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stepan:dprior
stepan:6.x
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stepan:kalman-mex
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stepan:mr#2067
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stepan:mr#2005
stepan:fix-nonlinear-solvers
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2 Commits
master ... kalman-mex

Author SHA1 Message Date
Stéphane Adjemian (Argos) a5ccc7aa5b
Improve debug mode. 2023-11-05 19:26:29 +01:00
Normann Rion 17e1b60537
Kalman filter mex: routine and tests 2023-11-05 18:16:46 +01:00
895 changed files with 23772 additions and 29079 deletions
Show Stats Expand all files Collapse all files

27
.clang-format
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@ -1,27 +0,0 @@
# NB: whenever the present file is modified, the same modification should be
# applied to the copy in preprocessor.git
# For general information about our coding style, and the specific version
# of clang-format used, see:
# https://git.dynare.org/Dynare/dynare/-/wikis/CodingGuidelines#c-code
# For the list of options of clang-format, see:
# https://clang.llvm.org/docs/ClangFormatStyleOptions.html
# Effective configuration can be obtained with:
# clang-format --dump-config
Language: Cpp
Standard: c++20
ColumnLimit: 100
BasedOnStyle: GNU
AllowShortFunctionsOnASingleLine: None
AlwaysBreakTemplateDeclarations: Yes
BreakConstructorInitializers: AfterColon
BreakInheritanceList: AfterColon
Cpp11BracedListStyle: true
DeriveLineEnding: false
IndentPPDirectives: AfterHash
PackConstructorInitializers: NextLine
PPIndentWidth: 1
PointerAlignment: Left
SpaceAfterTemplateKeyword: false
SpaceBeforeParens: ControlStatements
SpaceBeforeCpp11BracedList: true

8
.clang-tidy
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@ -1,8 +0,0 @@
# NB: to use clang-tidy on the MEX source code, make sure that you have
# libomp-dev installed (the LLVM implementation of OpenMP)
# TODO: add the following check families:
# - bugprone-*
# - cppcoreguidelines-
Checks: 'performance-*,modernize-*,-modernize-use-trailing-return-type,-clang-diagnostic-unqualified-std-cast-call'

2
.dir-locals.el
View File

@ -1,8 +1,6 @@
((c-mode . ((indent-tabs-mode . nil)
(fill-column . 100)
(c-file-style . "gnu")))
(c++-mode . ((indent-tabs-mode . nil)
(fill-column . 100)
(c-file-style . "gnu")))
(makefile-mode . ((indent-tabs-mode . t)))
(octave-mode . ((indent-tabs-mode . nil)

1
.gitignore vendored
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@ -31,5 +31,4 @@
build-doc
# Emacs stuff
TAGS
scripts/dynare.elc

30
.gitlab-ci.yml
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@ -2,23 +2,21 @@ variables:
GIT_SUBMODULE_STRATEGY: recursive
TERM: linux
MATLAB_VERSION: R2023b
OLD_MATLAB_VERSION: R2018b
# To ensure that "false && true" fails, see https://gitlab.com/gitlab-org/gitlab-runner/-/issues/25394#note_412609647
FF_ENABLE_BASH_EXIT_CODE_CHECK: 'true'
OLD_MATLAB_VERSION: R2014a
# The next stanza creates the version number used for the source tarball and the
# binary packages. Here are the following possible cases:
# - if VERSION was already set (when manually running a pipeline), use it
# - if we are in the official Dynare repository:
# + if on a tag: use the tag
# + if on master: use 7-unstable-$TIMESTAMP-$COMMIT
# + if on master: use 6-unstable-$TIMESTAMP-$COMMIT
# + on another branch: use $BRANCH-$TIMESTAMP-$COMMIT
# - if in a personal repository: use $USER-$TIMESTAMP-$COMMIT
before_script:
- 'if [[ -z $VERSION ]] && [[ $CI_PROJECT_NAMESPACE == Dynare ]] && [[ -n $CI_COMMIT_TAG ]]; then export VERSION=$CI_COMMIT_TAG; fi'
- 'if [[ -z $VERSION ]] && [[ $CI_PROJECT_NAMESPACE == Dynare ]] && [[ $CI_COMMIT_REF_NAME == master ]]; then export VERSION=7-unstable-$(date +%F-%H%M)-$CI_COMMIT_SHORT_SHA; fi'
- 'if [[ -z $VERSION ]] && [[ $CI_PROJECT_NAMESPACE == Dynare ]]; then export VERSION=$CI_COMMIT_REF_NAME-$(date +%F-%H%M)-$CI_COMMIT_SHORT_SHA; fi'
- 'if [[ -z $VERSION ]]; then export VERSION=$CI_PROJECT_NAMESPACE-$(date +%F-%H%M)-$CI_COMMIT_SHORT_SHA; fi'
- '[[ -z $VERSION ]] && [[ $CI_PROJECT_NAMESPACE == Dynare ]] && [[ -n $CI_COMMIT_TAG ]] && export VERSION=$CI_COMMIT_TAG'
- '[[ -z $VERSION ]] && [[ $CI_PROJECT_NAMESPACE == Dynare ]] && [[ $CI_COMMIT_REF_NAME == master ]] && export VERSION=6-unstable-$(date +%F-%H%M)-$CI_COMMIT_SHORT_SHA'
- '[[ -z $VERSION ]] && [[ $CI_PROJECT_NAMESPACE == Dynare ]] && export VERSION=$CI_COMMIT_REF_NAME-$(date +%F-%H%M)-$CI_COMMIT_SHORT_SHA'
- '[[ -z $VERSION ]] && export VERSION=$CI_PROJECT_NAMESPACE-$(date +%F-%H%M)-$CI_COMMIT_SHORT_SHA'
stages:
- build
@ -51,12 +49,8 @@ build_doc:
stage: build
script:
- meson rewrite kwargs set project / version "$VERSION"
# Use a local copy of MathJax to avoid using CDN (which are a privacy issue)
# NB: Debian 12 “Bookworm” has MathJax 2. The following needs to be updated
# when Debian moves to MathJax 3.
- meson setup -Dbuild_for=octave -Dmathjax_path=mathjax/MathJax.js?config=TeX-AMS-MML_HTMLorMML build-doc
- meson setup -Dbuild_for=octave build-doc
- meson compile -v -C build-doc doc
- cp -dR /usr/share/javascript/mathjax build-doc/dynare-manual.html/_static/
artifacts:
paths:
- build-doc/
@ -66,6 +60,7 @@ pkg_source:
stage: pkg
script:
- meson rewrite kwargs set project / version "$VERSION"
- rm doc/manual/source/_static/mathjax && sed -i "/^mathjax_path *=/d" doc/manual/source/conf.py
- git commit -a -m "Source for $VERSION"
- meson setup -Dbuild_for=octave build-src
- meson dist -C build-src --no-tests
@ -177,13 +172,6 @@ test_octave:
needs: [ "build_octave" ]
when: manual
test_clang_format:
stage: test
script:
- meson setup -Dbuild_for=octave build-clang-format
- ninja -C build-clang-format clang-format-check
needs: []
# For the sign and deploy jobs, we don’t use the “needs” keyword, since we
# don’t want those jobs to start before the “test” and “pkg” stages have
# succeeded. Hence we stick to the “dependencies” keyword.
@ -216,6 +204,8 @@ deploy_manual_unstable:
dependencies:
- build_doc
script:
- rm -rf build-doc/dynare-manual.html/_static/mathjax
- ln -s /usr/share/javascript/mathjax build-doc/dynare-manual.html/_static/mathjax
- rsync --recursive --links --delete build-doc/dynare-manual.html/ /srv/www.dynare.org/manual-unstable/
deploy_snapshot_unstable:

8
.gitmodules vendored
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@ -11,9 +11,15 @@
path = matlab/utilities/tests
url = ../../Dynare/m-unit-tests.git
[submodule "matlab/modules/dseries"]
path = matlab/dseries
path = matlab/modules/dseries
url = ../../Dynare/dseries.git
branch = master
[submodule "matlab/modules/reporting"]
path = matlab/modules/reporting
url = ../../Dynare/reporting.git
[submodule "contrib/jsonlab"]
path = contrib/jsonlab
url = https://github.com/fangq/jsonlab.git
[submodule "preprocessor"]
path = preprocessor
url = ../../Dynare/preprocessor.git

402
NEWS.md
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@ -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)
===========================================

9
README.md
View File

@ -70,7 +70,7 @@ A number of tools and libraries are needed in order to recompile everything. You
- [GNU Octave](https://www.octave.org) with
- the development headers (if you want to compile the MEX for Octave)
- the development libraries corresponding to the [UMFPACK](https://people.engr.tamu.edu/davis/suitesparse.html) packaged with Octave (if you want to compile the MEX for Octave)
- the [statistics](https://octave.sourceforge.io/statistics/) package and, optionally, the [control](https://octave.sourceforge.io/control/), [io](https://octave.sourceforge.io/io/) and [optimization](https://octave.sourceforge.io/optim/) packages, either installed via your package manager or through [Octave Forge](https://octave.sourceforge.io/)
- Optionally, the [Control](https://octave.sourceforge.io/control/), [IO](https://octave.sourceforge.io/io/), [Optimization](https://octave.sourceforge.io/optim/) and [Statistics](https://octave.sourceforge.io/statistics/) package either installed via your package manager or through [Octave Forge](https://octave.sourceforge.io/).
- [Meson](https://mesonbuild.com), version 0.64.0 or later
- [Pkgconf](http://pkgconf.org/), or another pkg-config implementation
- [Bash](https://www.gnu.org/software/bash/)
@ -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
@ -480,7 +479,7 @@ If you want a certain version (e.g. 5.x) , then add `--single-branch --branch 5.
```sh
export BUILDDIR=build-matlab
export MATLABPATH=/Applications/MATLAB_R2023b.app
arch -$ARCH meson setup --native-file macOS/homebrew-native-$ARCH.ini -Dmatlab_path=$MATLABPATH -Dbuildtype=debugoptimized -Dfortran_args="['-B','$DYNAREDIR/slicot/lib']" $BUILDDIR
arch -$ARCH meson setup --native-file scripts/homebrew-native-$ARCH.ini -Dmatlab_path=$MATLABPATH -Dbuildtype=debugoptimized -Dfortran_args="['-B','$DYNAREDIR/slicot/lib']" $BUILDDIR
```
where you need to adapt the path to MATLAB.
Similarly, if you want to compile for Octave, replace the `-Dmatlab_path` option by `-Dbuild_for=octave`, and change the build directory to `build-octave`.
@ -515,4 +514,4 @@ e.g. by adding this to your mod file. Alternatively, you can create a `startup.m
## Docker
We offer a variety of pre-configured Docker containers for Dynare, pre-configured with Octave and MATLAB including all recommended toolboxes.
These are readily available for your convenience on [Docker Hub](https://hub.docker.com/r/dynare/dynare).
The `scripts/docker` folder contains [information and instructions](scripts/docker/README.md) to interact, built and customize the containers.
The docker folder contains [information and instructions](docker/README.md) to interact, built and customize the containers.

