options_.simul.maxit, options_.dynatol.f, options_.dynatol.x, options_, oo_.exo_simul(t,:), M_.params);
     if errorflag
         dprintf('simul_static_mode: Nonlinear solver failed with errorcode=%i in period %i.', errorcode, t)
         oo_.endo_simul(:,t) = nan;
@@ -108,6 +103,4 @@ if isdseries(innovations)
     initperiod = innovations.dates(1);
 end
 
-simulation = [dseries(ysim', initperiod, M_.endo_names(1:M_.orig_endo_nbr)), dseries(xsim, initperiod, M_.exo_names)];
+simulation = [dseries(ysim', initperiod, M_.endo_names(1:M_.orig_endo_nbr)), dseries(xsim, initperiod, M_.exo_names)];
-
-end

matlab/build_two_dim_hessian.m

@@ -1,58 +0,0 @@
-function H = build_two_dim_hessian(sparse_indices, g2_v, neq, nvar)
-% Creates a 2D Hessian (equations in rows, pairs of variables in columns),
-% given the output from the sparse {static,dynamic}_g2.m
-%
-% – sparse_indices is typically equal to M_.{static,dynamic}_g2_sparse_indices
-% – g2_v is the vector of non zero values returned by {static,dynamic}_g2.m
-% – neq is the number of equations (equal to number of rows of the output matrix)
-% – nvar is the number of variables (the output matrix will have nvar² columns)
-
-% Copyright © 2024 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
-
-nnz = size(sparse_indices, 1);
-
-%% The g2_* arrays may be expanded if there are symmetric elements added
-g2_i = int32(zeros(nnz, 1));
-g2_j = int32(zeros(nnz, 1));
-
-next_sym_idx = nnz + 1; % Index of next symmetric element to be added
-
-for k = 1:length(g2_v)
-    eq = sparse_indices(k, 1);
-    var1 = sparse_indices(k, 2)-1;
-    var2 = sparse_indices(k, 3)-1;
-
-    g2_i(k) = eq;
-    g2_j(k) = var1 * nvar + var2 + 1;
-
-    %% Add symmetric elements, which are not included by sparse {static,dynamic}_g2.m
-    if var1 ~= var2
-        g2_i(next_sym_idx) = eq;
-        g2_j(next_sym_idx) = var2 * nvar + var1 + 1;
-        g2_v(next_sym_idx) = g2_v(k);
-        next_sym_idx = next_sym_idx + 1;
-    end
-end
-
-%% On MATLAB < R2020a, sparse() does not accept int32 indices
-if ~isoctave && matlab_ver_less_than('9.8')
-    g2_i = double(g2_i);
-    g2_j = double(g2_j);
-end
-
-H = sparse(g2_i, g2_j, g2_v, neq, nvar*nvar);

matlab/collapse_figures_in_tabgroup.m

@@ -1,42 +0,0 @@
-function collapse_figures_in_tabgroup
-
-% Copyright © 2023 Eduard Benet Cerda
-% Copyright © 2024 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
-
-% Create a new figure with results
-fig = uifigure(Name = 'Dynare Results');
-
-% Add a grid layout to make sure it spans the entire width
-g = uigridlayout(fig, [1,1], Padding = 0);
-
-% Add a tabgroup
-tg = uitabgroup(g);
-
-% Find all figures with Dynare Tag 
-f = findobj('-regexp','tag','dynare-figure');
-
-% Loop over all figures and reparent them to a tab. Avoid legends, they are
-% automatically tied.
-for j = 1 : numel(f)
-    t = uitab(tg);
-    types = arrayfun(@class, f(j).Children, 'UniformOutput', false);
-    idx = ismember(types, 'matlab.graphics.illustration.Legend'); % no need to reparent legends
-    set(f(j).Children(~idx),'Parent',t)
-    t.Title = f(j).Name;
-    delete(f(j))
-end

matlab/discretionary_policy/discretionary_policy_1.m

@@ -13,7 +13,7 @@ function [dr, info, params]=discretionary_policy_1(M_, options_, dr, endo_steady
 % - info          [integer]       scalar or vector, error code.
 % - params        [double]        vector of potentially updated parameters
 
-% Copyright © 2007-2024 Dynare Team
+% Copyright © 2007-2020 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -49,9 +49,7 @@ else
 end
 params=M_.params;
 
-y = zeros(M_.endo_nbr,1);
+[~,Uy,W] = feval([M_.fname,'.objective.static'],zeros(M_.endo_nbr,1),[], M_.params);
-[Uy, T_order, T] = feval([M_.fname,'.objective.sparse.static_g1'], y, [], params, M_.objective_g1_sparse_rowval, M_.objective_g1_sparse_colval, M_.objective_g1_sparse_colptr);
-
 if any(any(isnan(Uy)))
     info = 64 ; %the derivatives of the objective function contain NaN
     return;
@@ -72,8 +70,6 @@ if any(any(Uy~=0))
     return;
 end
 
-g2_v = feval([M_.fname,'.objective.sparse.static_g2'], y, [], params, T_order, T);
-W = build_two_dim_hessian(M_.objective_g2_sparse_indices, g2_v, 1, M_.endo_nbr);
 W=reshape(W,M_.endo_nbr,M_.endo_nbr);
 
 klen = M_.maximum_lag + M_.maximum_lead + 1;
@@ -126,4 +122,4 @@ dr.ghu=G(dr.order_var,:);
 if M_.maximum_endo_lag
     Selection=M_.lead_lag_incidence(1,dr.order_var)>0;%select state variables
 end
-dr.ghx=T(:,Selection);
+dr.ghx=T(:,Selection);

matlab/dyn_figure.m

@@ -12,7 +12,7 @@ function h = dyn_figure(nodisplay, varargin)
 % SPECIAL REQUIREMENTS
 %    none
 
-% Copyright © 2012-2024 Dynare Team
+% Copyright © 2012-2017 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -30,7 +30,7 @@ function h = dyn_figure(nodisplay, varargin)
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
 if nodisplay
-    h = figure(varargin{:},'visible','off','tag','dynare-figure');
+    h = figure(varargin{:},'visible','off');
 else
-    h = figure(varargin{:},'tag','dynare-figure');
+    h = figure(varargin{:});
 end

matlab/dynare.m

@@ -122,7 +122,7 @@ if isempty(dot_location)
     fnamelength = length(fname);
     fname1 = [fname '.dyn'];
     d = dir(fname1);
-    if isempty(d)
+    if length(d) == 0
         fname1 = [fname '.mod'];
     end
     fname = fname1;
@@ -138,7 +138,7 @@ if fnamelength + length('.set_auxiliary_variables') > namelengthmax()
     error('Dynare: the name of your .mod file is too long, please shorten it')
 end
 
-if contains(fname,filesep)
+if ~isempty(strfind(fname,filesep))
     fprintf('\nIt seems you are trying to call a .mod file not located in the "Current Folder". This is not possible (the %s symbol is not allowed in the name of the .mod file).\n', filesep)
     [pathtomodfile,basename] = fileparts(fname);
     if exist(pathtomodfile,'dir')
@@ -297,13 +297,17 @@ if status
     error('Dynare: preprocessing failed')
 end
 
+if ~ isempty(find(abs(fname) == 46))
+    fname = fname(:,1:find(abs(fname) == 46)-1) ;
+end
+
 % We need to clear the driver (and only the driver, because the "clear all"
 % within the driver will clean the rest)
-clear(['+' fname(1:end-4) '/driver'])
+clear(['+' fname '/driver'])
 
 try
     cTic = tic;
-    evalin('base',[fname(1:end-4) '.driver']);
+    evalin('base',[fname '.driver']);
     cToc = toc(cTic);
     if nargout
         DynareInfo.time.compute = cToc;
@@ -311,7 +315,7 @@ try
 catch ME
     W = evalin('caller','whos');
     diary off
-    if ismember(fname(1:end-4),{W(:).name})
+    if ismember(fname,{W(:).name})
         error('Your workspace already contains a variable with the same name as the mod-file. You need to delete it or rename the mod-file.')
     else
         rethrow(ME)

matlab/dyntable.m

@@ -63,7 +63,7 @@ end
 value_format  = sprintf('%%%d.%df', val_width, val_precis);
 header_string_format  = sprintf('%%%ds', val_width);
 
-if ~isempty(title)
+if length(title) > 0
     fprintf('\n\n%s\n',title);
 end
 
@@ -72,7 +72,7 @@ if nargin==9
     disp(optional_header)
 end
 
-if ~isempty(headers)
+if length(headers) > 0
     hh_tbl = sprintf(label_format_leftbound , headers{1});
     for i=2:length(headers)
         hh_tbl  = [hh_tbl sprintf(header_string_format, headers{i})];

matlab/estimation/dsge_likelihood.m

@@ -1,6 +1,6 @@
 function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,M_,options_,bayestopt_,dr] = dsge_likelihood(xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,BoundsInfo,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,derivatives_info)
 % [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,M_,options_,bayestopt_,oo_] = dsge_likelihood(xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,BoundsInfo,oo_,derivatives_info)
-% Evaluates the negative of the posterior kernel of a DSGE model using the specified
+% Evaluates the posterior kernel of a DSGE model using the specified
 % kalman_algo; the resulting posterior includes the 2*pi constant of the
 % likelihood function
 %
@@ -21,7 +21,7 @@ function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,M_,options_,baye
 % - derivatives_info    [structure]     derivative info for identification
 %
 % OUTPUTS
-% - fval                    [double]        scalar, value of minus the likelihood or posterior kernel.
+% - fval                    [double]        scalar, value of the likelihood or posterior kernel.
 % - info                    [integer]       4×1 vector, informations resolution of the model and evaluation of the likelihood.
 % - exit_flag               [integer]       scalar, equal to 1 (no issues when evaluating the likelihood) or 0 (not able to evaluate the likelihood).
 % - DLIK                    [double]        Vector with score of the likelihood
@@ -37,7 +37,7 @@ function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,M_,options_,baye
 % This function calls: dynare_resolve, lyapunov_symm, lyapunov_solver, compute_Pinf_Pstar, kalman_filter_d, missing_observations_kalman_filter_d,
 % univariate_kalman_filter_d, kalman_steady_state, get_perturbation_params_deriv, kalman_filter, missing_observations_kalman_filter, univariate_kalman_filter, priordens
 
-% Copyright © 2004-2024 Dynare Team
+% Copyright © 2004-2023 Dynare Team
 %
 % This file is part of Dynare.
 %

matlab/estimation/dynare_estimation_1.m

@@ -493,66 +493,52 @@ if issmc(options_) || (any(bayestopt_.pshape>0) && options_.mh_replic) ||  (any(
         end
         if ~issmc(options_)
             [error_flag, ~, options_]= metropolis_draw(1, options_, estim_params_, M_);
-        else
-            error_flag=false;
         end
         if ~(~isempty(options_.sub_draws) && options_.sub_draws==0)
             if options_.bayesian_irf
-                if ~issmc(options_)
+                if error_flag
-                    if error_flag
+                    error('%s: I cannot compute the posterior IRFs!',dispString)
-                        error('%s: I cannot compute the posterior IRFs!',dispString)
-                    end
-                    oo_=PosteriorIRF('posterior',options_,estim_params_,oo_,M_,bayestopt_,dataset_,dataset_info,dispString);
-                else
-                    fprintf('%s: SMC does not yet support the bayesian_irf option. Skipping computation.\n',dispString);
                 end
+                oo_=PosteriorIRF('posterior',options_,estim_params_,oo_,M_,bayestopt_,dataset_,dataset_info,dispString);
             end
             if options_.moments_varendo
-                if ~issmc(options_)
+                if error_flag
-                    if error_flag
+                    error('%s: I cannot compute the posterior moments for the endogenous variables!',dispString)
-                        error('%s: I cannot compute the posterior moments for the endogenous variables!',dispString)
+                end
-                    end
+                if options_.load_mh_file && options_.mh_replic==0 %user wants to recompute results
-                    if options_.load_mh_file && options_.mh_replic==0 %user wants to recompute results
+                    [MetropolisFolder, info] = CheckPath('metropolis',M_.dname);
-                        [MetropolisFolder, info] = CheckPath('metropolis',M_.dname);
+                    if ~info
-                        if ~info
+                        generic_post_data_file_name={'Posterior2ndOrderMoments','decomposition','PosteriorVarianceDecomposition','correlation','PosteriorCorrelations','conditional decomposition','PosteriorConditionalVarianceDecomposition'};
-                            generic_post_data_file_name={'Posterior2ndOrderMoments','decomposition','PosteriorVarianceDecomposition','correlation','PosteriorCorrelations','conditional decomposition','PosteriorConditionalVarianceDecomposition'};
+                        for ii=1:length(generic_post_data_file_name)
-                            for ii=1:length(generic_post_data_file_name)
+                            delete_stale_file([MetropolisFolder filesep M_.fname '_' generic_post_data_file_name{1,ii} '*']);
-                                delete_stale_file([MetropolisFolder filesep M_.fname '_' generic_post_data_file_name{1,ii} '*']);
+                        end
-                            end
+                        % restore compatibility for loading pre-4.6.2 runs where estim_params_ was not saved; see 6e06acc7 and !1944
-                            % restore compatibility for loading pre-4.6.2 runs where estim_params_ was not saved; see 6e06acc7 and !1944
+                        NumberOfDrawsFiles = length(dir([M_.dname '/metropolis/' M_.fname '_posterior_draws*' ]));
-                            NumberOfDrawsFiles = length(dir([M_.dname '/metropolis/' M_.fname '_posterior_draws*' ]));
+                        if NumberOfDrawsFiles>0
-                            if NumberOfDrawsFiles>0
+                            temp=load([M_.dname '/metropolis/' M_.fname '_posterior_draws1']);
-                                temp=load([M_.dname '/metropolis/' M_.fname '_posterior_draws1']);
+                            if ~isfield(temp,'estim_params_')
-                                if ~isfield(temp,'estim_params_')
+                                for file_iter=1:NumberOfDrawsFiles
-                                    for file_iter=1:NumberOfDrawsFiles
+                                    save([M_.dname '/metropolis/' M_.fname '_posterior_draws' num2str(file_iter)],'estim_params_','-append')
-                                        save([M_.dname '/metropolis/' M_.fname '_posterior_draws' num2str(file_iter)],'estim_params_','-append')
-                                    end
                                 end
                             end
                         end
                     end
-                    oo_ = compute_moments_varendo('posterior',options_,M_,oo_,estim_params_,var_list_);
-                else
-                    fprintf('%s: SMC does not yet support the moments_varendo option. Skipping computation.\n',dispString);
                 end
+                oo_ = compute_moments_varendo('posterior',options_,M_,oo_,estim_params_,var_list_);
             end
             if options_.smoother || ~isempty(options_.filter_step_ahead) || options_.forecast
-                if ~ishssmc(options_)
+                if error_flag
-                    if error_flag
+                    error('%s: I cannot compute the posterior statistics!',dispString)
-                        error('%s: I cannot compute the posterior statistics!',dispString)
+                end
-                    end
+                if options_.order==1 && ~options_.particle.status
-                    if options_.order==1 && ~options_.particle.status
+                    oo_=prior_posterior_statistics('posterior',dataset_,dataset_info,M_,oo_,options_,estim_params_,bayestopt_,dispString); %get smoothed and filtered objects and forecasts
-                        oo_=prior_posterior_statistics('posterior',dataset_,dataset_info,M_,oo_,options_,estim_params_,bayestopt_,dispString); %get smoothed and filtered objects and forecasts
-                    else
-                        error('%s: Particle Smoothers are not yet implemented.',dispString)
-                    end
                 else
-                    fprintf('%s: SMC does not yet support the smoother and forecast options. Skipping computation.\n',dispString);
+                    error('%s: Particle Smoothers are not yet implemented.',dispString)
                 end
             end
-        else
+            else
-            fprintf('%s: sub_draws was set to 0. Skipping posterior computations.\n',dispString);
+                fprintf('%s: sub_draws was set to 0. Skipping posterior computations.',dispString);
-        end
+            end
         xparam1 = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
         M_ = set_all_parameters(xparam1,estim_params_,M_);
     end

matlab/estimation/likelihood_quadratic_approximation.m

@@ -0,0 +1,191 @@
+function [f, df, d2f, R2] = likelihood_quadratic_approximation(particles, likelihoodvalues)
+
+% Approximate the shape of the likelihood function with a multivariate second order polynomial.
+%
+%
+% INPUTS
+% - particles            [double]   n×p matrix of (p) particles around the estimated posterior mode.
+% - likelihoodvalues     [double]   p×1 vector of corresponding values for the likelihood (or posterior kernel).
+%
+% OUTPUTS
+% - f                    [handle]   function handle for the approximated likelihood.
+% - df                   [handle]   function handle for the gradient of the approximated likelihood.
+% - d2f                  [handle]   Hessian matrix of the approximated likelihood (constant since we consider a second order multivariate polynomial)
+% - R2                   [double]   scalar, goodness of fit measure.
+%
+% REMARKS
+% [1] Function f takes a n×m matrix as input argument (the approximated likelihood is evaluated in m points) and returns a m×1 vector.
+% [2] Funtion df takes a n×1 vector as input argument (the point where the gradient is computed) and returns a n×1 vector.
+
+% Copyright © 2024 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
+
+    n = rows(particles);      % Number of parmaeters
+    p = columns(particles);   % Number of particles
+    q = 1 + n + n*(n+1)/2;    % Number of regressors (with a constant)
+
+    if p<=q
+        error('Quadratic approximation requires more than %u particles.', q)
+    end
+
+    %
+    % Build the set of regressors.
+    %
+
+    X = NaN(p, q);
+    X(:,1) = 1;                                   % zero order term
+    X(:,2:n+1) = transpose(particles);            % first order terms
+    X(:,n+2:end) = crossproducts(particles);      % second order terms
+
+    %
+    % Perform the regression
+    %
+
+    parameters = X\likelihoodvalues(:);
+
+    %
+    % Return a function to evaluate the approximation at x (a n×1 vector).
+    %
+
+    f = @(X) parameters(1) + transpose(X)*parameters(2:n+1) + crossproducts(X)*parameters(n+2:end);
+
+    if nargout>1
+        %
+        % Return a function to evaluate the gradient of the approximation at x (a n×1 vector)
+        %
+
+        df = @(X) parameters(2:n+1) + dcrossproducts(X)*parameters(n+2:end);
+
+        if nargout>2
+            %
+            % Return the hessian matrix of the approximation.
+            %
+
+            d2f = NaN(n,n);
+
+            h = 1;
+            for i=1:n
+                for j=i:n
+                    d2f(i,j) = parameters(n+1+h);
+                    if ~isequal(j, i)
+                        d2f(j,i) = d2f(i,j);
+                    end
+                    h = h+1;
+                end
+            end
+
+            if nargout>3
+                %
+                % Return a measure of fit goodness
+                %
+
+                R2 = 1-sum((likelihoodvalues(:)-X*parameters).^2)/sum(demean(likelihoodvalues(:)).^2);
+
+            end
+
+        end
+    end
+
+
+    function XX = crossproducts(X)
+    %                         n          n
+    % XX*ones(1,(n+1)*n/2) =  ∑  xᵢ² + 2 ∑ xᵢxⱼ
+    %                        i=1        i=1
+    %                                   j>i
+        XX = NaN(columns(X), n*(n+1)/2);
+        column = 1;
+        for i=1:n
+            XX(:,column) = transpose(X(i,:).*X(i,:));
+            column = column+1;
+            for j=i+1:n
+                XX(:,column) = 2*transpose(X(i,:).*X(j,:));
+                column = column+1;
+            end
+        end
+    end
+
+
+    function xx = dcrossproducts(x)
+        xx = zeros(n, n*(n+1)/2);
+        for i = 1:n
+            base = (i-1)*n-sum(0:i-2);
+            incol = 1;
+            xx(i,base+incol) = 2*x(i);
+            for j = i+1:n
+                incol = incol+1;
+                xx(i,incol) = 2*x(j);
+            end
+            for j=1:i-1
+                base = (j-1)*n-sum(0:j-2)+1;
+                colid = base+i-j;
+                xx(i,colid) = 2*x(j);
+            end
+        end
+    end
+
+    return % --*-- Unit tests --*--
+
+    %@test:1
+    % Create data
+    X = randn(10,1000);
+    Y = 1 + rand(1,10)*X.^2+0.01*randn(1,1000);
+
+    % Perform approximation
+    try
+        [f,df, d2f,R2] = likelihood_quadratic_approximation(X,Y);
+        t(1) = true;
+    catch
+        t(1) = false;
+    end
+
+    % Test returned arguments
+    if t(1)
+        try
+            y = f(randn(10,100));
+            t(2) = true;
+            if ~(rows(y)==100 && columns(y)==1)
+                t(2) = false;
+            end
+        catch
+            t(2) = false;
+        end
+        try
+            dy = df(zeros(10,1));
+            t(3) = true;
+            if ~(rows(dy)==10 && columns(dy)==1)
+                t(3) = false;
+            end
+        catch
+            t(3) = false;
+        end
+        t(4) = true;
+        if ~(rows(d2f)==10 && columns(d2f)==10)
+            t(4) = false
+        end
+        if ~(rows(d2f)==10 && columns(d2f)==10)
+            t(4) = false
+        end
+        t(4) = issymmetric(d2f);
+        t(4) = ispd(d2f);
+        t(5) = isscalar(R2);
+        t(5) = (R2>0) & (R2<1); % Note that in a nonlinear model nothing ensures that these inequalities are satisfied.
+    end
+
+    T = all(t);
+    %@eof:1
+
+end

matlab/estimation/selec_posterior_draws.m

@@ -26,7 +26,7 @@ function SampleAddress = selec_posterior_draws(M_,options_,dr,endo_steady_state,
 %   None.
 %
 
-% Copyright © 2006-2024 Dynare Team
+% Copyright © 2006-2022 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -146,7 +146,7 @@ if info
             pdraws(linee,1) = {x2(SampleAddress(i,4),:)};
             if info==2
                 M_ = set_parameters_locally(M_,pdraws{i,1});
-                [dr,~,M_.params] = compute_decision_rules(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
+                [dr,~,M_.params] = compute_decision_rules(M_,options_,oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
                 pdraws(linee,2) = { dr };
             end
             old_mhfile = mhfile;

matlab/latex/collect_latex_files.m

@@ -35,7 +35,7 @@ fprintf(fid,'%s \n','\usepackage{psfrag}');
 fprintf(fid,'%s \n','\usepackage{graphicx}');
 fprintf(fid,'%s \n','\usepackage{epstopdf}');
 fprintf(fid,'%s \n','\usepackage{longtable,booktabs}');
-fprintf(fid,'%s \n','\usepackage{amsmath,amsfonts,amssymb}');
+fprintf(fid,'%s \n','\usepackage{amsmath,amsfonts}');
 fprintf(fid,'%s \n','\usepackage{breqn}');
 fprintf(fid,'%s \n','\usepackage{float,morefloats,caption}');
 fprintf(fid,'%s \n','\begin{document}');

matlab/model_info.m

@@ -280,10 +280,20 @@ function print_line(names,var_index,lead_lag,M_)
     else
         aux_index=find([M_.aux_vars(:).endo_index]==var_index);
         aux_type=M_.aux_vars(aux_index).type;
-        if lead_lag==0
+        if ~isfield(M_.aux_vars(aux_index),'orig_lead_lag') || isempty(M_.aux_vars(aux_index).orig_lead_lag)
-            str = subst_auxvar(var_index, [], M_);
+            if ismember(aux_type,[1,3])
+                str = subst_auxvar(var_index, -1, M_);
+            elseif ismember(aux_type,[0,2])
+                str = subst_auxvar(var_index, 1, M_);
+            else
+                if lead_lag==0
+                    str = subst_auxvar(var_index, [], M_);
+                else
+                    str = subst_auxvar(var_index, lead_lag, M_);
+                end
+            end
         else
-            str = subst_auxvar(var_index, lead_lag, M_);
+            str = subst_auxvar(var_index, M_.aux_vars(aux_index).orig_lead_lag, M_);
         end
         aux_orig_expression=M_.aux_vars(aux_index).orig_expr;
         if isempty(aux_orig_expression)

matlab/moments/compute_variance_decomposition.m

@@ -56,7 +56,7 @@ else
         var_decomp(stationary_vars,i) = vx2;
         variance_sum_loop = variance_sum_loop +vx2; %track overall variance over shocks
     end
-    if ~options_.pruning && max(abs(variance_sum_loop-var_stationary)./var_stationary) > 1e-4 && max(abs(variance_sum_loop-var_stationary))>1e-7
+    if ~options_.pruning && max(abs(variance_sum_loop-var_stationary)./var_stationary) > 1e-4
         warning(['Aggregate variance and sum of variances by shocks ' ...
             'differ by more than 0.01 %'])
     end

matlab/optimal_policy/dyn_ramsey_static.m

@@ -25,7 +25,7 @@ function [steady_state, params, check] = dyn_ramsey_static(ys_init, exo_ss, M_,
 % SPECIAL REQUIREMENTS
 %    none
 
-% Copyright © 2003-2024 Dynare Team
+% Copyright © 2003-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -137,7 +137,7 @@ end
 % Compute the value of the Lagrange multipliers that minimizes the norm of the
 % residuals, given the other endogenous
 if options_.bytecode
-    res = bytecode('static', M_, options_, xx, exo_ss, M_.params, 'evaluate');
+    res = bytecode('static', M_, options, xx, exo_ss, M_.params, 'evaluate');
 else
     res = feval([M_.fname '.sparse.static_resid'], xx, exo_ss, M_.params);
 end
@@ -167,7 +167,7 @@ end
 function result = check_static_model(ys,exo_ss,M_,options_)
 result = false;
 if (options_.bytecode)
-    res = bytecode('static', M_, options_, ys, exo_ss, M_.params, 'evaluate');
+    res = bytecode('static', M_, options, ys, exo_ss, M_.params, 'evaluate');
 else
     res = feval([M_.fname '.sparse.static_resid'], ys, exo_ss, M_.params);
 end

matlab/optimal_policy/evaluate_planner_objective.m

@@ -59,7 +59,7 @@ function planner_objective_value = evaluate_planner_objective(M_,options_,oo_)
 
 % In the deterministic case, resorting to approximations for welfare is no longer required as it is possible to simulate the model given initial conditions for pre-determined variables and terminal conditions for forward-looking variables, whether these initial and terminal conditions are explicitly or implicitly specified. Assuming that the number of simulated periods is high enough for the new steady-state to be reached, the new unconditional welfare is thus the last period's welfare. As for the conditional welfare, it can be derived using backward recursions on the equation W = U + beta*W(+1) starting from the final unconditional steady-state welfare.
 
-% Copyright © 2007-2024 Dynare Team
+% Copyright © 2007-2022 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -92,11 +92,11 @@ end
 
 if options_.ramsey_policy && oo_.gui.ran_perfect_foresight
     T = size(oo_.endo_simul,2);
-    U_term = feval([M_.fname '.objective.sparse.static_resid'], oo_.endo_simul(:,T-M_.maximum_lead), oo_.exo_simul(T-M_.maximum_lead,:), M_.params);
+    [U_term] = feval([M_.fname '.objective.static'],oo_.endo_simul(:,T-M_.maximum_lead),oo_.exo_simul(T-M_.maximum_lead,:), M_.params);
     EW = U_term/(1-beta);
     W = EW;
     for t=T-M_.maximum_lead:-1:1+M_.maximum_lag
-        U = feval([M_.fname '.objective.sparse.static_resid'], oo_.endo_simul(:,t), oo_.exo_simul(t,:), M_.params);
+        [U] = feval([M_.fname '.objective.static'],oo_.endo_simul(:,t),oo_.exo_simul(t,:), M_.params);
         W = U + beta*W;
     end
     planner_objective_value = struct('conditional', W, 'unconditional', EW);
@@ -108,8 +108,7 @@ else
         ys = oo_.dr.ys;
     end
     if options_.order == 1 && ~options_.discretionary_policy
-        [U, T_order, T] = feval([M_.fname '.objective.sparse.static_resid'], ys, zeros(1,exo_nbr), M_.params);
+        [U,Uy] = feval([M_.fname '.objective.static'],ys,zeros(1,exo_nbr), M_.params);
-        Uy = feval([M_.fname '.objective.sparse.static_g1'], ys, zeros(1,exo_nbr), M_.params, M_.objective_g1_sparse_rowval, M_.objective_g1_sparse_colval, M_.objective_g1_sparse_colptr, T_order, T);
 
         Gy = dr.ghx(nstatic+(1:nspred),:);
         Gu = dr.ghu(nstatic+(1:nspred),:);
@@ -138,9 +137,7 @@ else
         planner_objective_value.conditional.zero_initial_multiplier = W_L_0;
 
     elseif options_.order == 2 && ~M_.hessian_eq_zero %full second order approximation
-        [U, T_order, T] = feval([M_.fname '.objective.sparse.static_resid'], ys, zeros(1,exo_nbr), M_.params);
+        [U,Uy,Uyy] = feval([M_.fname '.objective.static'],ys,zeros(1,exo_nbr), M_.params);
-        [Uy, T_order, T] = feval([M_.fname '.objective.sparse.static_g1'], ys, zeros(1,exo_nbr), M_.params, M_.objective_g1_sparse_rowval, M_.objective_g1_sparse_colval, M_.objective_g1_sparse_colptr, T_order, T);
-        Uyy_v = feval([M_.fname '.objective.sparse.static_g2'], ys, zeros(1,exo_nbr), M_.params, T_order, T);
 
         Gy = dr.ghx(nstatic+(1:nspred),:);
         Gu = dr.ghu(nstatic+(1:nspred),:);
@@ -156,7 +153,6 @@ else
         guu(dr.order_var,:) = dr.ghuu;
         gss(dr.order_var,:) = dr.ghs2;
 
-        Uyy = build_two_dim_hessian(M_.objective_g2_sparse_indices, Uyy_v, 1, M_.endo_nbr);
         Uyy = full(Uyy);
 
         Uyygygy = A_times_B_kronecker_C(Uyy,gy,gy);
@@ -232,16 +228,13 @@ else
         planner_objective_value.conditional.steady_initial_multiplier = W_L_SS;
         planner_objective_value.conditional.zero_initial_multiplier = W_L_0;
     elseif (options_.order == 2 && M_.hessian_eq_zero) || options_.discretionary_policy %linear quadratic problem
-        [U, T_order, T] = feval([M_.fname '.objective.sparse.static_resid'], ys, zeros(1,exo_nbr), M_.params);
+        [U,Uy,Uyy] = feval([M_.fname '.objective.static'],ys,zeros(1,exo_nbr), M_.params);
-        [Uy, T_order, T] = feval([M_.fname '.objective.sparse.static_g1'], ys, zeros(1,exo_nbr), M_.params, M_.objective_g1_sparse_rowval, M_.objective_g1_sparse_colval, M_.objective_g1_sparse_colptr, T_order, T);
-        Uyy_v = feval([M_.fname '.objective.sparse.static_g2'], ys, zeros(1,exo_nbr), M_.params, T_order, T);
 
         Gy = dr.ghx(nstatic+(1:nspred),:);
         Gu = dr.ghu(nstatic+(1:nspred),:);
         gy(dr.order_var,:) = dr.ghx;
         gu(dr.order_var,:) = dr.ghu;
 
-        Uyy = build_two_dim_hessian(M_.objective_g2_sparse_indices, Uyy_v, 1, M_.endo_nbr);
         Uyy = full(Uyy);
 
         Uyygygy = A_times_B_kronecker_C(Uyy,gy,gy);
@@ -312,7 +305,7 @@ else
             dr.(['g_' num2str(i)]) = [dr.(['g_' num2str(i)]); W.(['W_' num2str(i)])];
         end
         % Amends the steady-state vector accordingly
-        U = feval([M_.fname '.objective.sparse.static_resid'], ys, zeros(1,exo_nbr), M_.params);
+        [U] = feval([M_.fname '.objective.static'],ys,zeros(1,exo_nbr), M_.params);
         ysteady = [ys(oo_.dr.order_var); U/(1-beta)];
 
         % Generates the sequence of shocks to compute unconditional welfare

matlab/optimization/solve1.m

@@ -123,11 +123,7 @@ for its = 1:maxit
         fjac2=fjac'*fjac;
         temp=max(sum(abs(fjac2)));
         if temp>0
-            if issparse(fjac)
+            p=-(fjac2+sqrt(nn*eps)*temp*eye(nn))\(fjac'*fvec);
-                p=-(fjac2+sqrt(nn*eps)*temp*speye(nn))\(fjac'*fvec);
-            else
-                p=-(fjac2+sqrt(nn*eps)*temp*eye(nn))\(fjac'*fvec);
-            end
         else
             errorflag = true;
             errorcode = 5;

matlab/pac-tools/hVectors.m

@@ -18,7 +18,7 @@ function [h, lrcp] = hVectors(params, H, auxmodel, kind, id)
 %    params(2:end-1) ⟶ Autoregressive parameters.
 %    params(end)     ⟶ Discount factor.
 