1
contrib/jsonlab Submodule

@ -0,0 +1 @@
Subproject commit 5a58fafdca239e61be4d7b2270307b2445bb571a

139
doc/gsa/gsa.tex
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@ -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
a Dynare estimation environment to be set up. Rather, \verb"sensitivity"
is the only command to run to
With respect to the previous version of the toolbox, in order to
work properly, the sensitivity analysis Toolbox \emph{no longer}
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
options of \verb"estimation". These are:
\verb"dynare_sensitivity" command also allows to indicate some
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
test analyses confronting the CDF of the sample fulfilling
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
test analyses confronting the CDF of the sample producing
indeterminacy with the CDF of the original prior sample;
\item[]\verb"<Dynare_file>_prior_stab_unst_SA_*.fig": plots of the Smirnov
test analyses confronting the CDF of the sample producing unstable
(explosive roots) behaviour with the CDF of the original prior
\item[]\verb"<DYNARE_file>_prior_stab_SA_*.fig": plots of the Smirnov
test analyses confronting the cdf of the sample fulfilling
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
test analyses confronting the cdf of the sample producing
indeterminacy with the cdf of the original prior sample;
\item[]\verb"<DYNARE_file>_prior_stab_unst_SA_*.fig": plots of the Smirnov
test analyses confronting the cdf of the sample producing unstable
(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
series, plots \emph{in red} the CDF of the log-likelihood
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
\item[]\verb"<DYNARE_file>_rmse_prior_lnlik*.fig": for each observed
series, plots \emph{in red} the cdf of the log-likelihood
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
sample; this allows to see the presence of some idiosyncratic
behaviour;
\item[]\verb"<Dynare_file>_rmse_prior_lnpost*.fig": for each observed
series, plots \emph{in red} the CDF of the log-posterior
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
\item[]\verb"<DYNARE_file>_rmse_prior_lnpost*.fig": for each observed
series, plots \emph{in red} the cdf of the log-posterior
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
sample; this allows to see idiosyncratic behaviour;
\item[]\verb"<Dynare_file>_rmse_prior_lnprior*.fig": for each observed
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
the sample and \emph{in blue }the CDF of the full sample; this
\item[]\verb"<DYNARE_file>_rmse_prior_lnprior*.fig": for each observed
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
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.

3
doc/internals/build_internal_documentation.m
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@ -2,6 +2,7 @@ function build_internal_documentation()
% The name of the function should be explicit...
datafiles = [];
datafiles = [ datafiles ; {'../../matlab/utilities/dataset'}, {'initialize_dataset'}];
datafiles = [ datafiles ; {'../../matlab/utilities/dataset'}, {'descriptive_statistics'}];
datafiles = [ datafiles ; {'../../matlab/utilities/dataset'}, {'compute_stdv'}];
datafiles = [ datafiles ; {'../../matlab/utilities/dataset'}, {'compute_cova'}];
@ -80,4 +81,4 @@ if rows(miscfiles)
fprintf(fid,'\n\n\n');
end
end
fclose(fid);
fclose(fid);

1
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@ -0,0 +1 @@
/usr/share/javascript/mathjax/

10
doc/manual/source/bibliography.rst
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@ -13,23 +13,20 @@ Bibliography
* Andrews, Donald W.K (1991): “Heteroskedasticity and autocorrelation consistent covariance matrix estimation”, *Econometrica*, 59(3), 817–858.
* Backus, David K., Patrick J. Kehoe, and Finn E. Kydland (1992): “International Real Business Cycles,” *Journal of Political Economy*, 100(4), 745–775.
* Baxter, Marianne and Robert G. King (1999): “Measuring Business Cycles: Approximate Band-pass Filters for Economic Time Series,” *Review of Economics and Statistics*, 81(4), 575–593.
* 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, Morris Davis and Peter Tulip (2000): “Polynomial Adjustment Costs in FRB/US,” *Unpublished manuscript*.
* 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*.
* 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.
* Christiano, Lawrence J., Martin 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.
* Christiano, Lawrence J., Mathias Trabandt and Karl Walentin (2011): “Introducing financial frictions and unemployment into a small open economy model,” *Journal of Economic Dynamics and Control*, 35(12), 1999–2041.
* Christoffel, Kai, Günter Coenen and Anders Warne (2010): “Forecasting with DSGE models,” *ECB Working Paper Series*, 1185.
* Collard, Fabrice (2001): “Stochastic simulations with Dynare: A practical guide”.
* 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 +46,6 @@ 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 (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.

9
doc/manual/source/conf.py
View File

@ -1,6 +1,6 @@
# -*- coding: utf-8 -*-
# Copyright © 2018-2024 Dynare Team
# Copyright © 2018-2022 Dynare Team
#
# This file is part of Dynare.
#
@ -31,10 +31,12 @@ templates_path = ['_templates']
html_static_path = ['_static']
mathjax_path = 'mathjax/MathJax.js?config=TeX-AMS-MML_HTMLorMML'
master_doc = 'index'
project = u'Dynare'
copyright = u'1996–2024 Dynare Team'
copyright = u'1996–2022 Dynare Team'
author = u'Dynare Team'
add_function_parentheses = False
@ -71,11 +73,12 @@ 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 = [

108
doc/manual/source/dynare-misc-commands.rst
View File

@ -8,20 +8,6 @@
Dynare misc commands
####################
.. 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 ;
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 ;
Puts the IRFs stored in ``oo_.irfs`` into vectors with the same name into the base workspace.
.. command:: prior_function(OPTIONS);
Executes a user-defined function on parameter draws from the prior
@ -230,97 +216,27 @@ 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
line option to be set.
displayed instead of the name of the parameters.
*Example*
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`
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`
::
::
>> 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;
|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.
diff(X) = -0.031459*(X(-1)-mX) + 0.1*Z - 0.2*R + u;

4
doc/manual/source/index.rst
View File

@ -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:
@ -26,7 +26,7 @@ The following people used to be members of the team:
* Ferhat Mihoubi
* George Perendia
Copyright © 1996-2024, Dynare Team.
Copyright © 1996-2023, 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 Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.

8
doc/manual/source/installation-and-configuration.rst
View File

@ -14,8 +14,8 @@ compilation steps are necessary in that case.
In order to run Dynare, you need one of the following:
* MATLAB, any version ranging from 9.5 (R2018b) to 23.2 (R2023b);
* GNU Octave, any version ranging from 7.1.0 to 8.4.0, with the ``statistics`` package
* MATLAB, any version ranging from 8.3 (R2014a) to 23.2 (R2023b);
* GNU Octave, any version ranging from 6.2.0 to 8.3.0, with the statistics package
from `Octave-Forge`_. Note however that the Dynare installer for Windows
requires a more specific version of Octave, as indicated on the download
page.
@ -26,8 +26,8 @@ extra features, but are in no way required:
* If under MATLAB: the Optimization Toolbox, the Statistics Toolbox,
the Control System Toolbox;
* If under Octave, the following `Octave-Forge`_ packages: ``optim``, ``io``,
``control``.
* If under Octave, the following `Octave-Forge`_ packages: ``optim, io,
control``.
Installation of Dynare

22
doc/manual/source/introduction.rst
View File

@ -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,
Johannes Pfeifer, Marco Ratto, Normann Rion and Sébastien Villemot (2024),
“Dynare: Reference Manual, Version 6,” *Dynare Working Papers*, 80, CEPREMAP
Stéphane Adjemian, Houtan Bastani, Michel Juillard, Frédéric Karamé,
Ferhat Mihoubi, Willi Mutschler, Johannes Pfeifer, Marco Ratto,
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,
author = {Adjemian, St\'ephane and Juillard, Michel and
Karam\'e, Fr\'ederic and Mutschler, Willi and
Pfeifer, Johannes and Ratto, Marco and
@TechReport{Adjemianetal2022,
author = {Adjemian, St\'ephane and Bastani, Houtan and
Juillard, Michel and Karam\'e, Fr\'ederic 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},
year = {2024},
title = {Dynare: Reference Manual Version 5},
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:

2
doc/manual/source/running-dynare.rst
View File

@ -554,7 +554,7 @@ by the ``dynare`` command.
executing the ``dynare`` command will leave variables containing
results in the workspace available for further processing. More
details are given under the relevant computing tasks. The
``M_``, ``oo_``, and ``options_`` structures are saved in a file
``M_``,``oo_``, and ``options_`` structures are saved in a file
called ``FILENAME_results.mat`` located in the ``MODFILENAME/Output`` folder.
If they exist, ``estim_params_``,
``bayestopt_``, ``dataset_``, ``oo_recursive_`` and