-% Copyright © 2018-2024 Dynare Team
+% Copyright © 2018-2021 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -52,21 +52,21 @@ n = length(H);
 tmp = eye(n*m)-kron(G, transpose(H)); % inv(W2)
 
 switch kind
-  case 'll' % (A.84), page 28 in Brayton, Davis and Tulip (2000) ⟹ The target is stationary (level-level).
+  case 'll'
     h = A_1*A_b*((kron(iota(m, m), H))'*(tmp\kron(iota(m, m), iota(n, id))));
-  case 'dd' % (A.79), page 26 in Brayton, Davis and Tulip (2000) ⟹ The target appears in first difference as a dependent variable in the auxiliary model.
+  case 'dd'
     h = A_1*A_b*(kron(iota(m, m)'*inv(eye(m)-G), H')*(tmp\kron(iota(m, m), iota(n, id))));
-  case 'dl' % (A.74), page 24 in Brayton, Davis and Tulip (2000) ⟹ The target appears in level as a dependent variable in the auxiliary model.
+  case 'dl'
     h = A_1*A_b*(kron(iota(m, m)'*inv(eye(m)-G), (H'-eye(length(H))))*(tmp\kron(iota(m, m), iota(n, id))));
   otherwise
     error('Unknown kind value in PAC model.')
 end
 
-if nargout>1
+if nargin>1
     if isequal(kind, 'll')
         lrcp = NaN;
     else
         d = A_1*A_b*(iota(m, m)'*inv((eye(m)-G)*(eye(m)-G))*iota(m, m));
         lrcp = (1-sum(params(2:end-1))-d);
     end
-end
+end

matlab/partial_information/PCL_resol.m

@@ -25,7 +25,7 @@ function [dr,info]=PCL_resol(ys,check_flag)
 % SPECIAL REQUIREMENTS
 %    none
 
-% Copyright © 2001-2024 Dynare Team
+% Copyright © 2001-2017 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -65,13 +65,7 @@ end
 dr.ys = ys;
 check1 = 0;
 % testing for steadystate file
-static_resid = str2func(sprintf('%s.sparse.static_resid', M_.fname));
+fh = str2func([M_.fname '.static']);
-static_g1 = str2func(sprintf('%s.sparse.static_g1', M_.fname));
-function [resid, g1] = static_resid_g1(y, x, params)
-    [resid, T_order, T] = static_resid(y, x, params);
-    g1 = static_g1(y, x, params, M_.static_g1_sparse_rowval, M_.static_g1_sparse_colval, M_.static_g1_sparse_colptr, T_order, T);
-end
-
 if options_.steadystate_flag
     [dr.ys,check1] = feval([M_.fname '_steadystate'],dr.ys,...
                            [oo_.exo_steady_state; ...
@@ -93,17 +87,17 @@ else
     if ~options_.ramsey_policy
         if options_.linear == 0
             % nonlinear models
-            if max(abs(static_resid_g1(dr.ys, [oo_.exo_steady_state; ...
+            if max(abs(feval(fh,dr.ys,[oo_.exo_steady_state; ...
-                                               oo_.exo_det_steady_state], M_.params))) > options_.solve_tolf
+                                    oo_.exo_det_steady_state], M_.params))) > options_.solve_tolf
                 opt = options_;
                 opt.jacobian_flag = false;
-                [dr.ys, check1] = dynare_solve(static_resid_g1, dr.ys, options.steady_.maxit, options_.solve_tolf, options_.solve_tolx, ...
+                [dr.ys, check1] = dynare_solve(fh,dr.ys, options.steady_.maxit, options_.solve_tolf, options_.solve_tolx, ...
                                                opt, [oo_.exo_steady_state; oo_.exo_det_steady_state], M_.params);
             end
         else
             % linear models
-            [fvec,jacob] = static_resid_g1(dr.ys, [oo_.exo_steady_state;...
+            [fvec,jacob] = feval(fh,dr.ys,[oo_.exo_steady_state;...
-                                                   oo_.exo_det_steady_state], M_.params);
+                                oo_.exo_det_steady_state], M_.params);
             if max(abs(fvec)) > 1e-12
                 dr.ys = dr.ys-jacob\fvec;
             end
@@ -117,7 +111,7 @@ if check1
         resid = check1 ;
     else
         info(1)= 20;
-        resid = static_resid(ys, oo_.exo_steady_state, M_.params);
+        resid = feval(fh,ys,oo_.exo_steady_state, M_.params);
     end
     info(2) = resid'*resid ;
     return
@@ -146,8 +140,6 @@ end
 oo_.exo_simul = tempex;
 tempex = [];
 
-end
-
 % 01/01/2003 MJ added dr_algo == 1
 % 08/24/2001 MJ uses Schmitt-Grohe and Uribe (2001) constant correction
 %               in dr.ghs2

matlab/partial_information/add_auxiliary_variables_to_steadystate.m

@@ -2,7 +2,7 @@ function ys1 = add_auxiliary_variables_to_steadystate(ys,aux_vars,fname, ...
                                                   exo_steady_state, exo_det_steady_state,params, byte_code)
 % Add auxiliary variables to the steady state vector
 
-% Copyright © 2009-2024 Dynare Team
+% Copyright © 2009-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -30,7 +30,7 @@ for i=1:n+1
                        [exo_steady_state; ...
                         exo_det_steady_state],params);
     else
-        res = feval([fname '.sparse.static_resid'], ys1,...
+        res = feval([fname '.static'],ys1,...
                     [exo_steady_state; ...
                      exo_det_steady_state],params);
     end

matlab/perfect-foresight-models/perfect_foresight_solver.m

@@ -23,7 +23,7 @@ function [oo_, ts]=perfect_foresight_solver(M_, options_, oo_, no_error_if_learn
 % SPECIAL REQUIREMENTS
 %   none
 
-% Copyright © 1996-2024 Dynare Team
+% Copyright © 1996-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -55,7 +55,7 @@ end
 periods = options_.periods;
 
 if options_.debug
-    model_static = str2func([M_.fname,'.sparse.static_resid']);
+    model_static = str2func([M_.fname,'.static']);
     for ii=1:size(oo_.exo_simul,1)
         [residual(:,ii)] = model_static(oo_.steady_state, oo_.exo_simul(ii,:),M_.params);
     end

matlab/perfect-foresight-models/solve_block_decomposed_problem.m

@@ -14,7 +14,7 @@ function [y, success, maxerror, per_block_status] = solve_block_decomposed_probl
 %   maxerror         [double]    ∞-norm of the residual
 %   per_block_status [struct]    vector structure with per-block information about convergence
 
-% Copyright © 2020-2024 Dynare Team
+% Copyright © 2020-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -33,21 +33,18 @@ function [y, success, maxerror, per_block_status] = solve_block_decomposed_probl
 
 cutoff = 1e-15;
 
-switch options_.stack_solve_algo
+if options_.stack_solve_algo==0
-    case 0
+    mthd='Sparse LU';
-        mthd='Sparse LU on stacked system';
+elseif options_.stack_solve_algo==1 || options_.stack_solve_algo==6
-    case {1,6}
+    mthd='LBJ';
-        mthd='LBJ with LU solver';
+elseif options_.stack_solve_algo==2
-    case 2
+    mthd='GMRES';
-        mthd='GMRES on stacked system';
+elseif options_.stack_solve_algo==3
-    case 3
+    mthd='BICGSTAB';
-        mthd='BiCGStab on stacked system';
+elseif options_.stack_solve_algo==4
-    case 4
+    mthd='OPTIMPATH';
-        mthd='Sparse LU solver with optimal path length on stacked system';
+else
-    case 7
+    mthd='UNKNOWN';
-        mthd='Solver from solve_algo option on stacked system';
-    otherwise
-        error('Unsupported stack_solve_algo value')
 end
 if options_.verbosity
     printline(41)

matlab/send_endogenous_variables_to_workspace.m

@@ -20,5 +20,5 @@ function send_endogenous_variables_to_workspace()
 global M_ oo_
 
 for idx = 1:M_.endo_nbr
-    assignin('base', M_.endo_names{idx}, oo_.endo_simul(idx,:)')
+    assignin('base', M_.endo_names{idx}, oo_.endo_simul(idx,:))
 end

matlab/send_exogenous_variables_to_workspace.m

@@ -21,5 +21,5 @@ function send_exogenous_variables_to_workspace()
 global M_ oo_
 
 for idx = 1:M_.exo_nbr
-    assignin('base', M_.exo_names{idx}, oo_.exo_simul(:,idx)')
+    assignin('base', M_.exo_names{idx}, oo_.exo_simul(:,idx))
 end

matlab/shock_decomposition/annualized_shock_decomposition.m

@@ -129,7 +129,7 @@ if realtime_==0
         myopts=options_;
         myopts.plot_shock_decomp.type='qoq';
         myopts.plot_shock_decomp.realtime=0;
-        z = plot_shock_decomposition(M_,oo_,myopts,[],true);
+        z = plot_shock_decomposition(M_,oo_,myopts,[]);
     else
         z = oo_;
     end
@@ -139,7 +139,7 @@ if realtime_==0
             myopts=options_;
             myopts.plot_shock_decomp.type='qoq';
             myopts.plot_shock_decomp.realtime=0;
-            [y_aux, steady_state_aux] = plot_shock_decomposition(M_,oo_,myopts,aux.y,true);
+            [y_aux, steady_state_aux] = plot_shock_decomposition(M_,oo_,myopts,aux.y);
             aux.y=y_aux;
             aux.yss=steady_state_aux;
         end
@@ -158,13 +158,13 @@ if realtime_ && isstruct(oo_) && isfield(oo_, 'realtime_shock_decomposition')
         myopts.plot_shock_decomp.realtime=1;
         myopts.plot_shock_decomp.vintage=i;
         % retrieve quarterly shock decomp
-        z = plot_shock_decomposition(M_,oo_,myopts,[],true);
+        z = plot_shock_decomposition(M_,oo_,myopts,[]);
         zdim = size(z);
         z = z(i_var,:,:);
         if isstruct(aux)
             if ischar(aux0.y)
                 % retrieve quarterly shock decomp for aux variable
-                [y_aux, steady_state_aux] = plot_shock_decomposition(M_,oo_,myopts,aux0.y,true);
+                [y_aux, steady_state_aux] = plot_shock_decomposition(M_,oo_,myopts,aux0.y);
                 aux.y=y_aux;
                 aux.yss=steady_state_aux;
             end
@@ -185,13 +185,13 @@ if realtime_ && isstruct(oo_) && isfield(oo_, 'realtime_shock_decomposition')
             if qvintage_>i-4 && qvintage_<i
                 myopts.plot_shock_decomp.vintage=qvintage_;
                 % retrieve quarterly shock decomp
-                z = plot_shock_decomposition(M_,oo_,myopts,[],true);
+                z = plot_shock_decomposition(M_,oo_,myopts,[]);
                 z(:,:,end+1:zdim(3))=nan; % fill with nan's remaining time points to reach Q4
                 z = z(i_var,:,:);
                 if isstruct(aux)
                     if ischar(aux0.y)
                         % retrieve quarterly shock decomp for aux variable
-                        [y_aux, steady_state_aux] = plot_shock_decomposition(M_,oo_,myopts,aux0.y,true);
+                        [y_aux, steady_state_aux] = plot_shock_decomposition(M_,oo_,myopts,aux0.y);
                         y_aux(:,:,end+1:zdim(3))=nan; % fill with nan's remaining time points to reach Q4
                         aux.y=y_aux;
                         aux.yss=steady_state_aux;

matlab/shock_decomposition/plot_shock_decomposition.m

@@ -1,4 +1,4 @@
-function [out, steady_state] = plot_shock_decomposition(M_,oo_,options_,varlist,get_decomp_only)
+function [out, steady_state] = plot_shock_decomposition(M_,oo_,options_,varlist)
 % function plot_shock_decomposition(M_,oo_,options_,varlist)
 % Plots the results of shock_decomposition
 %
@@ -7,12 +7,11 @@ function [out, steady_state] = plot_shock_decomposition(M_,oo_,options_,varlist,
 %    oo_:         [structure]  Storage of results
 %    options_:    [structure]  Options
 %    varlist:     [char]       List of variables
-%    get_decomp_only    [bool] indicator on whether to only return with
+%
-%                               basic decomposition (required for e.g. annualized_shock_decomposition)
 % SPECIAL REQUIREMENTS
 %    none
 
-% Copyright © 2016-2023 Dynare Team
+% Copyright © 2016-2019 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -29,10 +28,6 @@ function [out, steady_state] = plot_shock_decomposition(M_,oo_,options_,varlist,
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
-if nargin<5
-    get_decomp_only=false;
-end
-
 options_.nodisplay = options_.plot_shock_decomp.nodisplay;
 options_.graph_format = options_.plot_shock_decomp.graph_format;
 
@@ -537,7 +532,7 @@ if steadystate
     options_.plot_shock_decomp.steady_state=steady_state;
 end
 
-if get_decomp_only
+if nargout == 2
     out=z(i_var,:,:);
     steady_state = steady_state(i_var);
     return

matlab/stochastic_solver/k_order_pert.m

@@ -1,7 +1,7 @@
 function [dr,info] = k_order_pert(dr,M_,options_)
 % Compute decision rules using the k-order DLL from Dynare++
 
-% Copyright © 2009-2024 Dynare Team
+% Copyright © 2009-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -30,16 +30,6 @@ if M_.maximum_endo_lead == 0 && order>1
            'backward models'])
 end
 
-if options_.aim_solver
-    error('Option aim_solver is not compatible with k_order_solver')
-end
-if options_.dr_cycle_reduction
-    error('Option dr=cycle_reduction is not compatible with k_order_solver')
-end
-if options_.dr_logarithmic_reduction
-    error('Option dr=logarithmic_reduction is not compatible with k_order_solver')
-end
-
 try
     [dynpp_derivs, dyn_derivs] = k_order_perturbation(dr,M_,options_);
 catch ME

matlab/utilities/general/display_parameter_values.m

@@ -1,25 +0,0 @@
-function display_parameter_values
-
-% Copyright © 2024 Dynare Team
-%
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
-global M_
-my_title='Current parameter values:';
-labels = M_.param_names;
-headers = {'Parameter'; 'Value'};
-lh = cellofchararraymaxlength(labels)+2;
-options_.noprint=false;
-dyntable(options_, my_title, headers, labels, M_.params, lh, 10, 6);

matlab/utilities/general/ispd.m

@@ -1,30 +1,15 @@
 function [test, penalty] = ispd(A)
 
-%@info:
+% Test if the square matrix A is positive definite.
-%! @deftypefn {Function File} {[@var{test}, @var{penalty}  =} ispd (@var{A})
+%
-%! @anchor{ispd}
+% INPUTS
-%! @sp 1
+% - A       [double]   n×n matrix.
-%! Tests if the square matrix @var{A} is positive definite.
+%
-%! @sp 2
+% OUTPUTS
-%! @strong{Inputs}
+% - test    [logical]  scalar, true if and only if matrix A is positive definite (and symmetric...)
-%! @sp 1
+% - penalty [double]   scalar, absolute value of the uum of the negative eigenvalues of A. This output argument is optional.
-%! @table @ @var
-%! @item A
-%! A square matrix.
-%! @end table
-%! @sp 2
-%! @strong{Outputs}
-%! @sp 1
-%! @table @ @var
-%! @item test
-%! Integer scalar equal to 1 if @var{A} is a positive definite sqquare matrix, 0 otherwise.
-%! @item penalty
-%! Absolute value of the uum of the negative eigenvalues of A. This output argument is optional.
-%! @end table
-%! @end deftypefn
-%@eod:
 
-% Copyright © 2007-2022 Dynare Team
+% Copyright © 2007-2024 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -54,6 +39,7 @@ if nargout>1
         if isoctave && any(any(~isfinite(A))) % workaround for https://savannah.gnu.org/bugs/index.php?63082
             penalty = 1;
         else
+            % TODO: the penalty is only concerned with negative eigenvalues, we should also consider the case of non symmetric matrix A.
             a = diag(eig(A));
             k = find(a<0);
             if k > 0
@@ -61,4 +47,4 @@ if nargout>1
             end
         end
     end
-end
+end

meson.build

@@ -21,14 +21,9 @@ c_compiler = meson.get_compiler('c')
 
 subdir('preprocessor/src')
 
-# NB: the following two symlinks my be improved if this wishlist item is done:
-#  https://github.com/mesonbuild/meson/issues/11519
-
-install_symlink('dynare-preprocessor', install_dir : 'lib/dynare/preprocessor/',
-                pointing_to : '../../../bin/dynare-preprocessor'
-                              + (host_machine.system() == 'windows' ? '.exe' : ''))
-
 # Compatibility symlink
+# NB: the following my be improved if this wishlist item is done:
+#  https://github.com/mesonbuild/meson/issues/11519
 install_symlink('dynare_m' + (host_machine.system() == 'windows' ? '.exe' : ''),
                 install_dir : 'lib/dynare/matlab/preprocessor64',
                 pointing_to : '../../../../bin/dynare-preprocessor'
@@ -39,12 +34,17 @@ install_symlink('dynare_m' + (host_machine.system() == 'windows' ? '.exe' : ''),
 install_subdir('matlab', install_dir : 'lib/dynare',
                exclude_files : [ 'utilities/tests/.git' ,
                                  'utilities/tests/.gitignore',
-                                 'dseries/.git',
+                                 'modules/reporting/.git',
-                                 'dseries/.gitignore',
+                                 'modules/reporting/.gitignore',
-                                 'dseries/src/modules/matlab-fame-io/.git',
+                                 'modules/reporting/.gitlab-ci.yml',
-                                 'dseries/src/modules/matlab-fame-io/.gitignore',
+                                 'modules/dseries/.git',
-                                 'dseries/.gitmodules',
+                                 'modules/dseries/.gitignore',
-                                 'dseries/.gitlab-ci.yml' ])
+                                 'modules/dseries/src/modules/matlab-fame-io/.git',
+                                 'modules/dseries/src/modules/matlab-fame-io/.gitignore',
+                                 'modules/dseries/src/modules/matlab-fame-io/doc/.gitignore',
+                                 'modules/dseries/src/modules/matlab-fame-io/tests/FameDatabases/.gitignore',
+                                 'modules/dseries/.gitmodules',
+                                 'modules/dseries/.gitlab-ci.yml' ])
 
 sed_exe = find_program('sed')
 custom_target(output : 'dynare_version.m', input : 'matlab/dynare_version.m.in',
@@ -53,9 +53,6 @@ custom_target(output : 'dynare_version.m', input : 'matlab/dynare_version.m.in',
               install : true,
               install_dir : 'lib/dynare/matlab')
 
-install_subdir('contrib/ms-sbvar/TZcode/MatlabFiles',
-               install_dir : 'lib/dynare/contrib/ms-sbvar/TZcode')
-
 ### MEX files
 
 mex_incdir = include_directories('mex/sources')
@@ -160,20 +157,7 @@ if get_option('build_for') == 'matlab'
   umfpack_dep = declare_dependency(link_args : '-lmwumfpack', dependencies : blas_dep)
   ut_dep = declare_dependency(link_args : '-lut')
 
-  # Workaround for Meson bug https://github.com/mesonbuild/meson/issues/12757
+  slicot_dep = declare_dependency(dependencies : [ fortran_compiler.find_library('slicot64_pic'), blas_dep, lapack_dep ])
-  # Use the C compiler as a fallback for detecting SLICOT under Linux with
-  # prefer_static=true (but still try the Fortran compiler to honour the -B
-  # option in fortran_args, as documented). Needed for building the MATLAB
-  # Online package.
-  if get_option('prefer_static') and host_machine.system() == 'linux'
-    slicot_dep_tmp = fortran_compiler.find_library('slicot64_pic', required : false)
-    if not slicot_dep_tmp.found()
-      slicot_dep_tmp = c_compiler.find_library('slicot64_pic')
-    endif
-    slicot_dep = declare_dependency(dependencies : [ slicot_dep_tmp, blas_dep, lapack_dep ])
-  else
-    slicot_dep = declare_dependency(dependencies : [ fortran_compiler.find_library('slicot64_pic'), blas_dep, lapack_dep ])
-  endif
 else # Octave build
   octave_exe = find_program('octave', required : not meson.is_cross_build(), disabler : true)
   mkoctfile_exe = find_program('mkoctfile')
@@ -307,11 +291,8 @@ if get_option('prefer_static')
 
   # NB: constructing a dependency object with link_args : ['-Wl,-Bstatic', '-lgomp', '-Wl,-Bdynamic'] does not work,
   # because it reorders the three arguments and puts -lgomp at the end
-  if host_machine.system() != 'linux'
+  openmp_dep_tmp = cpp_compiler.find_library('gomp', static : true)
-    # Under Debian 12, trying to link (static) libgomp.a in a MEX fails.
+  openmp_dep = declare_dependency(dependencies : [ openmp_dep, openmp_dep_tmp ])
-    openmp_dep_tmp = cpp_compiler.find_library('gomp', static : true)
-    openmp_dep = declare_dependency(dependencies : [ openmp_dep, openmp_dep_tmp ])
-  endif
 endif
 
 # For use when creating intermediate static libraries to be incorporated in MEX files
@@ -1911,9 +1892,7 @@ endforeach
 git_exe = find_program('git', required : false)
 etags_exe = find_program('etags', required : false)
 
-fs = import('fs')
+if git_exe.found() and etags_exe.found()
-
-if fs.is_dir('.git') and git_exe.found() and etags_exe.found()
   all_files = run_command(git_exe,
                           [ '--git-dir=@0@/.git'.format(meson.project_source_root()),
                             'ls-files', '--recurse-submodules',

mex/sources/bytecode/Interpreter.cc

@@ -1,5 +1,5 @@
 /*
- * Copyright © 2007-2024 Dynare Team
+ * Copyright © 2007-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -4710,26 +4710,23 @@ Interpreter::Simulate_Newton_Two_Boundaries(
           switch (stack_solve_algo)
             {
             case 0:
-              mexPrintf("MODEL SIMULATION: (method=Sparse LU solver on stacked system)\n");
+              mexPrintf("MODEL SIMULATION: (method=Sparse LU)\n");
               break;
             case 2:
-              mexPrintf(
+              mexPrintf(preconditioner_print_out("MODEL SIMULATION: (method=GMRES)\n",
-                  preconditioner_print_out("MODEL SIMULATION: (method=GMRES on stacked system)\n",
+                                                 preconditioner, false)
-                                           preconditioner, false)
+                            .c_str());
-                      .c_str());
               break;
             case 3:
-              mexPrintf(preconditioner_print_out(
+              mexPrintf(preconditioner_print_out("MODEL SIMULATION: (method=BiCGStab)\n",
-                            "MODEL SIMULATION: (method=BiCGStab on stacked system)\n",
+                                                 preconditioner, false)
-                            preconditioner, false)
                             .c_str());
               break;
             case 4:
-              mexPrintf("MODEL SIMULATION: (method=Sparse LU solver with optimal path length on "
+              mexPrintf("MODEL SIMULATION: (method=Sparse LU & optimal path length)\n");
-                        "stacked system)\n");
               break;
             case 5:
-              mexPrintf("MODEL SIMULATION: (method=LBJ with Sparse Gaussian Elimination)\n");
+              mexPrintf("MODEL SIMULATION: (method=Sparse Gaussian Elimination)\n");
               break;
             }
         }

mex/sources/bytecode/bytecode.cc

@@ -1,5 +1,5 @@
 /*
- * Copyright © 2007-2024 Dynare Team
+ * Copyright © 2007-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -262,22 +262,6 @@ mexFunction(int nlhs, mxArray* plhs[], int nrhs, const mxArray* prhs[])
       if (!shock_str_date_)
         mexErrMsgTxt(
             "The extended_path description structure does not contain the member: shock_str_date_");
-      mxArray* shock_perfect_foresight_
-          = mxGetField(extended_path_struct, 0, "shock_perfect_foresight_");
-      if (!shock_perfect_foresight_)
-        mexErrMsgTxt("The extended_path description structure does not contain the member: "
-                     "shock_perfect_foresight_");
-
-      // Check that there is no 'perfect_foresight' shocks, which are not implemented
-      double* constrained_pf = mxGetPr(constrained_perfect_foresight_);
-      double* shock_pf = mxGetPr(shock_perfect_foresight_);
-      if (auto is_pf = [](double v) { return v != 0; };
-          any_of(constrained_pf,
-                 constrained_pf + mxGetNumberOfElements(constrained_perfect_foresight_), is_pf)
-          || any_of(shock_pf, shock_pf + mxGetNumberOfElements(shock_perfect_foresight_), is_pf))
-        mexErrMsgTxt(
-            "Shocks of type 'perfect_foresight' are not supported with the bytecode option.");
-
       int nb_constrained = mxGetM(constrained_vars_) * mxGetN(constrained_vars_);
       int nb_controlled = 0;
       mxArray* options_cond_fcst_ = mxGetField(extended_path_struct, 0, "options_cond_fcst_");

mex/sources/k_order_welfare/k_ord_objective.cc

@@ -1,5 +1,5 @@
 /*
- * Copyright © 2021-2024 Dynare Team
+ * Copyright © 2021-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -18,23 +18,26 @@
  */
 
 #include "k_ord_objective.hh"
+#include "objective_abstract_class.hh"
 
 #include <cassert>
 #include <utility>
 
-KordwDynare::KordwDynare(KordpDynare& m, Journal& jr, Vector& inParams,
+KordwDynare::KordwDynare(KordpDynare& m, ConstVector& NNZD_arg, Journal& jr, Vector& inParams,
-                         std::unique_ptr<ObjectiveMFile> objectiveFile_arg,
+                         std::unique_ptr<ObjectiveAC> objectiveFile_arg,
                          const std::vector<int>& dr_order) :
     model {m},
+    NNZD {NNZD_arg},
     journal {jr},
     params {inParams},
     resid(1),
     ud {1},
     objectiveFile {std::move(objectiveFile_arg)}
 {
+  dynppToDyn = dr_order;
   dynToDynpp.resize(model.ny());
   for (int i = 0; i < model.ny(); i++)
-    dynToDynpp[dr_order[i]] = i;
+    dynToDynpp[dynppToDyn[i]] = i;
 }
 
 void
@@ -43,10 +46,71 @@ KordwDynare::calcDerivativesAtSteady()
 
   assert(ud.begin() == ud.end());
 
+  std::vector<TwoDMatrix> dyn_ud;     // Planner's objective derivatives, in Dynare form
+  dyn_ud.emplace_back(1, model.ny()); // Allocate Jacobian
+  dyn_ud.back().zeros();
+
+  for (int i = 2; i <= model.order(); i++)
+    {
+      // Higher order derivatives, as sparse (3-column) matrices
+      dyn_ud.emplace_back(static_cast<int>(NNZD[i - 1]), 3);
+      dyn_ud.back().zeros();
+    }
+
   Vector xx(model.nexog());
   xx.zeros();
   resid.zeros();
-  objectiveFile->eval(model.getSteady(), xx, params, resid, dynToDynpp, ud);
+  objectiveFile->eval(model.getSteady(), xx, params, resid, dyn_ud);
+
+  for (int i = 1; i <= model.order(); i++)
+    populateDerivativesContainer(dyn_ud, i);
+}
+
+void
+KordwDynare::populateDerivativesContainer(const std::vector<TwoDMatrix>& dyn_ud, int ord)
+{
+  const TwoDMatrix& u = dyn_ud[ord - 1];
+
+  // utility derivatives FSSparseTensor instance
+  auto udTi = std::make_unique<FSSparseTensor>(ord, model.ny(), 1);
+
+  IntSequence s(ord, 0);
+
+  if (ord == 1)
+    for (int i = 0; i < u.ncols(); i++)
+      {
+        for (int j = 0; j < u.nrows(); j++)
+          {
+            double x = u.get(j, dynppToDyn[s[0]]);
+            if (x != 0.0)
+              udTi->insert(s, j, x);
+          }
+        s[0]++;
+      }
+  else // ord ≥ 2
+    for (int i = 0; i < u.nrows(); i++)
+      {
+        int j = static_cast<int>(u.get(i, 0)) - 1;
+        int i1 = static_cast<int>(u.get(i, 1)) - 1;
+        if (j < 0 || i1 < 0)
+          continue; // Discard empty entries (see comment in DynamicModelAC::unpackSparseMatrix())
+
+        for (int k = 0; k < ord; k++)
+          {
+            s[k] = dynToDynpp[i1 % model.ny()];
+            i1 /= model.ny();
+          }
+
+        if (ord == 2 && !s.isSorted())
+          continue; // Skip symmetric elements (only needed at order 2)
+        else if (ord > 2)
+          s.sort(); // For higher order, canonicalize the multi-index
+
+        double x = u.get(i, 2);
+        udTi->insert(s, j, x);
+      }
+
+  ud.insert(std::move(udTi));
 }
 
 template<>

mex/sources/k_order_welfare/k_ord_objective.hh

@@ -1,5 +1,5 @@
 /*
- * Copyright © 2021-2024 Dynare Team
+ * Copyright © 2021-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -21,7 +21,7 @@
 #define K_ORD_OBJECTIVE_HH
 
 #include "k_ord_dynare.hh"
-#include "objective_m.hh"
+#include "objective_abstract_class.hh"
 
 class KordwDynare;
 
@@ -29,19 +29,22 @@ class KordwDynare
 {
 public:
   KordpDynare& model;
+  const ConstVector& NNZD;
 
 private:
   Journal& journal;
   Vector& params;
   Vector resid;
   TensorContainer<FSSparseTensor> ud; // planner's objective derivatives, in Dynare++ form
+  std::vector<int> dynppToDyn;        // Maps Dynare++ jacobian variable indices to Dynare ones
   std::vector<int> dynToDynpp;        // Maps Dynare jacobian variable indices to Dynare++ ones
-  std::unique_ptr<ObjectiveMFile> objectiveFile;
+  std::unique_ptr<ObjectiveAC> objectiveFile;
 
 public:
-  KordwDynare(KordpDynare& m, Journal& jr, Vector& inParams,
+  KordwDynare(KordpDynare& m, ConstVector& NNZD_arg, Journal& jr, Vector& inParams,
-              std::unique_ptr<ObjectiveMFile> objectiveFile_arg, const std::vector<int>& varOrder);
+              std::unique_ptr<ObjectiveAC> objectiveFile_arg, const std::vector<int>& varOrder);
   void calcDerivativesAtSteady();
+  void populateDerivativesContainer(const std::vector<TwoDMatrix>& dyn_ud, int ord);
   [[nodiscard]] const TensorContainer<FSSparseTensor>&
   getPlannerObjDerivatives() const
   {

mex/sources/k_order_welfare/k_order_welfare.cc

@@ -1,5 +1,5 @@
 /*
- * Copyright © 2021-2024 Dynare Team
+ * Copyright © 2021-2022 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -31,7 +31,6 @@
 
 #include <algorithm>
 #include <cassert>
-#include <string>
 
 #include "dynmex.h"
 
@@ -88,8 +87,8 @@ The routine proceeds in several steps:
    to the approxAtSteady method in the ApproximationWelfare class carries out the necessary
    operation
    - Importing the derivatives of the felicity function with the calcDerivativesAtSteady() method of
-     the KordwDynare class. It relies on the MATLAB-generated files, which are handled by the
+     the KordwDynare class. It relies on the Matlab-generated files, which are handled by the
-     ObjectiveMFile class
+     ObjectiveAC and ObjectiveMFile classes
    - Pinpointing the derivatives of the felicity and welfare functions. The performStep method of
      the KOrderWelfare class carries out the calculations,resorting to the FaaDiBruno class and its
      methods to get the needed intermediary results.
@@ -215,6 +214,15 @@ extern "C"
       mexErrMsgTxt("The derivatives were not computed for the required order. Make sure that you "
                    "used the right order option inside the `stoch_simul' command");
 
+    const mxArray* nnzderivatives_obj_mx = mxGetField(M_mx, 0, "NNZDerivatives_objective");
+    if (!(nnzderivatives_obj_mx && mxIsDouble(nnzderivatives_obj_mx)
+          && !mxIsComplex(nnzderivatives_obj_mx) && !mxIsSparse(nnzderivatives_obj_mx)))
+      mexErrMsgTxt("M_.NNZDerivatives should be a real dense array");
+    ConstVector NNZD_obj {nnzderivatives_obj_mx};
+    if (NNZD.length() < kOrder || NNZD_obj[kOrder - 1] == -1)
+      mexErrMsgTxt("The derivatives were not computed for the required order. Make sure that you "
+                   "used the right order option inside the `stoch_simul' command");
+
     const mxArray* endo_names_mx = mxGetField(M_mx, 0, "endo_names");
     if (!(endo_names_mx && mxIsCell(endo_names_mx)
           && mxGetNumberOfElements(endo_names_mx) == static_cast<size_t>(nEndo)))
@@ -253,32 +261,6 @@ extern "C"
     std::transform(mxGetPr(order_var_mx), mxGetPr(order_var_mx) + nEndo, dr_order.begin(),
                    [](double x) { return static_cast<int>(x) - 1; });
 
-    const mxArray* objective_g1_sparse_rowval_mx
-        = mxGetField(M_mx, 0, "objective_g1_sparse_rowval");
-    if (!(objective_g1_sparse_rowval_mx && mxIsInt32(objective_g1_sparse_rowval_mx)))
-      mexErrMsgTxt("M_.objective_g1_sparse_rowval should be an int32 array");
-
-    const mxArray* objective_g1_sparse_colval_mx
-        = mxGetField(M_mx, 0, "objective_g1_sparse_colval");
-    if (!(objective_g1_sparse_colval_mx && mxIsInt32(objective_g1_sparse_colval_mx)))
-      mexErrMsgTxt("M_.objective_g1_sparse_colval should be an int32 array");
-
-    const mxArray* objective_g1_sparse_colptr_mx
-        = mxGetField(M_mx, 0, "objective_g1_sparse_colptr");
-    if (!(objective_g1_sparse_colptr_mx && mxIsInt32(objective_g1_sparse_colptr_mx)))
-      mexErrMsgTxt("M_.objective_g1_sparse_colptr should be an int32 array");
-
-    std::vector<const mxArray*> objective_gN_sparse_indices;
-    for (int o {2}; o <= kOrder; o++)
-      {
-        using namespace std::string_literals;
-        auto fieldname {"objective_g"s + std::to_string(o) + "_sparse_indices"};
-        const mxArray* indices = mxGetField(M_mx, 0, fieldname.c_str());
-        if (!(indices && mxIsInt32(indices)))
-          mexErrMsgTxt(("M_."s + fieldname + " should be an int32 array").c_str());
-        objective_gN_sparse_indices.push_back(indices);
-      }
-
     const int nSteps
         = 0; // Dynare++ solving steps, for time being default to 0 = deterministic steady state
 
@@ -309,14 +291,21 @@ extern "C"
     // run stochastic steady
     app.walkStochSteady();
 
+    const mxArray* objective_tmp_nbr_mx = mxGetField(M_mx, 0, "objective_tmp_nbr");
+    if (!(objective_tmp_nbr_mx && mxIsDouble(objective_tmp_nbr_mx)
+          && !mxIsComplex(objective_tmp_nbr_mx) && !mxIsSparse(objective_tmp_nbr_mx)
+          && mxGetNumberOfElements(objective_tmp_nbr_mx) >= static_cast<size_t>(kOrder + 1)))
+      mexErrMsgTxt("M_.objective_tmp_nbr should be a real dense array with strictly more elements "
+                   "than the order of derivation");
+    int ntt_objective = std::accumulate(mxGetPr(objective_tmp_nbr_mx),
+                                        mxGetPr(objective_tmp_nbr_mx) + kOrder + 1, 0);
+
     // Getting derivatives of the planner's objective function
-    std::unique_ptr<ObjectiveMFile> objectiveFile;
+    std::unique_ptr<ObjectiveAC> objectiveFile;
-    objectiveFile = std::make_unique<ObjectiveMFile>(
+    objectiveFile = std::make_unique<ObjectiveMFile>(fname, ntt_objective);
-        fname, kOrder, objective_g1_sparse_rowval_mx, objective_g1_sparse_colval_mx,
-        objective_g1_sparse_colptr_mx, objective_gN_sparse_indices);
 
     // make KordwDynare object
-    KordwDynare welfare(dynare, journal, modParams, std::move(objectiveFile), dr_order);
+    KordwDynare welfare(dynare, NNZD_obj, journal, modParams, std::move(objectiveFile), dr_order);
 
     // construct main K-order approximation class of welfare
     ApproximationWelfare appwel(welfare, discount_factor, app.get_rule_ders(),

mex/sources/k_order_welfare/objective_abstract_class.hh

@@ -0,0 +1,38 @@
+/*
+ * Copyright © 2021-2023 Dynare Team
+ *
+ * This file is part of Dynare.
+ *
+ * Dynare is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * Dynare is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
+ */
+
+#ifndef OBJECTIVE_ABSTRACT_CLASS_HH
+#define OBJECTIVE_ABSTRACT_CLASS_HH
+
+#include <vector>
+
+#include "twod_matrix.hh"
+
+class ObjectiveAC
+{
+protected:
+  int ntt; // Size of vector of temporary terms
+public:
+  ObjectiveAC(int ntt_arg) : ntt {ntt_arg} {};
+  virtual ~ObjectiveAC() = default;
+  virtual void eval(const Vector& y, const Vector& x, const Vector& params, Vector& residual,
+                    std::vector<TwoDMatrix>& md)
+      = 0;
+};
+#endif

mex/sources/k_order_welfare/objective_m.cc

@@ -1,5 +1,5 @@
 /*
- * Copyright © 2021-2024 Dynare Team
+ * Copyright © 2021-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -26,41 +26,88 @@
 