26
doc/manual/source/the-configuration-file.rst
View File

@ -15,16 +15,11 @@ related to the model (and hence not placed in the model file). At the
moment, it is only used when using Dynare to run parallel
computations.
On Linux and macOS, the configuration file is searched by default under
``dynare/dynare.ini`` in the configuration directories defined by the XDG
specification (typically ``$HOME/.config/dynare/dynare.ini`` for the
user-specific configuration and ``/etc/xdg/dynare/dynare.ini`` for the
system-wide configuration, the former having precedence over the latter). Under
Windows, the configuration file is searched by default in
``%APPDATA%\dynare\dynare.ini`` (typically
``c:\Users\USERNAME\AppData\Roaming\dynare\dynare.ini``). You can specify a non
standard location using the ``conffile`` option of the ``dynare`` command (see
:ref:`dyn-invoc`).
On Linux and macOS, the default location of the configuration file is
``$HOME/.dynare``, while on Windows it is ``%APPDATA%\dynare.ini``
(typically ``c:\Users\USERNAME\AppData\dynare.ini``). You
can specify a non standard location using the ``conffile`` option of
the ``dynare`` command (see :ref:`dyn-invoc`).
The parsing of the configuration file is case-sensitive and it should
take the following form, with each option/choice pair placed on a
@ -81,15 +76,8 @@ processing. Currently, there is only one option available.
.. option:: GlobalInitFile = PATH_AND_FILE
The location of a global initialization file that can be used to
customize some Dynare internals (typically default option values). This
is a MATLAB/Octave script.
If this option is not specified, Dynare will look for a
``global_init.m`` file in its 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).
The location of the global initialization file to be run at
the end of ``global_initialization.m``.
*Example*

1609
doc/manual/source/the-model-file.rst
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File diff suppressed because it is too large Load Diff

2
doc/manual/source/time-series.rst
View File

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

6
doc/manual/utils/dynare_dom.py
View File

@ -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

5
doc/manual/utils/dynare_lex.py
View File

@ -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',
'component', 'growth', 'auxname', 'kind'), prefix=r'\b', suffix=r'\s*\b'),Keyword.Reserved),
'verbatim','end','node','cluster','paths','hooks'), prefix=r'\b', suffix=r'\s*\b'),Keyword.Reserved),
# FIXME: Commands following multiline comments are not highlighted properly.
(words(commands + report_commands,

118
scripts/docker/Dockerfile → docker/Dockerfile
View File

@ -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 #
# provide a valid license, see https://git.dynare.org/dynare/dynare/docker/README.md#matlab-license. #
# The container is created using a network license, so no information on the license is inside the container #
# 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)
ARG MATLAB_RELEASE=R2023b
ARG DYNARE_RELEASE=6.0
# 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=R2023a
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)
RUN case "$DYNARE_RELEASE" in \
6.*) \
export DEBIAN_FRONTEND=noninteractive && \
apt-get update && \
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: $DYNARE_RELEASE. No dependencies will be installed." ;; \
esac
# Install common dependencies for Dynare and keep this layer small to reduce image size (apt cache cleanup)
RUN export DEBIAN_FRONTEND=noninteractive && \
apt-get update && \
apt-get install --no-install-recommends --yes \
# Install dynare dependencies.
RUN export DEBIAN_FRONTEND=noninteractive \
&& apt-get update \
&& apt-get install --no-install-recommends --yes \
build-essential \
gfortran \
libboost-graph-dev \
libgsl-dev \
@ -123,6 +94,8 @@ RUN export DEBIAN_FRONTEND=noninteractive && \
flex \
libfl-dev \
bison \
autoconf \
automake \
texlive \
texlive-publishers \
texlive-latex-extra \
@ -136,6 +109,7 @@ RUN export DEBIAN_FRONTEND=noninteractive && \
tex-gyre \
latexmk \
libjs-mathjax \
doxygen \
x13as \
liboctave-dev \
octave-control \
@ -149,30 +123,10 @@ RUN export DEBIAN_FRONTEND=noninteractive && \
ghostscript \
epstool \
git \
git-lfs \
&& apt-get clean \
&& apt-get autoremove \
&& rm -rf /var/lib/apt/lists/*
# Dynare 6.x is only compatible with Octave 7.1.0 to 8.4.0
# The current base image of R2023b ships is based on Ubuntu 22.04 which ships Octave 6.2.0,
# so we add an inofficial Octave PPA to install a compatible version
# Once the MATLAB containers are based on Ubuntu 24.04, we can remove this step and use the default Octave version from the Ubuntu repository
# Note: the pkg install -forge command takes a long time
RUN case "$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 ${DYNARE_RELEASE} --recurse-submodules https://git.dynare.org/dynare/dynare.git
# Compile Dynare
# Dynare 6.x: install meson 1.3.1 using python3-pip because meson package in the Ubuntu repositories is too old
# Once the MATLAB containers are based on Ubuntu 24.04, this step can be removed
RUN case "$DYNARE_RELEASE" in \
6.*) \
cd dynare && \
pip3 install meson==1.3.1 && \
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: $DYNARE_RELEASE. Compilation steps will be skipped." ;; \
esac
USER matlab
WORKDIR /home/matlab
RUN cd dynare \
&& autoreconf -si \
&& ./configure --with-matlab=${MATLAB_INSTALL_LOCATION} MATLAB_VERSION=${MATLAB_RELEASE} \
&& make -j$(($(nproc)+1))
# Add path of dynare to startup script for Octave
# Add path of dynare to startup script for Octave.
RUN echo "addpath /home/matlab/dynare/matlab" >> /home/matlab/.octaverc
# Add path of dynare to startup script for MATLAB
# Add path of dynare to startup script for MATLAB.
# Note that if startup.m file exists (in newer MATLAB containers), it is a MATLAB function
# and the last line is an "end", so we append the path to the second-to-last line
# For some reason we have to do this as root, otherwise the file is not writable
@ -255,6 +195,6 @@ RUN filename="/home/matlab/Documents/MATLAB/startup.m" && \
fi && \
chown matlab:matlab "$filename"
# Set user and work directory
# Set user and work directory.
USER matlab
WORKDIR /home/matlab