 #include "objective_m.hh"
 
-ObjectiveMFile::ObjectiveMFile(const std::string& modName, int kOrder_arg,
+ObjectiveMFile::ObjectiveMFile(const std::string& modName, int ntt_arg) :
-                               const mxArray* objective_g1_sparse_rowval_mx_arg,
+    ObjectiveAC(ntt_arg), ObjectiveMFilename {modName + ".objective.static"}
-                               const mxArray* objective_g1_sparse_colval_mx_arg,
-                               const mxArray* objective_g1_sparse_colptr_mx_arg,
-                               const std::vector<const mxArray*> objective_gN_sparse_indices_arg) :
-    ObjectiveMFilename {modName + ".objective.sparse.static"},
-    kOrder {kOrder_arg},
-    objective_g1_sparse_rowval_mx {objective_g1_sparse_rowval_mx_arg},
-    objective_g1_sparse_colval_mx {objective_g1_sparse_colval_mx_arg},
-    objective_g1_sparse_colptr_mx {objective_g1_sparse_colptr_mx_arg},
-    objective_gN_sparse_indices {objective_gN_sparse_indices_arg}
 {
 }
 
 void
-ObjectiveMFile::eval(const Vector& y, const Vector& x, const Vector& modParams, Vector& residual,
+ObjectiveMFile::unpackSparseMatrixAndCopyIntoTwoDMatData(mxArray* sparseMat, TwoDMatrix& tdm)
-                     const std::vector<int>& dynToDynpp,
-                     TensorContainer<FSSparseTensor>& derivatives) const
 {
-  mxArray* y_mx = mxCreateDoubleMatrix(y.length(), 1, mxREAL);
+  int totalCols = mxGetN(sparseMat);
-  std::copy_n(y.base(), y.length(), mxGetPr(y_mx));
+  mwIndex* rowIdxVector = mxGetIr(sparseMat);
+  mwIndex* colIdxVector = mxGetJc(sparseMat);
 
-  mxArray* x_mx = mxCreateDoubleMatrix(1, x.length(), mxREAL);
+  assert(tdm.ncols() == 3);
-  std::copy_n(x.base(), x.length(), mxGetPr(x_mx));
+  /* Under MATLAB, the following check always holds at equality; under Octave,
+     there may be an inequality, because Octave diminishes nzmax if one gives
+     zeros in the values vector when calling sparse(). */
+  assert(tdm.nrows() >= static_cast<int>(mxGetNzmax(sparseMat)));
 
-  mxArray* params_mx = mxCreateDoubleMatrix(modParams.length(), 1, mxREAL);
+  double* ptr = mxGetPr(sparseMat);
-  std::copy_n(modParams.base(), modParams.length(), mxGetPr(params_mx));
 
-  mxArray *T_order_mx, *T_mx;
+  int rind = 0;
+  int output_row = 0;
+
+  for (int i = 0; i < totalCols; i++)
+    for (int j = 0; j < static_cast<int>((colIdxVector[i + 1] - colIdxVector[i])); j++, rind++)
+      {
+        tdm.get(output_row, 0) = rowIdxVector[rind] + 1;
+        tdm.get(output_row, 1) = i + 1;
+        tdm.get(output_row, 2) = ptr[rind];
+        output_row++;
+      }
+
+  /* If there are less elements than expected (that might happen if some
+     derivative is symbolically not zero but numerically zero at the evaluation
+     point), then fill in the matrix with empty entries, that will be
+     recognized as such by KordpDynare::populateDerivativesContainer() */
+  while (output_row < tdm.nrows())
+    {
+      tdm.get(output_row, 0) = 0;
+      tdm.get(output_row, 1) = 0;
+      tdm.get(output_row, 2) = 0;
+      output_row++;
+    }
+}
+
+void
+ObjectiveMFile::eval(const Vector& y, const Vector& x, const Vector& modParams, Vector& residual,
+                     std::vector<TwoDMatrix>& md) noexcept(false)
+{
+  mxArray* T_m = mxCreateDoubleMatrix(ntt, 1, mxREAL);
+
+  mxArray* y_m = mxCreateDoubleMatrix(y.length(), 1, mxREAL);
+  std::copy_n(y.base(), y.length(), mxGetPr(y_m));
+
+  mxArray* x_m = mxCreateDoubleMatrix(1, x.length(), mxREAL);
+  std::copy_n(x.base(), x.length(), mxGetPr(x_m));
+
+  mxArray* params_m = mxCreateDoubleMatrix(modParams.length(), 1, mxREAL);
+  std::copy_n(modParams.base(), modParams.length(), mxGetPr(params_m));
+
+  mxArray* T_flag_m = mxCreateLogicalScalar(false);
+
+  {
+    // Compute temporary terms (for all orders)
+    std::string funcname = ObjectiveMFilename + "_g" + std::to_string(md.size()) + "_tt";
+    std::array<mxArray*, 1> plhs;
+    std::array prhs {T_m, y_m, x_m, params_m};
+
+    int retVal
+        = mexCallMATLAB(plhs.size(), plhs.data(), prhs.size(), prhs.data(), funcname.c_str());
+    if (retVal != 0)
+      throw DynareException(__FILE__, __LINE__, "Trouble calling " + funcname);
+
+    mxDestroyArray(T_m);
+    T_m = plhs[0];
+  }
 
   {
     // Compute residuals
     std::string funcname = ObjectiveMFilename + "_resid";
-    std::array<mxArray*, 3> plhs;
+    std::array<mxArray*, 1> plhs;
-    std::array prhs {y_mx, x_mx, params_mx};
+    std::array prhs {T_m, y_m, x_m, params_m, T_flag_m};
 
     int retVal
         = mexCallMATLAB(plhs.size(), plhs.data(), prhs.size(), prhs.data(), funcname.c_str());
@@ -69,103 +116,35 @@ ObjectiveMFile::eval(const Vector& y, const Vector& x, const Vector& modParams,
 
     residual = Vector {plhs[0]};
     mxDestroyArray(plhs[0]);
-
-    T_order_mx = plhs[1];
-    T_mx = plhs[2];
   }
 
-  {
+  for (size_t i = 1; i <= md.size(); i++)
-    // Compute Jacobian
-    std::string funcname = ObjectiveMFilename + "_g1";
-    std::array<mxArray*, 3> plhs;
-    std::array prhs {y_mx,
-                     x_mx,
-                     params_mx,
-                     const_cast<mxArray*>(objective_g1_sparse_rowval_mx),
-                     const_cast<mxArray*>(objective_g1_sparse_colval_mx),
-                     const_cast<mxArray*>(objective_g1_sparse_colptr_mx),
-                     T_order_mx,
-                     T_mx};
-
-    int retVal
-        = mexCallMATLAB(plhs.size(), plhs.data(), prhs.size(), prhs.data(), funcname.c_str());
-    if (retVal != 0)
-      throw DynareException(__FILE__, __LINE__, "Trouble calling " + funcname);
-
-    assert(static_cast<int>(mxGetN(plhs[0])) == y.length());
-    double* g1_v {mxGetPr(plhs[0])};
-    mwIndex* g1_ir {mxGetIr(plhs[0])};
-    mwIndex* g1_jc {mxGetJc(plhs[0])};
-
-    IntSequence s(1, 0);
-    auto tensor = std::make_unique<FSSparseTensor>(1, y.length(), 1);
-
-    for (int j {0}; j < y.length(); j++)
-      for (mwIndex k {g1_jc[j]}; k < g1_jc[j + 1]; k++)
-        {
-          s[0] = dynToDynpp[j];
-          tensor->insert(s, g1_ir[k], g1_v[k]);
-        }
-
-    mxDestroyArray(plhs[0]);
-
-    mxDestroyArray(T_order_mx);
-    T_order_mx = plhs[1];
-
-    mxDestroyArray(T_mx);
-    T_mx = plhs[2];
-
-    derivatives.insert(std::move(tensor));
-  }
-
-  for (int o {2}; o <= kOrder; o++)
     {
-      // Compute higher derivatives
+      // Compute model derivatives
-      std::string funcname = ObjectiveMFilename + "_g" + std::to_string(o);
+      std::string funcname = ObjectiveMFilename + "_g" + std::to_string(i);
-      std::array<mxArray*, 3> plhs;
+      std::array<mxArray*, 1> plhs;
-      std::array prhs {y_mx, x_mx, params_mx, T_order_mx, T_mx};
+      std::array prhs {T_m, y_m, x_m, params_m, T_flag_m};
 
       int retVal
           = mexCallMATLAB(plhs.size(), plhs.data(), prhs.size(), prhs.data(), funcname.c_str());
       if (retVal != 0)
         throw DynareException(__FILE__, __LINE__, "Trouble calling " + funcname);
 
-      const mxArray* sparse_indices_mx {objective_gN_sparse_indices[o - 2]};
+      if (i == 1)
-      size_t nnz {mxGetM(sparse_indices_mx)};
-#if MX_HAS_INTERLEAVED_COMPLEX
-      const int32_T* sparse_indices {mxGetInt32s(sparse_indices_mx)};
-#else
-      const int32_T* sparse_indices {static_cast<const int32_T*>(mxGetData(sparse_indices_mx))};
-#endif
-
-      assert(mxGetNumberOfElements(plhs[0]) == nnz);
-      double* gN_v {mxGetPr(plhs[0])};
-
-      IntSequence s(o, 0);
-      auto tensor = std::make_unique<FSSparseTensor>(o, y.length(), 1);
-
-      for (size_t k {0}; k < nnz; k++)
         {
-          for (int i {0}; i < o; i++)
+          assert(static_cast<int>(mxGetM(plhs[0])) == md[i - 1].nrows());
-            s[i] = dynToDynpp[sparse_indices[k + (i + 1) * nnz] - 1];
+          assert(static_cast<int>(mxGetN(plhs[0])) == md[i - 1].ncols());
-          assert(s.isSorted());
+          std::copy_n(mxGetPr(plhs[0]), mxGetM(plhs[0]) * mxGetN(plhs[0]), md[i - 1].base());
-          tensor->insert(s, sparse_indices[k] - 1, gN_v[k]);
         }
+      else
+        unpackSparseMatrixAndCopyIntoTwoDMatData(plhs[0], md[i - 1]);
 
       mxDestroyArray(plhs[0]);
-
-      mxDestroyArray(T_order_mx);
-      T_order_mx = plhs[1];
-
-      mxDestroyArray(T_mx);
-      T_mx = plhs[2];
-
-      derivatives.insert(std::move(tensor));
     }
 
-  mxDestroyArray(y_mx);
+  mxDestroyArray(T_m);
-  mxDestroyArray(x_mx);
+  mxDestroyArray(y_m);
-  mxDestroyArray(params_mx);
+  mxDestroyArray(x_m);
-  mxDestroyArray(T_order_mx);
+  mxDestroyArray(params_m);
-  mxDestroyArray(T_mx);
+  mxDestroyArray(T_flag_m);
 }

mex/sources/k_order_welfare/objective_m.hh

@@ -1,5 +1,5 @@
 /*
- * Copyright © 2021-2024 Dynare Team
+ * Copyright © 2021-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -20,34 +20,24 @@
 #ifndef OBJECTIVE_M_HH
 #define OBJECTIVE_M_HH
 
-#include <vector>
+#include "objective_abstract_class.hh"
 
-#include "dynmex.h"
+#include "mex.h"
+#include <dynmex.h>
 
-#include "Vector.hh"
+/**
-#include "sparse_tensor.hh"
+ * handles calls to <model>/+objective/static.m
-#include "t_container.hh"
+ *
-
+ **/
-// Handles calls to <model>/+objective/+sparse/static*.m
+class ObjectiveMFile : public ObjectiveAC
-class ObjectiveMFile
 {
 private:
   const std::string ObjectiveMFilename;
-  const int kOrder;
+  static void unpackSparseMatrixAndCopyIntoTwoDMatData(mxArray* sparseMat, TwoDMatrix& tdm);
-  const mxArray *const objective_g1_sparse_rowval_mx, *const objective_g1_sparse_colval_mx,
-                                                          *const objective_g1_sparse_colptr_mx;
-  // Stores M_.objective_gN_sparse_indices, starting from N=2
-  const std::vector<const mxArray*> objective_gN_sparse_indices;
 
 public:
-  ObjectiveMFile(const std::string& modName, int kOrder_arg,
+  explicit ObjectiveMFile(const std::string& modName, int ntt_arg);
-                 const mxArray* objective_g1_sparse_rowval_mx_arg,
-                 const mxArray* objective_g1_sparse_colval_mx_arg,
-                 const mxArray* objective_g1_sparse_colptr_mx_arg,
-                 const std::vector<const mxArray*> objective_gN_sparse_indices_arg);
   void eval(const Vector& y, const Vector& x, const Vector& params, Vector& residual,
-            const std::vector<int>& dynToDynpp, TensorContainer<FSSparseTensor>& derivatives) const
+            std::vector<TwoDMatrix>& md) override;
-      noexcept(false);
 };
-
 #endif

mex/sources/libkorder/sylv/QuasiTriangular.hh

@@ -1,6 +1,6 @@
 /*
  * Copyright © 2004-2011 Ondra Kamenik
- * Copyright © 2019-2024 Dynare Team
+ * Copyright © 2019-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -240,11 +240,16 @@ public:
     real = r;
   }
   virtual ~_matrix_iter() = default;
-  [[nodiscard]] bool
+  bool
   operator==(const _Self& it) const
   {
     return ptr == it.ptr;
   }
+  bool
+  operator!=(const _Self& it) const
+  {
+    return ptr != it.ptr;
+  }
   _TRef
   operator*() const
   {

mex/sources/libkorder/sylv/Vector.cc

@@ -1,6 +1,6 @@
 /*
  * Copyright © 2004-2011 Ondra Kamenik
- * Copyright © 2019-2024 Dynare Team
+ * Copyright © 2019-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -114,6 +114,42 @@ Vector::Vector(mxArray* p) :
     throw SYLV_MES_EXCEPTION("This is not a dense array of real doubles.");
 }
 
+bool
+Vector::operator==(const Vector& y) const
+{
+  return ConstVector(*this) == y;
+}
+
+bool
+Vector::operator!=(const Vector& y) const
+{
+  return ConstVector(*this) != y;
+}
+
+bool
+Vector::operator<(const Vector& y) const
+{
+  return ConstVector(*this) < y;
+}
+
+bool
+Vector::operator<=(const Vector& y) const
+{
+  return ConstVector(*this) <= y;
+}
+
+bool
+Vector::operator>(const Vector& y) const
+{
+  return ConstVector(*this) > y;
+}
+
+bool
+Vector::operator>=(const Vector& y) const
+{
+  return ConstVector(*this) >= y;
+}
+
 void
 Vector::zeros()
 {
@@ -287,10 +323,17 @@ ConstVector::operator==(const ConstVector& y) const
   return i == len;
 }
 
-std::partial_ordering
+bool
-ConstVector::operator<=>(const ConstVector& y) const
+ConstVector::operator<(const ConstVector& y) const
 {
-  return std::lexicographical_compare_three_way(data, data + len, y.data, y.data + y.len);
+  int i = std::min(len, y.len);
+  int ii = 0;
+  while (ii < i && operator[](ii) == y[ii])
+    ii++;
+  if (ii < i)
+    return operator[](ii) < y[ii];
+  else
+    return len < y.len;
 }
 
 double

mex/sources/libkorder/sylv/Vector.hh

@@ -1,6 +1,6 @@
 /*
  * Copyright © 2004-2011 Ondra Kamenik
- * Copyright © 2019-2024 Dynare Team
+ * Copyright © 2019-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -25,7 +25,6 @@
    to avoid running virtual method invokation mechanism. Some
    members, and methods are thus duplicated */
 
-#include <compare>
 #include <complex>
 #include <utility>
 
@@ -109,6 +108,15 @@ public:
     return s;
   }
 
+  // Exact equality.
+  bool operator==(const Vector& y) const;
+  bool operator!=(const Vector& y) const;
+  // Lexicographic ordering.
+  bool operator<(const Vector& y) const;
+  bool operator<=(const Vector& y) const;
+  bool operator>(const Vector& y) const;
+  bool operator>=(const Vector& y) const;
+
   virtual ~Vector()
   {
     if (destroy)
@@ -211,9 +219,29 @@ public:
     return s;
   }
   // Exact equality
-  [[nodiscard]] bool operator==(const ConstVector& y) const;
+  bool operator==(const ConstVector& y) const;
+  bool
+  operator!=(const ConstVector& y) const
+  {
+    return !operator==(y);
+  }
   // Lexicographic ordering
-  [[nodiscard]] std::partial_ordering operator<=>(const ConstVector& y) const;
+  bool operator<(const ConstVector& y) const;
+  bool
+  operator<=(const ConstVector& y) const
+  {
+    return operator<(y) || operator==(y);
+  }
+  bool
+  operator>(const ConstVector& y) const
+  {
+    return !operator<=(y);
+  }
+  bool
+  operator>=(const ConstVector& y) const
+  {
+    return !operator<(y);
+  }
 
   [[nodiscard]] double getNorm() const;
   [[nodiscard]] double getMax() const;

mex/sources/libkorder/tl/equivalence.cc

@@ -1,6 +1,6 @@
 /*
  * Copyright © 2004 Ondra Kamenik
- * Copyright © 2019-2024 Dynare Team
+ * Copyright © 2019-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -35,14 +35,12 @@ OrdSequence::operator[](int i) const
    orderings can be used for different problem sizes. We order them
    according to the average, and then according to the first item. */
 
-std::partial_ordering
+bool
-OrdSequence::operator<=>(const OrdSequence& s) const
+OrdSequence::operator<(const OrdSequence& s) const
 {
   double ta = average();
   double sa = s.average();
-  if (auto cmp1 = ta <=> sa; cmp1 != 0)
+  return (ta < sa || ((ta == sa) && (operator[](0) > s[0])));
-    return cmp1;
-  return operator[](0) <=> s[0];
 }
 
 bool

mex/sources/libkorder/tl/equivalence.hh

@@ -1,6 +1,6 @@
 /*
  * Copyright © 2004 Ondra Kamenik
- * Copyright © 2019-2024 Dynare Team
+ * Copyright © 2019-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -55,7 +55,6 @@
 
 #include "int_sequence.hh"
 
-#include <compare>
 #include <list>
 #include <string>
 #include <vector>
@@ -63,7 +62,7 @@
 /* Here is the abstraction for an equivalence class. We implement it as
    vector<int>. We have a constructor for empty class, copy
    constructor. What is important here is the ordering operator
-   operator<=>() and methods for addition of an integer, and addition of
+   operator<() and methods for addition of an integer, and addition of
    another sequence. Also we provide method has() which returns true if a
    given integer is contained. */
 
@@ -75,9 +74,9 @@ public:
   OrdSequence() : data()
   {
   }
-  [[nodiscard]] bool operator==(const OrdSequence& s) const;
+  bool operator==(const OrdSequence& s) const;
   int operator[](int i) const;
-  [[nodiscard]] std::partial_ordering operator<=>(const OrdSequence& s) const;
+  bool operator<(const OrdSequence& s) const;
   [[nodiscard]] const std::vector<int>&
   getData() const
   {
@@ -121,7 +120,12 @@ public:
   // Copy constructor plus gluing i1 and i2 in one class
   Equivalence(const Equivalence& e, int i1, int i2);
 
-  [[nodiscard]] bool operator==(const Equivalence& e) const;
+  bool operator==(const Equivalence& e) const;
+  bool
+  operator!=(const Equivalence& e) const
+  {
+    return !operator==(e);
+  }
   [[nodiscard]] int
   getN() const
   {

mex/sources/libkorder/tl/gs_tensor.hh

@@ -1,6 +1,6 @@
 /*
  * Copyright © 2004 Ondra Kamenik
- * Copyright © 2019-2024 Dynare Team
+ * Copyright © 2019-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -76,11 +76,16 @@ public:
   // Constructs the tensor dimensions for slicing (see the implementation for details)
   TensorDimens(const IntSequence& ss, const IntSequence& coor);
 
-  [[nodiscard]] bool
+  bool
   operator==(const TensorDimens& td) const
   {
     return nvs == td.nvs && sym == td.sym;
   }
+  bool
+  operator!=(const TensorDimens& td) const
+  {
+    return !operator==(td);
+  }
 
   [[nodiscard]] int
   dimen() const

mex/sources/libkorder/tl/int_sequence.cc

@@ -1,6 +1,6 @@
 /*
  * Copyright © 2004 Ondra Kamenik
- * Copyright © 2019-2024 Dynare Team
+ * Copyright © 2019-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -103,10 +103,10 @@ IntSequence::operator==(const IntSequence& s) const
   return std::equal(data, data + length, s.data, s.data + s.length);
 }
 
-std::strong_ordering
+bool
-IntSequence::operator<=>(const IntSequence& s) const
+IntSequence::operator<(const IntSequence& s) const
 {
-  return std::lexicographical_compare_three_way(data, data + length, s.data, s.data + s.length);
+  return std::lexicographical_compare(data, data + length, s.data, s.data + s.length);
 }
 
 bool

mex/sources/libkorder/tl/int_sequence.hh

@@ -1,6 +1,6 @@
 /*
  * Copyright © 2004 Ondra Kamenik
- * Copyright © 2019-2024 Dynare Team
+ * Copyright © 2019-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -43,7 +43,6 @@
 #define INT_SEQUENCE_HH
 
 #include <algorithm>
-#include <compare>
 #include <initializer_list>
 #include <utility>
 #include <vector>
@@ -120,7 +119,12 @@ public:
     if (destroy)
       delete[] data;
   }
-  [[nodiscard]] bool operator==(const IntSequence& s) const;
+  bool operator==(const IntSequence& s) const;
+  bool
+  operator!=(const IntSequence& s) const
+  {
+    return !operator==(s);
+  }
   int&
   operator[](int i)
   {
@@ -137,10 +141,15 @@ public:
     return length;
   }
 
-  /* We provide two orderings. The first operator<=>() is the linear
+  /* We provide two orderings. The first operator<() is the linear
      lexicographic ordering, the second less() is the non-linear Cartesian
      ordering. */
-  [[nodiscard]] std::strong_ordering operator<=>(const IntSequence& s) const;
+  bool operator<(const IntSequence& s) const;
+  bool
+  operator<=(const IntSequence& s) const
+  {
+    return (operator==(s) || operator<(s));
+  }
   [[nodiscard]] bool lessEq(const IntSequence& s) const;
   [[nodiscard]] bool less(const IntSequence& s) const;
 

mex/sources/libkorder/tl/kron_prod.hh

@@ -1,6 +1,6 @@
 /*
  * Copyright © 2004 Ondra Kamenik
- * Copyright © 2019-2024 Dynare Team
+ * Copyright © 2019-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -86,7 +86,7 @@ public:
 
   KronProdDimens& operator=(const KronProdDimens& kd) = default;
   KronProdDimens& operator=(KronProdDimens&& kd) = default;
-  [[nodiscard]] bool
+  bool
   operator==(const KronProdDimens& kd) const
   {
     return rows == kd.rows && cols == kd.cols;

mex/sources/libkorder/tl/permutation.hh

@@ -1,6 +1,6 @@
 /*
  * Copyright © 2004 Ondra Kamenik
- * Copyright © 2019-2024 Dynare Team
+ * Copyright © 2019-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -95,7 +95,7 @@ public:
   Permutation(const Permutation& p, int i) : permap(p.permap.insert(p.size(), i))
   {
   }
-  [[nodiscard]] bool
+  bool
   operator==(const Permutation& p) const
   {
     return permap == p.permap;

mex/sources/libkorder/tl/ps_tensor.hh

@@ -1,6 +1,6 @@
 /*
  * Copyright © 2004 Ondra Kamenik
- * Copyright © 2019-2024 Dynare Team
+ * Copyright © 2019-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -115,7 +115,7 @@ public:
   {
     per.apply(nvmax);
   }
-  [[nodiscard]] bool
+  bool
   operator==(const PerTensorDimens& td) const
   {
     return TensorDimens::operator==(td) && per == td.per;

mex/sources/libkorder/tl/symmetry.hh

@@ -1,6 +1,6 @@
 /*
  * Copyright © 2004 Ondra Kamenik
- * Copyright © 2019-2024 Dynare Team
+ * Copyright © 2019-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -126,11 +126,16 @@ public:
   {
     return run;
   }
-  [[nodiscard]] bool
+  bool
-  operator==(const symiterator& it) const
+  operator=(const symiterator& it)
   {
     return dim == it.dim && run == it.run;
   }
+  bool
+  operator!=(const symiterator& it)
+  {
+    return !operator=(it);
+  }
 };
 
 /* The class SymmetrySet defines a set of symmetries of the given length

mex/sources/libkorder/tl/tensor.hh

@@ -1,6 +1,6 @@
 /*
  * Copyright © 2004 Ondra Kamenik
- * Copyright © 2019-2024 Dynare Team
+ * Copyright © 2019-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -150,11 +150,16 @@ public:
     {
       return offset;
     }
-    [[nodiscard]] bool
+    bool
     operator==(const index& n) const
     {
       return offset == n.offset;
     }
+    bool
+    operator!=(const index& n) const
+    {
+      return offset != n.offset;
+    }
     [[nodiscard]] const IntSequence&
     getCoor() const
     {

mex/sources/sobol/gaussian.hh

@@ -2,7 +2,7 @@
 **
 ** Pseudo code of the algorithm is given at http://home.online.no/~pjacklam/notes/invnorm
 **
-** Copyright © 2010-2024 Dynare Team
+** Copyright © 2010-2023 Dynare Team
 **
 ** This file is part of Dynare.
 **
@@ -104,8 +104,9 @@ icdf(const T uniform)
   return gaussian;
 }
 
+template<typename T>
 void
-icdfm(int n, auto* U)
+icdfm(int n, T* U)
 {
 #pragma omp parallel for
   for (int i = 0; i < n; i++)
@@ -113,8 +114,9 @@ icdfm(int n, auto* U)
   return;
 }
 
+template<typename T>
 void
-icdfmSigma(int d, int n, auto* U, const double* LowerCholSigma)
+icdfmSigma(int d, int n, T* U, const double* LowerCholSigma)
 {
   double one = 1.0;
   double zero = 0.0;
@@ -126,8 +128,9 @@ icdfmSigma(int d, int n, auto* U, const double* LowerCholSigma)
   copy_n(tmp.begin(), d * n, U);
 }
 
+template<typename T>
 void
-usphere(int d, int n, auto* U)
+usphere(int d, int n, T* U)
 {
   icdfm(n * d, U);
 #pragma omp parallel for
@@ -144,8 +147,9 @@ usphere(int d, int n, auto* U)
     }
 }
 
+template<typename T>
 void
-usphereRadius(int d, int n, double radius, auto* U)
+usphereRadius(int d, int n, double radius, T* U)
 {
   icdfm(n * d, U);
 #pragma omp parallel for

preprocessor

@@ -1 +1 @@
-Subproject commit d8866fbec8a09df0d8fa1d2e7e60b2aae8685ea8
+Subproject commit e32025a76fb156a9af8101f9f43faf7eaa2f72ef

scripts/docker/Dockerfile

@@ -1,4 +1,4 @@
-# Copyright 2023-2024 Dynare Team
+# Copyright 2023 Dynare Team
 # This file is part of Dynare.
 #
 # Dynare is free software: you can redistribute it and/or modify
@@ -26,38 +26,38 @@
 # with the system libraries and adds the path of Dynare to the MATLAB and Octave startup scripts.            #
 #                                                                                                            #
 # MATLAB LICENSE:                                                                                            #
-# The container is created without any information on a license. To use Dynare with MATLAB, you need to      #
+# The container is created using a network license, so no information on the license is inside the container #
-# provide a valid license, see https://git.dynare.org/dynare/dynare/docker/README.md#matlab-license.         #
+# see https://git.dynare.org/dynare/dynare/docker/README.md#matlab-license for more information.             #
 ##############################################################################################################
 
+# Default values which MATLAB and Dynare release to install in the container
 # The Dynare release must conform to a corresponding tag on https://git.dynare.org/dynare/dynare
-# Note that Dynare 6.x uses the meson build system, while Dynare 4.x and 5.x use the autoconf/automake build system
 # MATLAB release must conform to a corresponding tag on https://hub.docker.com/r/mathworks/matlab/tags
-# Octave version is the one shipped with the Ubuntu version used in the base container (or from a PPA)
+# Octave version the one shipped with the Ubuntu version used in the base container (in 20.04 it is 6.4.0)
-ARG MATLAB_RELEASE=R2023b
+ARG MATLAB_RELEASE=R2023a
-ARG DYNARE_RELEASE=6.0
+ARG DYNARE_RELEASE=5.4
 
 # Specify the list of products to install into MATLAB with mpm
 ARG MATLAB_PRODUCT_LIST="Symbolic_Math_Toolbox Statistics_and_Machine_Learning_Toolbox Optimization_Toolbox Econometrics_Toolbox Parallel_Computing_Toolbox Control_System_Toolbox Global_Optimization_Toolbox"
 
-# Specify MATLAB install location
+# Specify MATLAB Install Location.
 ARG MATLAB_INSTALL_LOCATION="/opt/matlab/${MATLAB_RELEASE}"
 
-# Specify license server information using the format: port@hostname
+# Specify license server information using the format: port@hostname 
 ARG LICENSE_SERVER
 
-# Specify the base image with pre-installed MATLAB
+# Specify the base image with MATLAB installed.
 FROM mathworks/matlab:${MATLAB_RELEASE}
 USER root
 
-# Declare build arguments to use at the current build stage
+# Declare build arguments to use at the current build stage.
 ARG MATLAB_RELEASE
 ARG MATLAB_PRODUCT_LIST
 ARG MATLAB_INSTALL_LOCATION
 ARG LICENSE_SERVER
 ARG DYNARE_RELEASE
 
-# Install mpm dependencies
+# Install mpm dependencies.
 RUN export DEBIAN_FRONTEND=noninteractive \
     && apt-get update \
     && apt-get install --no-install-recommends --yes \
@@ -70,7 +70,6 @@ RUN export DEBIAN_FRONTEND=noninteractive \
 
 # Run mpm to install additional toolboxes for MATLAB in the target location and delete the mpm installation afterwards.
 # If mpm fails to install successfully, then print the logfile in the terminal, otherwise clean up.
-# Hint: Sometimes there is a segmentation fault when running mpm, just re-run the build command in this case.
 RUN wget -q https://www.mathworks.com/mpm/glnxa64/mpm \ 
     && chmod +x mpm \
     && ./mpm install \
@@ -80,39 +79,11 @@ RUN wget -q https://www.mathworks.com/mpm/glnxa64/mpm \
     || (echo "MPM Installation Failure. See below for more information:" && cat /tmp/mathworks_root.log && false) \
     && rm -f mpm /tmp/mathworks_root.log
 
-# Install specific build-system dependencies based on DYNARE_RELEASE and keep this layer small to reduce image size (apt cache cleanup)
+# Install dynare dependencies.
-RUN case "$DYNARE_RELEASE" in \
+RUN export DEBIAN_FRONTEND=noninteractive \
-    6.*) \
+    && apt-get update \
-        export DEBIAN_FRONTEND=noninteractive && \
+    && apt-get install --no-install-recommends --yes \
-        apt-get update && \
+    build-essential \
-        apt-get install --no-install-recommends --yes \
-        gcc \
-        g++ \
-        meson \
-        pkgconf \
-        python3-pip\
-        && apt-get clean \
-        && apt-get autoremove \
-        && rm -rf /var/lib/apt/lists/* ;; \
-    5.*|4.*) \
-        export DEBIAN_FRONTEND=noninteractive && \
-        apt-get update && \
-        apt-get install --no-install-recommends --yes \
-        build-essential \
-        autoconf \
-        automake \
-        doxygen \
-        && apt-get clean \
-        && apt-get autoremove \
-        && rm -rf /var/lib/apt/lists/*;; \
-    *) \
-        echo "Unsupported DYNARE_RELEASE version: $DYNARE_RELEASE. No dependencies will be installed." ;; \
-esac
-
-# Install common dependencies for Dynare and keep this layer small to reduce image size (apt cache cleanup)
-RUN export DEBIAN_FRONTEND=noninteractive && \
-    apt-get update && \
-    apt-get install --no-install-recommends --yes \    
     gfortran \
     libboost-graph-dev \
     libgsl-dev \
@@ -123,6 +94,8 @@ RUN export DEBIAN_FRONTEND=noninteractive && \
     flex \
     libfl-dev \
     bison \
+    autoconf \
+    automake \
     texlive \
     texlive-publishers \
     texlive-latex-extra \
@@ -136,6 +109,7 @@ RUN export DEBIAN_FRONTEND=noninteractive && \
     tex-gyre \
     latexmk \
     libjs-mathjax \
+    doxygen \
     x13as \
     liboctave-dev \
     octave-control \
@@ -149,30 +123,10 @@ RUN export DEBIAN_FRONTEND=noninteractive && \
     ghostscript \
     epstool \
     git \
-    git-lfs \
     && apt-get clean \
     && apt-get autoremove \
     && rm -rf /var/lib/apt/lists/*
 
-# Dynare 6.x is only compatible with Octave 7.1.0 to 8.4.0
-# The current base image of R2023b ships is based on Ubuntu 22.04 which ships Octave 6.2.0,
-# so we add an inofficial Octave PPA to install a compatible version
-# Once the MATLAB containers are based on Ubuntu 24.04, we can remove this step and use the default Octave version from the Ubuntu repository
-# Note: the pkg install -forge command takes a long time
-RUN case "$DYNARE_RELEASE" in \
-    6.*) \
-        export DEBIAN_FRONTEND=noninteractive && \
-        apt-get update && \
-        apt-get install --no-install-recommends --yes software-properties-common && \
-        add-apt-repository -y ppa:ubuntuhandbook1/octave && \
-        apt-get update && \
-        apt-get remove --purge --yes octave octave-control octave-econometrics octave-io octave-statistics octave-struct octave-parallel && \
-        apt-get install --no-install-recommends --yes octave octave-dev && \
-        apt-get clean && \
-        rm -rf /var/lib/apt/lists/* && \
-        octave --eval "pkg install -forge struct io statistics optim control econometrics parallel" ;; \
-esac
-
 # Rename libraries (see matlab-support package: https://salsa.debian.org/debian/matlab-support/-/blob/master/debian/matlab-support.postinst)
 RUN if [ -f "${MATLAB_INSTALL_LOCATION}/sys/os/glnxa64/libgcc_s.so.1" ]; then \
         mv ${MATLAB_INSTALL_LOCATION}/sys/os/glnxa64/libgcc_s.so.1 ${MATLAB_INSTALL_LOCATION}/sys/os/glnxa64/libgcc_s.so.1.bak; \
@@ -209,34 +163,20 @@ ENV MLM_LICENSE_FILE=$LICENSE_SERVER
 # Get Dynare sources as matlab user
 USER matlab
 WORKDIR /home/matlab
-RUN git lfs install
 RUN git clone --depth 1 --branch ${DYNARE_RELEASE} --recurse-submodules https://git.dynare.org/dynare/dynare.git
 