34
scripts/docker/README.md → docker/README.md
View File

@ -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 |
| 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 |
| latest | 5.4 | R2023a | 5.2.0 | Ubuntu 20.04 | mathworks/matlab:R2023a |
| 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:
```sh
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.
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.
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.
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=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:latest .
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 .

0
scripts/docker/docker-compose.yml → docker/docker-compose.yml
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38
examples/NK_baseline.mod
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@ -26,17 +26,13 @@
* The model is written in the beginning of period stock notation. To make the model
* conform with Dynare’s end of period stock notation, we use the
* predetermined_variables-command.
*
* The model has been implemented in detrended form, i.e. the \mu_{i,t} are actually
* the the growth rates of the original \mu_{i,t}^{orig} in the paper, i.e.
* log(\mu_{i,t})=log(\mu_{i,t}^{orig}/\mu_{i,t-1}^{orig})
*
*
* Please note that the following copyright notice only applies to this Dynare
* implementation of the model.
*/
/*
* Copyright © 2013-2023 Dynare Team
* Copyright © 2013-2020 Dynare Team
*
* This file is part of Dynare.
*
@ -155,12 +151,12 @@ gammmaPI =1.29;
PIbar = 1.01;
rhod = 0.12;
rhophi = 0.93;
sigma_A = exp(-3.97);
sigma_d = exp(-1.51);
sigma_A = -3.97;
sigma_d = -1.51;
sigma_phi =exp(-2.36);
sigma_mu =exp(-5.43);
sigma_m =exp(-5.85);
sigma_phi =-2.36;
sigma_mu =-5.43;
sigma_m =-5.85;
Lambdamu=3.4e-3;
LambdaA = 2.8e-3;
@ -218,7 +214,7 @@ mc=(1/(1-alppha))^(1-alppha)*(1/alppha)^alppha*w^(1-alppha)*r^alppha;
1=thetap*(PI(-1)^chi/PI)^(1-epsilon)+(1-thetap)*PIstar^(1-epsilon);
[name='Taylor Rule']
R/Rbar=(R(-1)/Rbar)^gammmaR*((PI/PIbar)^gammmaPI*((yd/yd(-1)*mu_z)/exp(LambdaYd))^gammmay)^(1-gammmaR)*exp(sigma_m*epsm);
R/Rbar=(R(-1)/Rbar)^gammmaR*((PI/PIbar)^gammmaPI*((yd/yd(-1)*mu_z)/exp(LambdaYd))^gammmay)^(1-gammmaR)*exp(epsm);
[name='Resource constraint']
yd=c+x+mu_z^(-1)*mu_I^(-1)*(gammma1*(u-1)+gammma2/2*(u-1)^2)*k;
@ -239,24 +235,24 @@ PIstarw=wstar/w;
//exogenous processes
[name='Preference Shock']
log(d)=rhod*log(d(-1))+sigma_d*epsd;
log(d)=rhod*log(d(-1))+epsd;
[name='Labor disutility Shock']
log(phi)=rhophi*log(phi(-1))+sigma_phi*epsphi;
log(phi)=rhophi*log(phi(-1))+epsphi;
[name='Investment specific technology']
log(mu_I)=Lambdamu+sigma_mu*epsmu_I;
log(mu_I)=Lambdamu+epsmu_I;
[name='Neutral technology']
log(mu_A)=LambdaA+sigma_A*epsA;
log(mu_A)=LambdaA+epsA;
[name='Defininition composite technology']
mu_z=mu_A^(1/(1-alppha))*mu_I^(alppha/(1-alppha));
end;
shocks;
var epsd; stderr 1;
var epsphi; stderr 1;
var epsmu_I; stderr 1;
var epsA; stderr 1;
var epsm; stderr 1;
var epsd; stderr exp(sigma_d);
var epsphi; stderr exp(sigma_phi);
var epsmu_I; stderr exp(sigma_mu);
var epsA; stderr exp(sigma_A);
var epsm; stderr exp(sigma_m);
end;
steady;

6
examples/dynare++/README.txt Normal file
View File

@ -0,0 +1,6 @@
Assuming that the dynare++ binary is in your PATH, you can run the example by using the following command
in a Command Prompt Window:
... > dynare++ example1.mod
Please, read the manual (doc\dynare++\dynare++-tutorial.pdf) for a description of the generated output.

45
examples/dynare++/example1.mod Normal file
View File

@ -0,0 +1,45 @@
/*
* This Dynare++ mod-file implements the RBC model with time-to-build
* described in Kamenik (2011): "DSGE Models with Dynare++. A Tutorial."
* Note that Dynare++ uses the same stock-at-the-end-of-period timing convention
* as the regular Dynare
*/
var Y, C, K, A, H, B;
varexo EPS, NU;
parameters beta, rho, alpha, delta, theta, psi, tau;
alpha = 0.36;
rho = 0.95;
tau = 0.025;
beta = 1/(1.03^0.25);
delta = 0.025;
psi = 0;
theta = 2.95;
model;
C*theta*H^(1+psi) = (1-alpha)*Y;
beta*exp(B)*C/exp(B(1))/C(1)*
(exp(B(1))*alpha*Y(1)/K(1)+1-delta) = 1;
Y = exp(A)*K^alpha*H^(1-alpha);
K = exp(B(-1))*(Y(-1)-C(-1)) + (1-delta)*K(-1);
A = rho*A(-1) + tau*B(-1) + EPS;
B = tau*A(-1) + rho*B(-1) + NU;
end;
initval;
A = 0;
B = 0;
H = ((1-alpha)/(theta*(1-(delta*alpha)/(1/beta-1+delta))))^(1/(1+psi));
Y = (alpha/(1/beta-1+delta))^(alpha/(1-alpha))*H;
K = alpha/(1/beta-1+delta)*Y;
C = Y - delta*K;
end;
vcov = [0.0002 0.00005;
0.00005 0.0001
];
order = 7;

113
examples/fs2000.mod
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@ -1,22 +1,22 @@
/*
* This file replicates the estimation of the cash in advance model (termed M1
* in the paper) described in Frank Schorfheide (2000): "Loss function-based
* This file replicates the estimation of the cash in advance model (termed M1
* in the paper) described in Frank Schorfheide (2000): "Loss function-based
* evaluation of DSGE models", Journal of Applied Econometrics, 15(6), 645-670.
*
* The data are taken from the replication package at
* http://dx.doi.org/10.15456/jae.2022314.0708799949
* The data are in file "fsdat_simul.m", and have been artificially generated.
* They are therefore different from the original dataset used by Schorfheide.
*
* The prior distribution follows the one originally specified in Schorfheide's
* paper. Note that the elicited beta prior for rho in the paper
* paper, except for parameter rho. In the paper, the elicited beta prior for rho
* implies an asymptote and corresponding prior mode at 0. It is generally
* recommended to avoid this extreme type of prior.
*
* Because the data are already logged and we use the loglinear option to conduct
* a full log-linearization, we need to use the logdata option.
* recommended to avoid this extreme type of prior. Some optimizers, for instance
* mode_compute=12 (Mathworks' particleswarm algorithm) may find a posterior mode
* with rho equal to zero. We lowered the value of the prior standard deviation
* (changing .223 to .100) to remove the asymptote.
*
* The equations are taken from J. Nason and T. Cogley (1994): "Testing the
* implications of long-run neutrality for monetary business cycle models",
* Journal of Applied Econometrics, 9, S37-S70, NC in the following.
* Journal of Applied Econometrics, 9, S37-S70.
* Note that there is an initial minus sign missing in equation (A1), p. S63.
*
* This implementation was originally written by Michel Juillard. Please note that the
@ -25,7 +25,7 @@
*/
/*
* Copyright © 2004-2023 Dynare Team
* Copyright © 2004-2017 Dynare Team
*
* This file is part of Dynare.
*
@ -43,71 +43,33 @@
* along with Dynare. If not, see <https://www.gnu.org/licenses/>.
*/
var m ${m}$ (long_name='money growth')
P ${P}$ (long_name='Price level')
c ${c}$ (long_name='consumption')
e ${e}$ (long_name='capital stock')
W ${W}$ (long_name='Wage rate')
R ${R}$ (long_name='interest rate')
k ${k}$ (long_name='capital stock')
d ${d}$ (long_name='dividends')
n ${n}$ (long_name='labor')
l ${l}$ (long_name='loans')
gy_obs ${\Delta \ln GDP}$ (long_name='detrended capital stock')
gp_obs ${\Delta \ln P}$ (long_name='detrended capital stock')
y ${y}$ (long_name='detrended output')
dA ${\Delta A}$ (long_name='TFP growth')
;
varexo e_a ${\epsilon_A}$ (long_name='TFP shock')
e_m ${\epsilon_M}$ (long_name='Money growth shock')
;
var m P c e W R k d n l gy_obs gp_obs y dA;
varexo e_a e_m;
parameters alp ${\alpha}$ (long_name='capital share')
bet ${\beta}$ (long_name='discount factor')
gam ${\gamma}$ (long_name='long-run TFP growth')
logmst ${\log(m^*)}$ (long_name='long-run money growth')
rho ${\rho}$ (long_name='autocorrelation money growth')
phi ${\phi}$ (long_name='labor weight in consumption')
del ${\delta}$ (long_name='depreciation rate')
;
parameters alp bet gam mst rho psi del;
% roughly picked values to allow simulating the model before estimation
alp = 0.33;
bet = 0.99;
gam = 0.003;
logmst = log(1.011);
mst = 1.011;
rho = 0.7;
phi = 0.787;
psi = 0.787;
del = 0.02;
model;
[name='NC before eq. (1), TFP growth equation']
dA = exp(gam+e_a);
[name='NC eq. (2), money growth rate']
log(m) = (1-rho)*logmst + rho*log(m(-1))+e_m;
[name='NC eq. (A1), Euler equation']
log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
[name='NC below eq. (A1), firm borrowing constraint']
W = l/n;
[name='NC eq. (A2), intratemporal labour market condition']
-(phi/(1-phi))*(c*P/(1-n))+l/n = 0;
[name='NC below eq. (A2), credit market clearing']
-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
[name='NC eq. (A3), credit market optimality']
1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
[name='NC eq. (18), aggregate resource constraint']
c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
[name='NC eq. (19), money market condition']
P*c = m;
[name='NC eq. (20), credit market equilibrium condition']
m-1+d = l;
[name='Definition TFP shock']
e = exp(e_a);
[name='Implied by NC eq. (18), production function']
y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
[name='Observation equation GDP growth']
gy_obs = dA*y/y(-1);
[name='Observation equation price level']
gp_obs = (P/P(-1))*m(-1)/dA;
end;
@ -119,41 +81,40 @@ end;
steady_state_model;
dA = exp(gam);
gst = 1/dA;
m = exp(logmst);
m = mst;
khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/m )^(-1);
nust = phi*m^2/( (1-alp)*(1-phi)*bet*gst^alp*khst^alp );
xist = ( ((khst*gst)^alp - (1-gst*(1-del))*khst)/mst )^(-1);
nust = psi*mst^2/( (1-alp)*(1-psi)*bet*gst^alp*khst^alp );
n = xist/(nust+xist);
P = xist + nust;
k = khst*n;
l = phi*m*n/( (1-phi)*(1-n) );
c = m/P;
d = l - m + 1;
l = psi*mst*n/( (1-psi)*(1-n) );
c = mst/P;
d = l - mst + 1;
y = k^alp*n^(1-alp)*gst^alp;
R = m/bet;
R = mst/bet;
W = l/n;
ist = y-c;
q = 1 - d;
e = 1;
gp_obs = m/dA;
gy_obs = dA;
end;
steady;
check;
% Table 1 of Schorfheide (2000)
estimated_params;
alp, beta_pdf, 0.356, 0.02;
bet, beta_pdf, 0.993, 0.002;
gam, normal_pdf, 0.0085, 0.003;
logmst, normal_pdf, 0.0002, 0.007;
rho, beta_pdf, 0.129, 0.223;
phi, beta_pdf, 0.65, 0.05;
mst, normal_pdf, 1.0002, 0.007;
rho, beta_pdf, 0.129, 0.100;
psi, beta_pdf, 0.65, 0.05;
del, beta_pdf, 0.01, 0.005;
stderr e_a, inv_gamma_pdf, 0.035449, inf;
stderr e_m, inv_gamma_pdf, 0.008862, inf;
@ -161,8 +122,14 @@ end;
varobs gp_obs gy_obs;
estimation(order=1, datafile=fs2000_data, loglinear,logdata, mode_compute=4, mh_replic=20000, nodiagnostic, mh_nblocks=2, mh_jscale=0.8, mode_check);
estimation(order=1, datafile=fsdat_simul, nobs=192, loglinear, mh_replic=2000, mh_nblocks=2, mh_jscale=0.8, mode_check);
%uncomment the following lines to generate LaTeX-code of the model equations
%write_latex_original_model(write_equation_tags);
%collect_latex_files;
/*
* The following lines were used to generate the data file. If you want to
* generate another random data file, comment the "estimation" line and uncomment
* the following lines.
*/
//stoch_simul(periods=200, order=1);
//datatomfile('fsdat_simul', {'gy_obs', 'gp_obs'});

215
examples/fs2000_data.m
View File

@ -1,215 +0,0 @@
%This file is a direct Matlab implementation of the loaddata.g and data.prn files
%of Schorfheide, Frank (2000): Loss function-based evaluation of DSGE models
%(replication data). Version: 1. Journal of Applied Econometrics. Dataset.
%http://dx.doi.org/10.15456/jae.2022314.0708799949
% Copyright: 2000-2022 Frank Schorfheide
% Copyright: 2023 Dynare Team
% License: CC BY 4.0
% (https://creativecommons.org/licenses/by/4.0/legalcode)
% Time series, extracted 05/04/00
% columms are quarterly data from 1949:IV to 1997:IV
% 1: GDPD = GROSS DOMESTIC PRODUCT:IMPLICIT PRICE DEFLATOR (INDEX,92=100)(T7.1)
% 2: GDPQ = GROSS DOMESTIC PRODUCT
% 3: GPOP = POPULATION, NIPA basis (THOUS.,NSA)
data_q=[18.02 1474.5 150.2
17.94 1538.2 150.9
18.01 1584.5 151.4
18.42 1644.1 152
18.73 1678.6 152.7
19.46 1693.1 153.3
19.55 1724 153.9
19.56 1758.2 154.7
19.79 1760.6 155.4
19.77 1779.2 156
19.82 1778.8 156.6
20.03 1790.9 157.3
20.12 1846 158
20.1 1882.6 158.6
20.14 1897.3 159.2
20.22 1887.4 160
20.27 1858.2 160.7
20.34 1849.9 161.4
20.39 1848.5 162
20.42 1868.9 162.8
20.47 1905.6 163.6
20.56 1959.6 164.3
20.62 1994.4 164.9
20.78 2020.1 165.7
21 2030.5 166.5
21.2 2023.6 167.2
21.33 2037.7 167.9
21.62 2033.4 168.7
21.71 2066.2 169.5
22.01 2077.5 170.2
22.15 2071.9 170.9
22.27 2094 171.7
22.29 2070.8 172.5
22.56 2012.6 173.1
22.64 2024.7 173.8
22.77 2072.3 174.5
22.88 2120.6 175.3
22.92 2165 176.045
22.91 2223.3 176.727
22.94 2221.4 177.481
23.03 2230.95 178.268
23.13 2279.22 179.694
23.22 2265.48 180.335
23.32 2268.29 181.094
23.4 2238.57 181.915
23.45 2251.68 182.634
23.51 2292.02 183.337
23.56 2332.61 184.103
23.63 2381.01 184.894
23.75 2422.59 185.553
23.81 2448.01 186.203
23.87 2471.86 186.926
23.94 2476.67 187.68
24 2508.7 188.299
24.07 2538.05 188.906
24.12 2586.26 189.631
24.29 2604.62 190.362
24.35 2666.69 190.954
24.41 2697.54 191.56
24.52 2729.63 192.256
24.64 2739.75 192.938
24.77 2808.88 193.467
24.88 2846.34 193.994
25.01 2898.79 194.647
25.17 2970.48 195.279
25.32 3042.35 195.763
25.53 3055.53 196.277
25.79 3076.51 196.877
26.02 3102.36 197.481
26.14 3127.15 197.967
26.31 3129.53 198.455
26.6 3154.19 199.012
26.9 3177.98 199.572
27.21 3236.18 199.995
27.49 3292.07 200.452
27.75 3316.11 200.997
28.12 3331.22 201.538
28.39 3381.86 201.955
28.73 3390.23 202.419
29.14 3409.65 202.986
29.51 3392.6 203.584
29.94 3386.49 204.086
30.36 3391.61 204.721
30.61 3422.95 205.419
31.02 3389.36 206.13
31.5 3481.4 206.763
31.93 3500.95 207.362
32.27 3523.8 208
32.54 3533.79 208.642
33.02 3604.73 209.142
33.2 3687.9 209.637
33.49 3726.18 210.181
33.95 3790.44 210.737
34.36 3892.22 211.192
34.94 3919.01 211.663
35.61 3907.08 212.191
36.29 3947.11 212.708
37.01 3908.15 213.144
37.79 3922.57 213.602
38.96 3879.98 214.147
40.13 3854.13 214.7
41.05 3800.93 215.135
41.66 3835.21 215.652
42.41 3907.02 216.289
43.19 3952.48 216.848
43.69 4044.59 217.314
44.15 4072.19 217.776
44.77 4088.49 218.338
45.57 4126.39 218.917
46.32 4176.28 219.427
47.07 4260.08 219.956
47.66 4329.46 220.573
48.63 4328.33 221.201
49.42 4345.51 221.719
50.41 4510.73 222.281
51.27 4552.14 222.933
52.35 4603.65 223.583
53.51 4605.65 224.152
54.65 4615.64 224.737
55.82 4644.93 225.418
56.92 4656.23 226.117
58.18 4678.96 226.754
59.55 4566.62 227.389
61.01 4562.25 228.07
62.59 4651.86 228.689
64.15 4739.16 229.155
65.37 4696.82 229.674
66.65 4753.02 230.301
67.87 4693.76 230.903
68.86 4615.89 231.395
69.72 4634.88 231.906
70.66 4612.08 232.498
71.44 4618.26 233.074
72.08 4662.97 233.546
72.83 4763.57 234.028
73.48 4849 234.603
74.19 4939.23 235.153
75.02 5053.56 235.605
75.58 5132.87 236.082
76.25 5170.34 236.657
76.81 5203.68 237.232
77.63 5257.26 237.673
78.25 5283.73 238.176
78.76 5359.6 238.789
79.45 5393.57 239.387
79.81 5460.83 239.861
80.22 5466.95 240.368
80.84 5496.29 240.962
81.45 5526.77 241.539
82.09 5561.8 242.009
82.68 5618 242.52
83.33 5667.39 243.12
84.09 5750.57 243.721
84.67 5785.29 244.208
85.56 5844.05 244.716
86.66 5878.7 245.354
87.44 5952.83 245.966
88.45 6010.96 246.46
89.39 6055.61 247.017
90.13 6087.96 247.698
90.88 6093.51 248.374
92 6152.59 248.928
93.18 6171.57 249.564
94.14 6142.1 250.299
95.11 6078.96 251.031
96.27 6047.49 251.65
97 6074.66 252.295
97.7 6090.14 253.033
98.31 6105.25 253.743
99.13 6175.69 254.338
99.79 6214.22 255.032
100.17 6260.74 255.815
100.88 6327.12 256.543
101.84 6327.93 257.151
102.35 6359.9 257.785
102.83 6393.5 258.516
103.51 6476.86 259.191
104.13 6524.5 259.738
104.71 6600.31 260.351
105.39 6629.47 261.04
106.09 6688.61 261.692
106.75 6717.46 262.236
107.24 6724.2 262.847
107.75 6779.53 263.527
108.29 6825.8 264.169
108.91 6882 264.681
109.24 6983.91 265.258
109.74 7020 265.887
110.23 7093.12 266.491
111 7166.68 266.987
111.43 7236.5 267.545
111.76 7311.24 268.171
112.08 7364.63 268.815];
%Compute growth rates: from 1950:I to 1997:IV
gy_obs=1000*data_q(:,2)./data_q(:,3); %real GDP per capita
gy_obs=diff(log(gy_obs));
gp_obs = diff(log(data_q(:,1))); %GDP deflator inflation

101
examples/pacmodel.mod
View File