 # Compile Dynare
-# Dynare 6.x: install meson 1.3.1 using python3-pip because meson package in the Ubuntu repositories is too old
+USER matlab
-# Once the MATLAB containers are based on Ubuntu 24.04, this step can be removed
+WORKDIR /home/matlab
-RUN case "$DYNARE_RELEASE" in \
+RUN cd dynare \
-    6.*) \
+    && autoreconf -si \
-        cd dynare && \        
+    && ./configure --with-matlab=${MATLAB_INSTALL_LOCATION} MATLAB_VERSION=${MATLAB_RELEASE} \
-        pip3 install meson==1.3.1 && \
+    && make -j$(($(nproc)+1))
-        export PATH="/home/matlab/.local/bin:${PATH}" && \
-        meson setup -Dmatlab_path=${MATLAB_INSTALL_LOCATION} -Dbuildtype=debugoptimized build-matlab && \
-        meson compile -C build-matlab && \
-        meson setup -Dbuild_for=octave -Dbuildtype=debugoptimized build-octave && \
-        meson compile -C build-octave ;; \
-    5.*|4.*) \
-        cd dynare && \
-        autoreconf -si && \
-        ./configure --with-matlab=${MATLAB_INSTALL_LOCATION} MATLAB_VERSION=${MATLAB_RELEASE} && \
-        make -j$(($(nproc)+1)) ;; \
-    *) \
-        echo "Unsupported DYNARE_RELEASE version: $DYNARE_RELEASE. Compilation steps will be skipped." ;; \
-esac
 
-# Add path of dynare to startup script for Octave
+# Add path of dynare to startup script for Octave.
 RUN echo "addpath /home/matlab/dynare/matlab" >> /home/matlab/.octaverc
 
-# Add path of dynare to startup script for MATLAB
+# Add path of dynare to startup script for MATLAB.
 # Note that if startup.m file exists (in newer MATLAB containers), it is a MATLAB function
 # and the last line is an "end", so we append the path to the second-to-last line
 # For some reason we have to do this as root, otherwise the file is not writable
@@ -255,6 +195,6 @@ RUN filename="/home/matlab/Documents/MATLAB/startup.m" && \
     fi && \
     chown matlab:matlab "$filename"
 
-# Set user and work directory
+# Set user and work directory.
 USER matlab
 WORKDIR /home/matlab

scripts/docker/README.md

@@ -1,5 +1,5 @@
 # Dynare Docker Containers
-We provide a range of pre-configured Docker containers for [Dynare](https://dynare.org), which include both Octave and MATLAB (pre-configured with Dynare already in the PATH) and all recommended toolboxes. These containers are ideal for using Dynare in CI/CD environments ([example Workflow for GitHub Actions](https://github.com/JohannesPfeifer/DSGE_mod/tree/master/.github/workflows)) or High Performance Computing clusters with either [Docker, ENROOT or Singularity](https://wiki.bwhpc.de/e/BwUniCluster2.0/Containers).
+We provide a range of pre-configured Docker containers for [Dynare](https://dynare.org), which include both Octave and MATLAB (pre-configured with Dynare already in the PATH) and all recommended toolboxes. These containers are ideal for using Dynare in CI/CD environments ([example Workflow for GitHub Actions](https://github.com/wmutschl/DSGE_mod/tree/master/.github/workflows)) or High Performance Computing clusters with either [Docker, ENROOT or Singularity](https://wiki.bwhpc.de/e/BwUniCluster2.0/Containers).
 
 To minimize maintenance efforts while ensuring high levels of security, reliability, and performance, our Docker containers are built from the official [MATLAB container base image](https://hub.docker.com/r/mathworks/matlab) using a custom [Dockerfile](Dockerfile). For more information on building and customizing the containers, see the [built instructions and customization](#built-instructions-and-customization) section below. Additionally, we provide an example [docker-compose file](docker-compose.yml) for complete access to the Ubuntu Desktop via VNC.
 
@@ -7,9 +7,7 @@ To minimize maintenance efforts while ensuring high levels of security, reliabil
 
 | Tags   | Dynare Version | MATLAB® Version | Octave Version | Operating System | Base Image              |
 |--------|----------------|-----------------|----------------|------------------|-------------------------|
-| latest | 6.0            | R2023b          | 8.4.0 (PPA)    | Ubuntu 22.04     | mathworks/matlab:R2023b |
+| latest | 5.4            | R2023a          | 5.2.0          | Ubuntu 20.04     | mathworks/matlab:R2023a |
-| 6.0    | 6.0            | R2023b          | 8.4.0 (PPA)    | Ubuntu 22.04     | mathworks/matlab:R2023b |
-| 5.5    | 5.5            | R2023b          | 6.4.0          | Ubuntu 22.04     | mathworks/matlab:R2023b |
 | 5.4    | 5.4            | R2023a          | 5.2.0          | Ubuntu 20.04     | mathworks/matlab:R2023a |
 | 5.3    | 5.3            | R2022b          | 5.2.0          | Ubuntu 20.04     | mathworks/matlab:R2022b |
 | 5.2    | 5.2            | R2022a          | 5.2.0          | Ubuntu 20.04     | mathworks/matlab:R2022a |
@@ -17,21 +15,14 @@ To minimize maintenance efforts while ensuring high levels of security, reliabil
 | 5.0    | 5.0            | R2021b          | 5.2.0          | Ubuntu 20.04     | mathworks/matlab:R2021b |
 | 4.6.4  | 4.6.4          | R2021a          | 5.2.0          | Ubuntu 20.04     | mathworks/matlab:R2021a |
 
-Note that we use an inofficial [PPA](https://launchpad.net/~ubuntuhandbook1/+archive/ubuntu/octave) (maintained by [https://ubuntuhandbook.org](https://ubuntuhandbook.org)) to install Octave 8.4.0 on Ubuntu 22.04, the usual disclaimer on PPAs applies.
-Once Ubuntu 24.04 is released, we will switch to the version from the official repositories.
-
 ## How to interact with the container
 
 To pull the latest image to your machine, execute:
 ```sh
 docker pull dynare/dynare:latest
 ```
-or a specific version:
+In the following we assume that you have access to a MATLAB license using e.g. a [network license server of a University](https://uni-tuebingen.de/de/3107#c4656) and show different workflows how to interact with the container.
-```sh
+Obviously, you would need to adjust the environmental variable `MLM_LICENSE_FILE` to your use-case, please refer to the [MATLAB license](#matlab-license) section on licensing information and how to pass a personal license.
-docker pull dynare/dynare:6.0
-```
-In the following we assume that you have access to a MATLAB license and show different workflows how to interact with the container.
-Obviously, you need to adjust the environmental variable `MLM_LICENSE_FILE` to your use-case, please refer to the [MATLAB license](#matlab-license) section on licensing information and how to pass a personal license.
 Alternatively, if you don't have access to a license or the closed-source mentality of MATLAB is an irreconcilable issue for you, you can equally well use Dynare with the free and open-source alternative Octave.
 
 ### Run Dynare in an interactive MATLAB session in the browser
@@ -40,9 +31,9 @@ To launch the container with the `-browser` option, execute:
 ```sh
 docker run -it --rm -p 8888:8888 -e MLM_LICENSE_FILE=27000@matlab-campus.uni-tuebingen.de --shm-size=512M dynare/dynare:latest -browser
 ```
-You will receive a URL to access MATLAB in a web browser, for example: `http://localhost:8888` or another IP address that you can use to reach your server, such as through a VPN like [Tailscale](https://tailscale.com) if you are behind a firewall. Enter the URL provided into a web browser. Note that if you set `MLM_LICENSE_FILE` to empty or leave it out from the command, you will be prompted to enter credentials for a MathWorks account associated with a MATLAB license. If you are using a network license manager, switch to the Network License Manager tab and enter the license server address instead. After providing your license information, a MATLAB session will start in the browser. This may take several minutes. To modify the behavior of MATLAB when launched with the `-browser` flag, pass environment variables to the `docker run` command. For more information, see [Advanced Usage](https://github.com/mathworks/matlab-proxy/blob/main/Advanced-Usage.md).
+You will receive a URL to access MATLAB in a web browser, for example: `http://localhost:8888` or another IP address that you can use to reach your server, such as through a VPN like [Tailscale](https://tailscale.com) if you are behind a firewall. Enter the URL provided into a web browser. If prompted, enter credentials for a MathWorks account associated with a MATLAB license. If you are using a network license manager, switch to the Network License Manager tab and enter the license server address instead. After providing your license information, a MATLAB session will start in the browser. This may take several minutes. To modify the behavior of MATLAB when launched with the `-browser` flag, pass environment variables to the `docker run` command. For more information, see [Advanced Usage](https://github.com/mathworks/matlab-proxy/blob/main/Advanced-Usage.md).
 
-Note that the `-browser` flag is supported by base images starting from `mathworks/matlab:R2022a` using [noVNC](https://novnc.com). Some browsers may not support this workflow.
+Note that the `-browser` flag is supported by base images starting from `mathworks/matlab:R2022a` using [noVNC](https://novnc.com). Some browsers, like Safari, may not support this workflow.
 
 ### Run Ubuntu desktop and interact with it via VNC
 
@@ -54,7 +45,7 @@ To connect to the Ubuntu desktop, either:
 
 - Point a browser to port 6080 of the docker host machine running this container (`http://hostname:6080`).
 - Use a VNC client to connect to display 1 of the docker host machine (`hostname:1`). The VNC password is `matlab` by default, you can change that by adjusting the `PASSWORD` environment variable in the run command.
-- If you are behind a firewall, we recommend to use a VPN such as [Tailscale](https://tailscale.com) such that you can access the VNC server via the Tailscale address of the server. 
+- If you are behind a firewall, we recommend to use a VPN such as [Tailscale](https://tailscale.com).
 
 ### Run Dynare with Octave in an interactive command prompt
 
@@ -101,7 +92,6 @@ The Desktop window of MATLAB will open on your machine. Note that the command ab
 ### MATLAB license
 
 To run this container, your license must be [configured for cloud use](https://mathworks.com/help/install/license/licensing-for-mathworks-products-running-on-the-cloud.html). Individual and Campus-Wide licenses are already configured for cloud use. If you have a different license type, please contact your license administrator to configure it for cloud use. You can identify your license type and administrator by viewing your MathWorks Account. Administrators can consult the "Administer Network Licenses" documentation. If you don't have a MATLAB license, you can obtain a trial license at [MATLAB Trial for Docker](https://de.mathworks.com/campaigns/products/trials/targeted/dkr.html).
-Lastly, if you run the container via a GitHub workflow, you don't need to provide a license as the IP of the GitHub runner is already covered by a sponsored MATLAB license.
 
 #### Network license
 If you're using a network license, you can pass the port and hostname via the `MLM_LICENSE_FILE` environmental variable in your `docker run` command or Docker Compose file. Here's an example `docker run` command that uses a network license:
@@ -112,14 +102,14 @@ docker run --init -it --rm -e MLM_LICENSE_FILE=27000@matlab-campus.uni-tuebingen
 #### Personal License
 
 To use a personal license, you must first create a license file via the MATHWORKS License Center, refer to [Option 2](https://de.mathworks.com/matlabcentral/answers/235126-how-do-i-generate-a-matlab-license-file#answer_190013) for detailed instructions.
-For this process, you will need the `username` and a `host ID`. In the container, the username is predefined as `matlab`.
+For this process, you will need the `username` and a `host ID`. In the container, the username is predefined as 'matlab'.
 The `host ID` corresponds to the MAC address of any network adapter in the container.
 In Docker, you can supply a [randomly generated MAC address](https://miniwebtool.com/mac-address-generator/) (e.g., A6-7E-1A-F4-9A-92) during the docker run command.
 Download the file from MATHWORKS License Center and ensure you provide the container with access to the license file by mounting it as a (read-only) volume.
 
-Here is an example `docker run` command that utilizes a license file named `license.lic`, which is located in a folder `$HOME/matlab-license` on the host machine; the MAC address associated with the license is set to `A6-7E-1A-F4-9A-92`:
+Here is an example `docker run` command that utilizes a license file named `matlab-license.lic`, which is located in your home folder:
 ```sh
-docker run --init -it --rm --mac-address A6-7E-1A-F4-9A-92 --shm-size=512M -v $HOME/matlab-license/:/licenses:ro -e MLM_LICENSE_FILE=/licenses/license.lic dynare/dynare:latest matlab -batch "cd dynare/examples; dynare example1"
+docker run --init -it --rm --mac-address A6-7E-1A-F4-9A-92 --shm-size=512M -v $HOME/matlab-license.lic:/licenses/license.lic:ro -e MLM_LICENSE_FILE=/licenses/license.lic dynare/dynare:latest matlab -batch "cd dynare/examples; dynare example1"
 ```
 
 ### Environment variables
@@ -130,9 +120,7 @@ When running the `docker run` command, you can specify environment variables usi
 