@ -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)

142
license.txt
View File

@ -1,6 +1,6 @@
Format: https://www.debian.org/doc/packaging-manuals/copyright-format/1.0/
Upstream-Name: Dynare
Upstream-Contact: Dynare Team, whose members in 2024 are:
Upstream-Contact: Dynare Team, whose members in 2023 are:
- Stéphane Adjemian <stephane.adjemian@univ-lemans.fr>
- Michel Juillard <michel.juillard@mjui.fr>
- Frédéric Karamé <frederic.karame@univ-lemans.fr>
@ -23,7 +23,7 @@ Upstream-Contact: Dynare Team, whose members in 2024 are:
Source: https://www.dynare.org
Files: *
Copyright: 1996-2024 Dynare Team
Copyright: 1996-2023 Dynare Team
License: GPL-3+
Files: matlab/+occbin/IVF_core.m
@ -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
@ -113,7 +113,7 @@ Copyright: 1995 E.G.Tsionas
2015-2017 Dynare Team
License: GPL-3+
Files: matlab/estimation/endogenous_prior.m
Files: matlab/endogenous_prior.m
Copyright: 2011 Lawrence J. Christiano, Mathias Trabandt and Karl Walentin
2013-2017 Dynare Team
License: GPL-3+
@ -123,17 +123,12 @@ 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
License: GPL-3+
Files: matlab/+pruned_SS/commutation.m matlab/+pruned_SS/duplication.m
Files: matlab/commutation.m matlab/duplication.m
Copyright: 1997 Tom Minka <minka@microsoft.com>
2019-2020 Dynare Team
License: GPL-3+
@ -146,7 +141,7 @@ Comment: The original author gave authorization to change
the license from BSD-2-clause to GPL-3+ and redistribute
it under GPL-3+ with Dynare.
Files: matlab/+pruned_SS/uperm.m
Files: matlab/uperm.m
Copyright: 2014 Bruno Luong <brunoluong@yahoo.com>
2020 Dynare Team
License: GPL-3+
@ -154,9 +149,9 @@ Comment: The original author gave authorization to change
the license from BSD-2-clause to GPL-3+ and redistribute
it under GPL-3+ with Dynare.
Files: matlab/+pruned_SS/prodmom.m matlab/+pruned_SS/bivmom.m
Files: matlab/prodmom.m matlab/bivmom.m
Copyright: 2008-2015 Raymond Kan <kan@chass.utoronto.ca>
2019-2023 Dynare Team
2019-2020 Dynare Team
License: GPL-3+
Comment: The author gave authorization to redistribute
these functions under GPL-3+ with Dynare and would
@ -166,37 +161,57 @@ Comment: The author gave authorization to redistribute
Journal of Multivariate Analysis, 2008, vol. 99, issue 3,
pages 542-554.
Files: matlab/+gsa/Morris_Measure_Groups.m
matlab/+gsa/Sampling_Function_2.m
Files: matlab/gsa/Morris_Measure_Groups.m
matlab/gsa/Sampling_Function_2.m
Copyright: 2005 European Commission
2012-2013 Dynare Team
2012-2017 Dynare Team
License: GPL-3+
Comment: Written by Jessica Cariboni and Francesca Campolongo
Joint Research Centre, The European Commission,
Files: matlab/+gsa/cumplot.m
matlab/+gsa/monte_carlo_filtering.m
matlab/+gsa/skewness.m
matlab/+gsa/log_transform.m
matlab/+gsa/map_calibration.m
matlab/+gsa/map_identification.m
matlab/+gsa/monte_carlo_filtering_analysis.m
matlab/+gsa/boxplot.m
matlab/+gsa/prior_draw.m
matlab/+gsa/reduced_form_mapping.m
matlab/+gsa/reduced_form_screening.m
matlab/+gsa/scatter_mcf.m
matlab/+gsa/smirnov_test.m
matlab/+gsa/stability_mapping.m
matlab/+gsa/stability_mapping_univariate.m
matlab/+gsa/stability_mapping_bivariate.m
matlab/+gsa/standardize_columns.m
matlab/+gsa/tcrit.m
matlab/+gsa/teff.m
Files: matlab/gsa/cumplot.m
matlab/gsa/filt_mc_.m
matlab/gsa/gsa_plotmatrix.m
matlab/gsa/gsa_skewness.m
matlab/gsa/gsa_speed.m
matlab/gsa/log_trans_.m
matlab/gsa/map_calibration.m
matlab/gsa/map_ident_.m
matlab/gsa/mcf_analysis.m
matlab/gsa/myboxplot.m
matlab/gsa/myprctilecol.m
matlab/gsa/prior_draw_gsa.m
matlab/gsa/read_data.m
matlab/gsa/redform_map.m
matlab/gsa/redform_screen.m
matlab/gsa/scatter_mcf.m
matlab/gsa/smirnov.m
matlab/gsa/stab_map_.m
matlab/gsa/stab_map_1.m
matlab/gsa/stab_map_2.m
matlab/gsa/stand_.m
matlab/gsa/tcrit.m
matlab/gsa/teff.m
matlab/gsa/trank.m
Copyright: 2011-2018 European Commission
2011-2023 Dynare Team
2011-2018 Dynare Team
License: GPL-3+
Files: matlab/gsa/pick.m
Copyright: none
License: public-domain-jrc
This software has been developed at the Joint Research Centre of European Commission
by officers in the course of their official duties. This software is not subject to copyright
protection and is in the public domain. It is an experimental system. The Joint Research Centre
of European Commission assumes no responsibility whatsoever for its use by other parties
and makes no guarantees, expressed or implied, about its quality, reliability, or any other
characteristic. We would appreciate acknowledgement if the software is used.
Comment: This file is part of GLUEWIN.
The program has been developed by M. Ratto, European Commission, Joint Research Centre,
Institute for the Protection and Security of The Citizen, Technological and Economic Risk Management,
Applied Statistics, as a deliverable of the IMPACT project
(EC Fifth Framework Programme, SCA Project, IST-1999-11313, DG-INFSO).
Files: matlab/optimization/simpsa.m matlab/optimization/simpsaget.m matlab/optimization/simpsaset.m
Copyright: 2005 Henning Schmidt, FCC, henning@fcc.chalmers.se
2006 Brecht Donckels, BIOMATH, brecht.donckels@ugent.be
@ -247,14 +262,9 @@ License: BSD-2-clause
Files: matlab/utilities/graphics/colorspace.m
Copyright: 2005-2010 Pascal Getreuer
2017-2023 Dynare Team
2017 Dynare Team
License: BSD-2-clause
Files: examples/fs2000_data.m
Copyright: 2000-2022 Frank Schorfheide
2023 Dynare Team
License: CC-BY-SA-4.0
Files: doc/*.rst doc/*.tex doc/*.svg doc/*.pdf doc/*.bib
Copyright: 1996-2022 Dynare Team
License: GFDL-NIV-1.3+
@ -297,6 +307,28 @@ 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 +435,32 @@ 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

27
macOS/build.sh
View File

@ -1,6 +1,6 @@
#!/usr/bin/env bash
# Copyright © 2019-2024 Dynare Team
# Copyright © 2019-2023 Dynare Team
#
# This file is part of Dynare.
#
@ -37,6 +37,8 @@ else
# Remove /opt/homebrew/bin from PATH, so it does not intervene with the x86_64 compilations
path_remove PATH /opt/homebrew/bin
MATLAB_ARCH=maci64
# On x86_64 we need to differentiate between older and newer MATLAB versions
OLD_MATLAB_PATH=/Applications/MATLAB_R2016b.app
fi
MATLAB_PATH=/Applications/"$PKG_ARCH"/MATLAB_R2023b.app
@ -54,7 +56,7 @@ LIB64="$ROOTDIR"/macOS/deps/"$PKG_ARCH"/lib64
## - the macOS linker is different from GNU ld and does not have the equivalent of -Bstatic/-Bdynamic
## - libgfortran.spec does not include --as-needed on macOS, hence it will link the library anyways
## Also, it does not seem possible to override libgfortran.spec with the --specs option.
GCC_VERSION=$(sed -En "/^c[[:space:]]*=/s/c[[:space:]]*=[[:space:]]*'.*gcc-([0-9]+)'/\1/p" "$ROOTDIR"/macOS/homebrew-native-"$PKG_ARCH".ini)
GCC_VERSION=$(sed -En "/^c[[:space:]]*=/s/c[[:space:]]*=[[:space:]]*'.*gcc-([0-9]+)'/\1/p" "$ROOTDIR"/scripts/homebrew-native-"$PKG_ARCH".ini)
QUADMATH_DIR=$(mktemp -d)
ln -s "$BREWDIR"/opt/gcc/lib/gcc/"$GCC_VERSION"/libquadmath.a "$QUADMATH_DIR"
@ -67,12 +69,18 @@ cd "$ROOTDIR"
# NB: the addition of -Wl,-ld_classic is a workaround for https://github.com/mesonbuild/meson/issues/12282 (see also the native file)
common_meson_opts=(-Dbuild_for=matlab -Dbuildtype=release -Dprefer_static=true -Dfortran_args="[ '-B', '$LIB64/Slicot/' ]" \
-Dc_link_args="[ '-Wl,-ld_classic', '-L$QUADMATH_DIR' ]" -Dcpp_link_args="[ '-Wl,-ld_classic', '-L$QUADMATH_DIR' ]" -Dfortran_link_args="[ '-Wl,-ld_classic', '-L$QUADMATH_DIR' ]" \
--native-file macOS/homebrew-native-$PKG_ARCH.ini)
--native-file scripts/homebrew-native-$PKG_ARCH.ini)
# Build for MATLAB ⩾ R2018b (x86_64) and MATLAB ⩾ R2023b (arm64)
# Build for MATLAB ⩾ R2018a (x86_64) and MATLAB ⩾ R2023b (arm64)
arch -"$PKG_ARCH" meson setup "${common_meson_opts[@]}" -Dmatlab_path="$MATLAB_PATH" build-matlab --wipe
arch -"$PKG_ARCH" meson compile -v -C build-matlab
if [[ "$PKG_ARCH" == x86_64 ]]; then
# Build for MATLAB < R2018a
arch -"$PKG_ARCH" meson setup "${common_meson_opts[@]}" -Dmatlab_path="$OLD_MATLAB_PATH" build-old-matlab --wipe
arch -"$PKG_ARCH" meson compile -v -C build-old-matlab
fi
# If not in CI, build the docs
if [[ -z $CI ]]; then