 Here are the commands to create the Docker images available at [Docker Hub](https://hub.docker.com/r/dynare/dynare):
 ```sh
-docker build --build-arg MATLAB_RELEASE=R2023b --build-arg DYNARE_RELEASE=6.0 -t dynare/dynare:latest .
+docker build --build-arg MATLAB_RELEASE=R2023a --build-arg DYNARE_RELEASE=5.4 -t dynare/dynare:latest .
-docker build --build-arg MATLAB_RELEASE=R2023b --build-arg DYNARE_RELEASE=6.0 -t dynare/dynare:6.0 .
-docker build --build-arg MATLAB_RELEASE=R2023b --build-arg DYNARE_RELEASE=5.5 -t dynare/dynare:5.5 .
 docker build --build-arg MATLAB_RELEASE=R2023a --build-arg DYNARE_RELEASE=5.4 -t dynare/dynare:5.4 .
 docker build --build-arg MATLAB_RELEASE=R2022b --build-arg DYNARE_RELEASE=5.3 -t dynare/dynare:5.3 .
 docker build --build-arg MATLAB_RELEASE=R2022a --build-arg DYNARE_RELEASE=5.2 -t dynare/dynare:5.2 .

scripts/matlab-online/packageDynare.sh

@@ -4,8 +4,12 @@ set -exo pipefail
 # Creates a dynare-X.Y.mltbx in the current repository, using the settings below.
 # Needs to be run from Ubuntu 22.04 LTS, with the needed packages installed.
 
+DYNAREVER=5.5
 X13ASVER=1-1-b60
+LIBLOCATION="/MATLAB Add-Ons/Toolboxes/Dynare/mex/matlab/libs"
 MATLABPATH=/opt/MATLAB/R2023b
+# MatIO has been recompiled by hand to avoid the dependency on HDF5, which is a nightmare
+MATIO_PREFIX=/home/sebastien/usr
 # TODO: change size and put white background for better rendering in MATLAB Add-Ons browser
 DYNARE_PNG_LOGO=../../preprocessor/doc/logos/dlogo.png
 
@@ -17,31 +21,48 @@ cleanup ()
 }
 trap cleanup EXIT
 
-pushd ../..
+pushd "$tmpdir"
-meson setup -Dmatlab_path="$MATLABPATH" -Dbuildtype=release -Dprefer_static=true "$tmpdir"/build-matlab-online
 
-cd "$tmpdir"/build-matlab-online
+# Get Dynare
-meson compile
+wget -q https://www.dynare.org/release/source/dynare-$DYNAREVER.tar.xz
-meson install --destdir "$tmpdir"
+tar xf dynare-$DYNAREVER.tar.xz
-DYNAREVER=$(meson introspect --projectinfo | jq -r '.version')
 
-cd ..
+# Build Dynare
-strip usr/local/bin/dynare-preprocessor
+cd dynare-$DYNAREVER
-strip usr/local/lib/dynare/mex/matlab/*.mexa64
+
+# Use static libstdc++ otherwise the one shipped with MATLAB creates problem (since it overrides the systemd-wide one from Ubuntu)
+./configure --with-matlab="$MATLABPATH" --with-matio="$MATIO_PREFIX" --disable-octave --disable-doc --disable-dynare++ LDFLAGS=-static-libstdc++
+make -j$(nproc)
+
+strip preprocessor/dynare-preprocessor
+strip mex/matlab/*.mexa64
+
+# Patch mex files to look into ./lib folder
+for f in mex/matlab/*.mexa64
+do
+    patchelf --set-rpath "$LIBLOCATION" $f
+done
+
+# Grab the shared libraries needed
+mkdir mex/matlab/libs
+for l in libgsl.so.27 libgslcblas.so.0
+do
+    cp /usr/lib/x86_64-linux-gnu/$l mex/matlab/libs/
+    # Patch rpath to find dependencies at runtime
+    patchelf --set-rpath "$LIBLOCATION" mex/matlab/libs/$l
+done
+cp "$MATIO_PREFIX"/lib/libmatio.so.11 mex/matlab/libs/
+patchelf --set-rpath "$LIBLOCATION" mex/matlab/libs/libmatio.so.11 # Probably not needed
 
 # Get X13 binary from the Census Bureau website
 # The binary from Ubuntu has some shared library dependencies, so it is safer to use a static binary
 wget -q https://www2.census.gov/software/x-13arima-seats/x13as/unix-linux/program-archives/x13as_ascii-v${X13ASVER}.tar.gz
 tar xf x13as_ascii-v${X13ASVER}.tar.gz
-
+mkdir -p matlab/modules/dseries/externals/x13/linux/64
-# Populate staging area for the zip
+cp x13as/x13as_ascii matlab/modules/dseries/externals/x13/linux/64/x13as
-cp -pRL usr/local/lib/dynare dynare # -L is needed to dereference the preprocessor symlink
-mkdir -p dynare/matlab/dseries/externals/x13/linux/64
-cp -p x13as/x13as_ascii dynare/matlab/dseries/externals/x13/linux/64/x13as
 
 # zip dynare
-cd dynare
+zip -q -r "$tmpdir"/dynare.zip contrib/jsonlab contrib/ms-sbvar/TZcode/MatlabFiles examples matlab mex/matlab preprocessor/dynare-preprocessor license.txt NEWS.md README.md VERSION
-zip -q -r "$tmpdir"/dynare.zip *
 
 # make toolbox
 popd

tests/deterministic_simulations/purely_backward/ar1.mod

@@ -33,6 +33,6 @@ if ~oo_.deterministic_simulation.status
 end
 send_endogenous_variables_to_workspace;
 
-if max(abs(y-[1; exp(cumprod([1; rho*ones(9, 1)]))]))>options_.dynatol.x
+if max(abs(y'-[1; exp(cumprod([1; rho*ones(9, 1)]))]))>options_.dynatol.x
     error('Wrong solution!')
 end

tests/estimation/hssmc/fs2000.mod

@@ -86,8 +86,6 @@ estimation(order=1, datafile='../fsdat_simul.m', nobs=192, loglinear,
            posterior_sampling_method='hssmc',
            posterior_sampler_options=('steps',10,
                                       'lambda',2,
-                                      'particles', 5000,
+                                      'particles', 20000,
                                       'scale',.5,
-                                      'target', .25),
+                                      'target', .25));
-bayesian_irf, smoother, moments_varendo,consider_all_endogenous
-);

tests/moments/example1_hp_test.mod

@@ -77,12 +77,12 @@ send_endogenous_variables_to_workspace;
 options_.nomoments=0;
 oo_unfiltered_all_shocks=oo_;
 
-[junk, y_filtered]=sample_hp_filter(y,1600);
+[junk, y_filtered]=sample_hp_filter(y',1600);
-[junk, c_filtered]=sample_hp_filter(c,1600);
+[junk, c_filtered]=sample_hp_filter(c',1600);
-[junk, k_filtered]=sample_hp_filter(k,1600);
+[junk, k_filtered]=sample_hp_filter(k',1600);
-[junk, a_filtered]=sample_hp_filter(a,1600);
+[junk, a_filtered]=sample_hp_filter(a',1600);
-[junk, h_filtered]=sample_hp_filter(h,1600);
+[junk, h_filtered]=sample_hp_filter(h',1600);
-[junk, b_filtered]=sample_hp_filter(b,1600);
+[junk, b_filtered]=sample_hp_filter(b',1600);
 
 verbatim;
 total_std_all_shocks_filtered_sim=std([y_filtered c_filtered k_filtered a_filtered h_filtered b_filtered]);
@@ -112,12 +112,12 @@ stoch_simul(order=1,nofunctions,hp_filter=0,periods=2500000,nomoments);
 send_endogenous_variables_to_workspace;
 oo_unfiltered_one_shock=oo_;
 
-[junk, y_filtered]=sample_hp_filter(y,1600);
+[junk, y_filtered]=sample_hp_filter(y',1600);
-[junk, c_filtered]=sample_hp_filter(c,1600);
+[junk, c_filtered]=sample_hp_filter(c',1600);
-[junk, k_filtered]=sample_hp_filter(k,1600);
+[junk, k_filtered]=sample_hp_filter(k',1600);
-[junk, a_filtered]=sample_hp_filter(a,1600);
+[junk, a_filtered]=sample_hp_filter(a',1600);
-[junk, h_filtered]=sample_hp_filter(h,1600);
+[junk, h_filtered]=sample_hp_filter(h',1600);
-[junk, b_filtered]=sample_hp_filter(b,1600);
+[junk, b_filtered]=sample_hp_filter(b',1600);
 
 verbatim;
 total_std_one_shock_filtered_sim=std([y_filtered c_filtered k_filtered a_filtered h_filtered b_filtered]);

tests/occbin/filter/NKM.mod

@@ -13,66 +13,66 @@
 
 // ----------------- Defintions -----------------------------------------//
 var        
-    c       $c$         (long_name='Consumption')
+    c          //1  Consumption 
-    n                   (long_name='Labor')
+    n          //2  Labor
-    y       $y$         (long_name='Output')
+    y          //5  Output
-    yf                  (long_name='Final goods')
+    yf         //6  Final goods       
-    yg                  (long_name='Output growth gap')
+    yg         //11 Output growth gap
-    w                   (long_name='Real wage rate')
+    w          //12 Real wage rate
-    wf                  (long_name='Flexible real wage')
+    wf         //13 Flexible real wage
-    pigap               (long_name='Inflation rate -> pi(t)/pibar = pigap')
+    pigap      //15 Inflation rate -> pi(t)/pibar = pigap
-    inom    ${i^{nom}}$ (long_name='Nominal interest rate')
+    inom    ${i^{nom}}$       //16 Nominal interest rate
-    inomnot             (long_name='Notional interest rate')
+    inomnot    //17 Notional interest rate
-    mc                  (long_name='Real marginal cost')
+    mc         //19 Real marginal cost
-    lam  ${\lambda}$    (long_name='Inverse marginal utility of wealth')
+    lam  ${\lambda}$      //20 Inverse marginal utility of wealth  
-    g                   (long_name='Growth shock')
+    g          //21 Growth shock       
-    s                   (long_name='Risk premium shock')
+    s          //22 Risk premium shock
-    mp                  (long_name='Monetary policy shock')
+    mp         //23 Monetary policy shock    
-    pi   ${\pi}$        (long_name='Observed inflation')
+    pi   ${\pi}$   //24 Observed inflation    
     @#if !(small_model)
-        x               (long_name='Investment')
+        x          //3  Investment
-        k               (long_name='Capital')
+        k          //4  Capital    
-        u               (long_name='Utilization cost')
+        u          //7  Utilization cost
-        ups             (long_name='Utilization choice')
+        ups        //8  Utilization choice
-        wg              (long_name='Real wage growth gap')    
+        wg         //9  Real wage growth gap    
-        xg              (long_name='Investment growth')
+        xg         //10 Investment growth
-        rk              (long_name='Real rental rate')
+        rk         //14 Real rental rate
-        q               (long_name='Tobins q')
+        q          //18 Tobins q   
     @#endif
 ;
 varexo          
-    epsg    ${\varepsilon_g}$   (long_name='Productivity growth shock')
+    epsg    ${\varepsilon_g}$   // Productivity growth shock
-    epsi                        (long_name='Notional interest rate shock')
+    epsi       // Notional interest rate shock
-    epss                        (long_name='Risk premium shock')
+    epss       // Risk premium shock
 ;        
 parameters
     // Calibrated Parameters    
-    beta    $\beta$    (long_name='Discount factor')
+    beta    $\beta$    // Discount factor
-    chi                 (long_name='Labor disutility scale')
+    chi         // Labor disutility scale
-    thetap              (long_name='Elasticity of subs. between intermediate goods')
+    thetap      // Elasticity of subs. between intermediate goods
-    thetaw              (long_name='Elasticity of subs. between labor types')
+    thetaw      // Elasticity of subs. between labor types
-    nbar                (long_name='Steady state labor')
+    nbar        // Steady state labor
-    eta                 (long_name='Inverse frish elasticity of labor supply')
+    eta         // Inverse frish elasticity of labor supply
-    delta               (long_name='Depreciation')
+    delta       // Depreciation
-    alpha               (long_name='Capital share')
+    alpha       // Capital share
-    gbar                (long_name='Mean growth rate')
+    gbar        // Mean growth rate
-    pibar               (long_name='Inflation target')
+    pibar       // Inflation target
-    inombar             (long_name='Steady gross nom interest rate')
+    inombar     // Steady gross nom interest rate    
-    inomlb              (long_name='Effective lower bound on gross nominal interest rate')
+    inomlb      // Effective lower bound on gross nominal interest rate    
-    sbar                (long_name='Average risk premium')
+    sbar        // Average risk premium
     // Parameters for DGP and Estimated parameters
-    varphip             (long_name='Rotemberg price adjustment cost')
+    varphip     // Rotemberg price adjustment cost
-    varphiw             (long_name='Rotemberg wage adjustment cost')
+    varphiw     // Rotemberg wage adjustment cost
-    h          	        (long_name='Habit persistence')
+    h          	// Habit persistence
-    rhos                (long_name='Persistence')
+    rhos        // Persistence
-    rhoi    	        (long_name='Persistence')
+    rhoi    	// Persistence
-    sigz                (long_name='Standard deviation technology')
+    sigz        // Standard deviation technology
-    sigs                (long_name='Standard deviation risk premia')
+    sigs        // Standard deviation risk premia
-    sigi                (long_name='Standard deviation mon pol')
+    sigi        // Standard deviation mon pol
-    phipi               (long_name='Inflation responsiveness')
+    phipi       // Inflation responsiveness
-    phiy                (long_name='Output responsiveness')
+    phiy        // Output responsiveness
-    nu                  (long_name='Investment adjustment cost')
+    nu          // Investment adjustment cost
-    sigups              (long_name='Utilization')
+    sigups      // Utilization    
  ;
 
 
@@ -316,34 +316,36 @@ varobs yg inom pi;
 
     // forecast starting from period 42, zero shocks (default)
     smoother2histval(period=42);
-    [forecast, error_flag] = occbin.forecast(options_,M_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state,8);
+    [oo, error_flag] = occbin.forecast(options_,M_,oo_,8);
     // forecast with stochastic shocks
     options_.occbin.forecast.qmc=true;
     options_.occbin.forecast.replic=127;
-    [forecast1, error_flag] = occbin.forecast(options_,M_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state,8);
+    [oo1, error_flag] = occbin.forecast(options_,M_,oo_,8);
-
-    figure('Name','OccBin: Forecasts')
-    subplot(2,1,1)
-    plot(1:8,forecast.piecewise.Mean.inom,'b-',1:8,forecast1.piecewise.Mean.inom,'r--')
-    subplot(2,1,2)
-    plot(1:8,forecast.piecewise.Mean.y,'b-',1:8,forecast1.piecewise.Mean.y,'r--')
 
     // GIRF given states in 42 and shocks in 43
     t0=42;
     options_.occbin.irf.exo_names=M_.exo_names;
     options_.occbin.irf.t0=t0;
-    oo_.occbin.irfs = occbin.irf(M_,oo_,options_);
+    oo_ = occbin.irf(M_,oo_,options_);
 
-    var_list_ = {'c','n','y','pigap','inom','inomnot'};
+    vars_irf = {
+    'c', 'consumption'    
+    'n', 'labor'    
+    'y', 'output'    
+    'pigap', 'inflation rate'
+    'inom', 'interest rate'  
+    'inomnot', 'shadow rate'
+    };
 
+    options_.occbin.plot_irf.exo_names = M_.exo_names;
+    options_.occbin.plot_irf.endo_names = vars_irf(:,1);
+    options_.occbin.plot_irf.endo_names_long = vars_irf(:,2);
     // if you want to scale ...
     // options_occbin_.plot_irf.endo_scaling_factor = vars_irf(:,3);
     options_.occbin.plot_irf.simulname = ['t0_' int2str(t0)];
-    options_.irf=40;
+    options_.occbin.plot_irf.tplot = min(40,options_.irf);
-    occbin.plot_irfs(M_,oo_.occbin.irfs,options_,var_list_);
+    occbin.plot_irfs(M_,oo_,options_);
-    var_list_={};
+
-    options_.occbin.plot_irf.simulname = ['t0_' int2str(t0) '_full'];
-    occbin.plot_irfs(M_,oo_.occbin.irfs,options_,var_list_);
     oo0=oo_;
     // use smoother_redux
     estimation(
@@ -372,7 +374,7 @@ varobs yg inom pi;
             consider_all_endogenous,heteroskedastic_filter,filter_step_ahead=[1],smoothed_state_uncertainty);
             
     // show initial condition effect of IF
-    figure('Name','OccBin: Smoothed shocks')
+    figure,
     subplot(221)
     plot([oo0.SmoothedShocks.epsg oo_.SmoothedShocks.epsg]), title('epsg')
     subplot(222)
@@ -380,7 +382,7 @@ varobs yg inom pi;
     subplot(223)
     plot([oo0.SmoothedShocks.epss oo_.SmoothedShocks.epss]), title('epss')
     legend('PKF','IF')
-    figure('Name','OccBin: Smoothed Variables')
+    figure,
     subplot(221)
     plot([oo0.SmoothedVariables.inom oo_.SmoothedVariables.inom]), title('inom')
     subplot(222)

windows/build.sh

@@ -5,7 +5,7 @@
 # The binaries are cross compiled for Windows (64-bit), Octave and MATLAB
 # (all supported versions).
 
-# Copyright © 2017-2024 Dynare Team
+# Copyright © 2017-2023 Dynare Team
 #
 # This file is part of Dynare.
 #
@@ -127,8 +127,8 @@ mkdir "$ZIPDIR"/preprocessor
 cp -p build-win-matlab/preprocessor/src/dynare-preprocessor.exe "$ZIPDIR"/preprocessor
 cp -pr matlab "$ZIPDIR"
 cp -p build-win-matlab/dynare_version.m "$ZIPDIR"/matlab
-mkdir -p "$ZIPDIR"/matlab/dseries/externals/x13/windows/64
+mkdir -p "$ZIPDIR"/matlab/modules/dseries/externals/x13/windows/64
-cp -p windows/deps/lib64/x13as/x13as.exe "$ZIPDIR"/matlab/dseries/externals/x13/windows/64
+cp -p windows/deps/lib64/x13as/x13as.exe "$ZIPDIR"/matlab/modules/dseries/externals/x13/windows/64
 cp -pr examples "$ZIPDIR"
 mkdir -p "$ZIPDIR"/scripts
 cp -p scripts/dynare.el "$ZIPDIR"/scripts

windows/deps/Makefile

@@ -1,4 +1,4 @@
-# Copyright © 2017-2024 Dynare Team
+# Copyright © 2017-2023 Dynare Team
 #
 # This file is part of Dynare.
 #
@@ -49,7 +49,7 @@ clean-all: clean-lib clean-src clean-tar
 
 tarballs/slicot-$(SLICOT_VERSION).tar.gz:
 	mkdir -p tarballs
-	wget $(WGET_OPTIONS) -O $@ https://deb.debian.org/debian/pool/main/s/slicot/slicot_$(SLICOT_VERSION).orig.tar.xz
+	wget $(WGET_OPTIONS) -O $@ https://deb.debian.org/debian/pool/main/s/slicot/slicot_$(SLICOT_VERSION).orig.tar.gz
 
 sources64/slicot-$(SLICOT_VERSION)-with-64bit-integer: tarballs/slicot-$(SLICOT_VERSION).tar.gz
 	rm -rf sources64/slicot-*-with-64bit-integer
@@ -64,13 +64,13 @@ sources64/slicot-$(SLICOT_VERSION)-with-32bit-integer-and-underscore: tarballs/s
 	touch $@
 
 lib64/Slicot/without-underscore/libslicot64_pic.a: sources64/slicot-$(SLICOT_VERSION)-with-64bit-integer
-	make -C $< -f makefile_Unix lib SLICOTLIB=../libslicot64_pic.a OPTS="$(FFLAGS) -fno-underscoring -fdefault-integer-8" FORTRAN=x86_64-w64-mingw32-gfortran LOADER=x86_64-w64-mingw32-gfortran ARCH=x86_64-w64-mingw32-ar
+	make -C $< lib SLICOTLIB=../libslicot64_pic.a OPTS="$(FFLAGS) -fno-underscoring -fdefault-integer-8" FORTRAN=x86_64-w64-mingw32-gfortran LOADER=x86_64-w64-mingw32-gfortran ARCH=x86_64-w64-mingw32-ar
 	x86_64-w64-mingw32-strip --strip-debug $</libslicot64_pic.a
 	mkdir -p $(dir $@)
 	cp $</libslicot64_pic.a $@
 
 lib64/Slicot/with-underscore/libslicot_pic.a: sources64/slicot-$(SLICOT_VERSION)-with-32bit-integer-and-underscore
-	make -C $< -f makefile_Unix lib SLICOTLIB=../libslicot_pic.a OPTS="$(FFLAGS)" FORTRAN=x86_64-w64-mingw32-gfortran LOADER=x86_64-w64-mingw32-gfortran ARCH=x86_64-w64-mingw32-ar
+	make -C $< lib SLICOTLIB=../libslicot_pic.a OPTS="$(FFLAGS)" FORTRAN=x86_64-w64-mingw32-gfortran LOADER=x86_64-w64-mingw32-gfortran ARCH=x86_64-w64-mingw32-ar
 	x86_64-w64-mingw32-strip --strip-debug $</libslicot_pic.a
 	mkdir -p $(dir $@)
 	cp $</libslicot_pic.a $@

windows/deps/versions.mk

@@ -1,4 +1,4 @@
-SLICOT_VERSION = 5.9~20240205.gita037f7e
+SLICOT_VERSION = 5.0+20101122
 X13AS_VERSION = 1-1-b60
 
 OCTAVE_VERSION = 8.4.0

windows/dynare.nsi

@@ -52,7 +52,7 @@ Section "Dynare core (preprocessor and M-files)"
  File README.txt ..\NEWS.md ..\license.txt
 
  SetOutPath $INSTDIR\matlab
- File /r ..\matlab\*.m ..\matlab\*.json ..\build-win-matlab\dynare_version.m
+ File /r ..\matlab\*.m ..\build-win-matlab\dynare_version.m
 
  SetOutPath $INSTDIR\preprocessor
  File ..\build-win-matlab\preprocessor\src\dynare-preprocessor.exe
@@ -60,7 +60,7 @@ Section "Dynare core (preprocessor and M-files)"
  SetOutPath $INSTDIR\matlab\preprocessor64
  File ..\matlab\preprocessor64\dynare_m.exe
 
- SetOutPath $INSTDIR\matlab\dseries\externals\x13\windows\64
+ SetOutPath $INSTDIR\matlab\modules\dseries\externals\x13\windows\64
  File deps\lib64\x13as\x13as.exe
 
  SetOutPath $INSTDIR\contrib