arch -"$PKG_ARCH" meson compile -v -C build-matlab doc
@ -117,7 +125,8 @@ mkdir -p \
"$PKGFILES"/scripts \
"$PKGFILES"/contrib/ms-sbvar/TZcode
if [[ "$PKG_ARCH" == x86_64 ]]; then
mkdir -p "$PKGFILES"/mex/matlab/"$MATLAB_ARCH"-9.5-23.2
mkdir -p "$PKGFILES"/mex/matlab/"$MATLAB_ARCH"-8.3-9.3 \
"$PKGFILES"/mex/matlab/"$MATLAB_ARCH"-9.4-23.2
else
mkdir -p "$PKGFILES"/mex/matlab/"$MATLAB_ARCH"-23.2
fi
@ -138,20 +147,22 @@ mkdir -p "$PKGFILES"
ln -sf ../../preprocessor/dynare-preprocessor "$PKGFILES"/matlab/preprocessor64/dynare_m
if [[ "$PKG_ARCH" == x86_64 ]]; then
cp -L "$ROOTDIR"/build-matlab/*.mex"$MATLAB_ARCH" "$PKGFILES"/mex/matlab/"$MATLAB_ARCH"-9.5-23.2
cp -L "$ROOTDIR"/build-matlab/*.mex"$MATLAB_ARCH" "$PKGFILES"/mex/matlab/"$MATLAB_ARCH"-9.4-23.2
cp -L "$ROOTDIR"/build-old-matlab/*.mex"$MATLAB_ARCH" "$PKGFILES"/mex/matlab/"$MATLAB_ARCH"-8.3-9.3
else
cp -L "$ROOTDIR"/build-matlab/*.mex"$MATLAB_ARCH" "$PKGFILES"/mex/matlab/"$MATLAB_ARCH"-23.2
fi
cp -p "$ROOTDIR"/scripts/dynare.el "$PKGFILES"/scripts
cp -pr "$ROOTDIR"/contrib/ms-sbvar/TZcode/MatlabFiles "$PKGFILES"/contrib/ms-sbvar/TZcode
cp -pr "$ROOTDIR"/contrib/jsonlab "$PKGFILES"/contrib
cp "$ROOTDIR"/build-doc/*.pdf "$PKGFILES"/doc
cp "$ROOTDIR"/build-doc/preprocessor/doc/*.pdf "$PKGFILES"/doc
cp -r "$ROOTDIR"/build-doc/dynare-manual.html "$PKGFILES"/doc
mkdir -p "$PKGFILES"/matlab/dseries/externals/x13/macOS/64
cp -p "$ROOTDIR"/macOS/deps/"$PKG_ARCH"/lib64/x13as/x13as "$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/modules/dseries/externals/x13/macOS/64
cd "$ROOTDIR"/macOS/pkg

8
macOS/deps/Makefile
View File

@ -1,4 +1,4 @@
# Copyright © 2019-2024 Dynare Team
# Copyright © 2019-2023 Dynare Team
#
# This file is part of Dynare.
#
@ -22,7 +22,7 @@ DEPS_ARCH ?= x86_64 # use x86_64 by default
BREWDIR := $(if $(filter arm64,$(DEPS_ARCH)),/opt/homebrew,/usr/local)
GCC_VERSION = $(shell sed -En "/^c[[:space:]]*=/s/c[[:space:]]*=[[:space:]]*'.*gcc-([0-9]+)'/\1/p" ../homebrew-native-$(DEPS_ARCH).ini)
GCC_VERSION = $(shell sed -En "/^c[[:space:]]*=/s/c[[:space:]]*=[[:space:]]*'.*gcc-([0-9]+)'/\1/p" ../../scripts/homebrew-native-$(DEPS_ARCH).ini)
ROOT_PATH = $(realpath .)
@ -53,7 +53,7 @@ clean-all: clean-lib clean-src clean-tar
#
tarballs/slicot-$(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 $@

2
macOS/deps/versions.mk
View File

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

1
macOS/mexFunction-MacOSX.map Normal file
View File

@ -0,0 +1 @@
_mexFunction

6
matlab/+bvar/density.m
View File

@ -34,7 +34,7 @@ global oo_
oo_.bvar.log_marginal_data_density=NaN(maxnlags,1);
for nlags = 1:maxnlags
[ny, ~, posterior, prior] = bvar.toolbox(nlags);
[ny, nx, posterior, prior] = bvar.toolbox(nlags);
oo_.bvar.posterior{nlags}=posterior;
oo_.bvar.prior{nlags}=prior;
@ -75,8 +75,8 @@ function w = matrictint(S, df, XXi)
k=size(XXi,1);
ny=size(S,1);
[cx,p] = chol(XXi); %second output required to prevent error
[cs,p] = chol(S); %second output required to prevent error
[cx,p]=chol(XXi);
[cs,q]=chol(S);
if any(diag(cx)<100*eps)
error('singular XXi')

38
matlab/+bvar/forecast.m
View File

@ -33,7 +33,7 @@ global options_ oo_ M_
if options_.forecast == 0
error('bvar.forecast: you must specify "forecast" option')
end
[ny, nx, posterior, ~, forecast_data] = bvar.toolbox(nlags);
[ny, nx, posterior, prior, forecast_data] = bvar.toolbox(nlags);
sims_no_shock = NaN(options_.forecast, ny, options_.bvar_replic);
sims_with_shocks = NaN(options_.forecast, ny, options_.bvar_replic);
@ -95,7 +95,7 @@ end
if p > 0
skipline()
disp('Some of the VAR models sampled from the posterior distribution')
disp(['Some of the VAR models sampled from the posterior distribution'])
disp(['were found to be explosive (' num2str(p/options_.bvar_replic) ' percent).'])
skipline()
end
@ -119,7 +119,7 @@ OutputDirectoryName = CheckPath('graphs',M_.dname);
dyn_graph=bvar.graph_init(sprintf('BVAR forecasts (nlags = %d)', nlags), ny, {'b-' 'g-' 'g-' 'r-' 'r-'});
for i = 1:ny
dyn_graph=plot_graph(dyn_graph,[ sims_no_shock_median(:, i) ...
dyn_graph=dynare_graph(dyn_graph,[ sims_no_shock_median(:, i) ...
sims_no_shock_up_conf(:, i) sims_no_shock_down_conf(:, i) ...
sims_with_shocks_up_conf(:, i) sims_with_shocks_down_conf(:, i) ], ...
options_.varobs{i});
@ -154,8 +154,8 @@ end
% Store results
DirectoryName = [ M_.dname '/bvar_forecast' ];
if ~isfolder(DirectoryName)
if ~isfolder(M_.dname)
if ~isdir(DirectoryName)
if ~isdir(M_.dname)
mkdir(M_.dname);
end
mkdir(DirectoryName);
@ -183,31 +183,3 @@ for i = 1:length(options_.varobs)
oo_.bvar.forecast.rmse.(name) = rmse(i);
end
end
function dyn_graph=plot_graph(dyn_graph,y,tit,x)
% function plot_graph(dyn_graph, y,tit,x)
if nargin < 4
x = (1:size(y,1))';
end
nplot = dyn_graph.plot_nbr + 1;
if nplot > dyn_graph.max_nplot
figure('Name',dyn_graph.figure_name);
nplot = 1;
end
dyn_graph.plot_nbr = nplot;
subplot(dyn_graph.nr,dyn_graph.nc,nplot);
line_types = dyn_graph.line_types;
line_type = line_types{1};
for i=1:size(y,2)
if length(line_types) > 1
line_type = line_types{i};
end
plot(x,y(:,i),line_type);
hold on
end
title(tit);
hold off

6
matlab/+bvar/graph_init.m
View File

@ -1,5 +1,5 @@
function dyn_graph=graph_init(figure_name,nplot,line_types,line_width)
% function graph_init(figure_name,colors)
function dyn_graph=dynare_graph_init(figure_name,nplot,line_types,line_width)
% function dynare_graph_init(figure_name,colors)
% initializes set of graphs
%
% INPUTS:
@ -12,7 +12,7 @@ function dyn_graph=graph_init(figure_name,nplot,line_types,line_width)
% SPECIAL REQUIREMENT
% none
% Copyright © 2006-2023 Dynare Team
% Copyright © 2006-2017 Dynare Team
%
% This file is part of Dynare.
%

8
matlab/+bvar/irf.m
View File

@ -35,7 +35,7 @@ if nargin==1
identification = 'Cholesky';
end
[ny, nx, posterior] = bvar.toolbox(nlags);
[ny, nx, posterior, prior] = bvar.toolbox(nlags);
S_inv_upper_chol = chol(inv(posterior.S));
@ -96,7 +96,7 @@ end
if p > 0
skipline()
disp('Some of the VAR models sampled from the posterior distribution')
disp(['Some of the VAR models sampled from the posterior distribution'])
disp(['were found to be explosive (' int2str(p) ' samples).'])
skipline()
end
@ -130,7 +130,7 @@ end
% Save intermediate results
DirectoryName = [ M_.dname '/bvar_irf' ];
if ~isfolder(DirectoryName)
if ~isdir(DirectoryName)
mkdir('.',DirectoryName);
end
save([ DirectoryName '/simulations.mat'], 'sampled_irfs');
@ -146,4 +146,4 @@ for i=1:ny
oo_.bvar.irf.Upper_bound.(variable_name).(shock_name) = posterior_up_conf_irfs(j,i,:);
oo_.bvar.irf.Lower_bound.(variable_name).(shock_name) = posterior_down_conf_irfs(j,i,:);
end
end
end

6
matlab/+bvar/toolbox.m
View File

@ -1,5 +1,5 @@
function [ny, nx, posterior, prior, forecast_data] = toolbox(nlags)
%function [ny, nx, posterior, prior, forecast_data] = toolbox(nlags)
function [ny, nx, posterior, prior, forecast_data] = bvar_toolbox(nlags)
%function [ny, nx, posterior, prior, forecast_data] = bvar_toolbox(nlags)
% bvar_toolbox Routines shared between BVAR methods
% Computes several things for the estimations of a BVAR(nlags)
%
@ -42,7 +42,7 @@ function [ny, nx, posterior, prior, forecast_data] = toolbox(nlags)
% - bvar_prior_{tau,decay,lambda,mu,omega,flat,train}
% Copyright © 2003-2007 Christopher Sims
% Copyright © 2007-2023 Dynare Team
% Copyright © 2007-2017 Dynare Team
%
% This file is part of Dynare.
%

28
matlab/+estimate/nls.m
View File

@ -28,7 +28,7 @@ function nls(eqname, params, data, range, optimizer, varargin)
% equation must have NaN values in the object.
% [4] It is assumed that the residual is additive.
% Copyright © 2021-2023 Dynare Team
% Copyright © 2021-2022 Dynare Team
%
% This file is part of Dynare.
%
@ -179,6 +179,12 @@ write_residuals_routine(lhs, rhs, eqname, ipnames_, M_);
% Create a routine for evaluating the sum of squared residuals of the nonlinear model
write_ssr_routine(lhs, rhs, eqname, ipnames_, M_);
% Workaround for Octave bug https://savannah.gnu.org/bugs/?46282
% Octave will randomly fail to read the ssr_* file generated in the +folder
if isoctave && octave_ver_less_than('7')
path(path)
end
% Create a function handle returning the sum of square residuals for a given vector of parameters.
ssrfun = @(p) feval([M_.fname '.ssr_' eqname], p, DATA, M_, oo_);
@ -292,23 +298,23 @@ end
%
if is_gauss_newton
[params1, SSR] = gauss_newton(resfun, params0);
[params1, SSR, exitflag] = gauss_newton(resfun, params0);
elseif is_lsqnonlin
if ismember('levenberg-marquardt', varargin)
% Levenberg Marquardt does not handle boundary constraints.
[params1, SSR] = lsqnonlin(resfun, params0, [], [], optimset(varargin{:}));
[params1, SSR, ~, exitflag] = lsqnonlin(resfun, params0, [], [], optimset(varargin{:}));
else
[params1, SSR] = lsqnonlin(resfun, params0, bounds(:,1), bounds(:,2), optimset(varargin{:}));
[params1, SSR, ~, exitflag] = lsqnonlin(resfun, params0, bounds(:,1), bounds(:,2), optimset(varargin{:}));
end
else
% Estimate the parameters by minimizing the sum of squared residuals.
[params1, SSR] = dynare_minimize_objective(ssrfun, params0, ...
minalgo, ...
options_, ...
bounds, ...
parameter_names, ...
[], ...
[]);
[params1, SSR, exitflag] = dynare_minimize_objective(ssrfun, params0, ...
minalgo, ...
options_, ...
bounds, ...
parameter_names, ...
[], ...
[]);
end
% Revert local modifications to the options.

305
matlab/+gsa/map_identification.m
View File

@ -1,305 +0,0 @@
function map_identification(OutputDirectoryName,opt_gsa,M_,oo_,options_,estim_params_,bayestopt_)
% map_identification(OutputDirectoryName,opt_gsa,M_,oo_,options_,estim_params_,bayestopt_)
% Inputs
% - OutputDirectoryName [string] name of the output directory
% - opt_gsa [structure] GSA options structure
% - M_ [structure] Matlab's structure describing the model
% - oo_ [structure] Matlab's structure describing the results
% - options_ [structure] Matlab's structure describing the current options
% - estim_params_ [structure] characterizing parameters to be estimated
% - bayestopt_ [structure] describing the priors
% Written by Marco Ratto
% Joint Research Centre, The European Commission,
% marco.ratto@ec.europa.eu
% Copyright © 2012-2016 European Commission
% Copyright © 2012-2023 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/>.
fname_ = M_.fname;
dr=oo_.dr;
nliv = opt_gsa.morris_nliv;
itrans = opt_gsa.trans_ident;
np = size(estim_params_.param_vals,1);
pnames = M_.param_names(estim_params_.param_vals(:,1));
if opt_gsa.pprior
filetoload=[OutputDirectoryName '/' fname_ '_prior'];
else
filetoload=[OutputDirectoryName '/' fname_ '_mc'];
end
load(filetoload,'lpmat','lpmat0','istable','T','yys')
if ~isempty(lpmat0)
lpmatx=lpmat0(istable,:);
else
lpmatx=[];
end
Nsam = size(lpmat,1);
nshock = size(lpmat0,2);
npT = np+nshock;
fname_ = M_.fname;
if opt_gsa.load_ident_files==0
mss = yys(bayestopt_.mfys,:);
mss = gsa.teff(mss(:,istable),Nsam,istable);
yys = gsa.teff(yys(dr.order_var,istable),Nsam,istable);
if exist('T','var')
[vdec, cc, ac] = gsa.monte_carlo_moments(T, lpmatx, dr, M_, options_, estim_params_);
else
return
end
if opt_gsa.morris==2
pdraws = identification.run(M_,oo_,options_,bayestopt_,estim_params_,options_.options_ident,[lpmatx lpmat(istable,:)]);
if ~isempty(pdraws) && max(max(abs(pdraws-[lpmatx lpmat(istable,:)])))==0
disp(['Sample check OK. Largest difference: ', num2str(max(max(abs(pdraws-[lpmatx lpmat(istable,:)]))))]),
clear pdraws;
end
clear GAM gas
end
if opt_gsa.morris~=1 && M_.exo_nbr>1
ifig=0;
for j=1:M_.exo_nbr
if mod(j,6)==1
hh_fig=dyn_figure(options_.nodisplay,'name','Variance decomposition shocks');
ifig=ifig+1;
iplo=0;
end
iplo=iplo+1;
subplot(2,3,iplo)
gsa.boxplot(squeeze(vdec(:,j,:))',[],'.',[],10)
set(gca,'xticklabel',' ','fontsize',10,'xtick',1:size(options_.varobs,1))
set(gca,'xlim',[0.5 size(options_.varobs,1)+0.5])
set(gca,'ylim',[-2 102])
for ip=1:size(options_.varobs,1)
if options_.TeX
text(ip,-4,deblank(opt_gsa.varobs_tex(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','latex')
else
text(ip,-4,deblank(options_.varobs(ip,:)),'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
end
xlabel(' ')
ylabel(' ')
title(M_.exo_names{j},'interpreter','none')
if mod(j,6)==0 || j==M_.exo_nbr
dyn_saveas(hh_fig,[OutputDirectoryName,'/',fname_,'_vdec_exo_',int2str(ifig)],options_.nodisplay,options_.graph_format);
create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_vdec_exo_',int2str(ifig)],ifig,'Variance decomposition shocks','vdec_exo',options_.figures.textwidth*min(iplo/3,1))
end
end
end
for j=1:size(cc,1)
cc(j,j,:)=gsa.standardize_columns(squeeze(log(cc(j,j,:))))./2;
end
[vdec, ~, ir_vdec, ic_vdec] = gsa.teff(vdec,Nsam,istable);
[cc, ~, ir_cc, ic_cc] = gsa.teff(cc,Nsam,istable);
[ac, ~, ir_ac, ic_ac] = gsa.teff(ac,Nsam,istable);
nc1= size(T,2);
endo_nbr = M_.endo_nbr;
nstatic = M_.nstatic;
nspred = M_.nspred;
iv = (1:endo_nbr)';
ic = [ nstatic+(1:nspred) endo_nbr+(1:size(dr.ghx,2)-nspred) ]';
dr.ghx = T(:, 1:(nc1-M_.exo_nbr),1);
dr.ghu = T(:, (nc1-M_.exo_nbr+1):end, 1);
[Aa,Bb] = kalman_transition_matrix(dr,iv,ic);
A = zeros(size(Aa,1),size(Aa,2)+size(Aa,1),length(istable));
if ~isempty(lpmatx)
M_=gsa.set_shocks_param(M_,estim_params_,lpmatx(1,:));
end
A(:,:,1)=[Aa, triu(Bb*M_.Sigma_e*Bb')];
for j=2:length(istable)
dr.ghx = T(:, 1:(nc1-M_.exo_nbr),j);
dr.ghu = T(:, (nc1-M_.exo_nbr+1):end, j);
[Aa,Bb] = kalman_transition_matrix(dr, iv, ic);
if ~isempty(lpmatx)
M_=gsa.set_shocks_param(M_,estim_params_,lpmatx(j,:));
end
A(:,:,j)=[Aa, triu(Bb*M_.Sigma_e*Bb')];
end
clear T
clear lpmatx
[yt, j0]=gsa.teff(A,Nsam,istable);
yt = [yys yt];
if opt_gsa.morris==2
clear TAU A
else
clear A
end
save([OutputDirectoryName,'/',fname_,'_main_eff.mat'],'ac','cc','vdec','yt','mss')
else %load identification files
load([OutputDirectoryName,'/',fname_,'_main_eff.mat'],'ac','cc','vdec','yt','mss')
end
if opt_gsa.morris==1
if ~isempty(vdec)
if opt_gsa.load_ident_files==0
SAMorris=NaN(npT,3,size(vdec,2));
for i=1:size(vdec,2)
[~, SAMorris(:,:,i)] = gsa.Morris_Measure_Groups(npT, [lpmat0 lpmat], vdec(:,i),nliv);
end
SAvdec = squeeze(SAMorris(:,1,:))';
save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAvdec','vdec','ir_vdec','ic_vdec')
else
load([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAvdec')
end
hh_fig = dyn_figure(options_.nodisplay,'name','Screening identification: variance decomposition');
gsa.boxplot(SAvdec,[],'.',[],10)
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
if options_.TeX
[~, param_name_tex_temp]= get_the_name(ip,options_.TeX,M_,estim_params_,options_.varobs);
text(ip,-2,param_name_tex_temp,'rotation',90,'HorizontalAlignment','right','interpreter','latex')
else
text(ip,-2,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
end
xlabel(' ')
title('Elementary effects variance decomposition')
dyn_saveas(hh_fig,[OutputDirectoryName,'/',fname_,'_morris_vdec'],options_.nodisplay,options_.graph_format);
create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_morris_vdec'],1,'Screening identification: variance decomposition','morris_vdec',1)
else
save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'vdec')
end
if opt_gsa.load_ident_files==0
ccac = [mss cc ac];
SAMorris=NaN(npT,3,size(ccac,2));
for i=1:size(ccac,2)
[~, SAMorris(:,:,i)] = gsa.Morris_Measure_Groups(npT, [lpmat0 lpmat], [ccac(:,i)],nliv);
end
SAcc = squeeze(SAMorris(:,1,:))';
SAcc = SAcc./(max(SAcc,[],2)*ones(1,npT));
save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAcc','cc','ir_cc','ic_cc','-append')
save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'ac','ir_ac','ic_ac','-append')
else
load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAcc','cc','ir_cc','ic_cc')
load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'ac','ir_ac','ic_ac')
end
hh_fig=dyn_figure(options_.nodisplay,'name','Screening identification: theoretical moments');
gsa.boxplot(SAcc,[],'.',[],10)
set(gca,'xticklabel',' ','fontsize',10,'xtick',1:npT)
set(gca,'xlim',[0.5 npT+0.5])
set(gca,'ylim',[0 1])
set(gca,'position',[0.13 0.2 0.775 0.7])
for ip=1:npT
if options_.TeX
[~, param_name_tex_temp]= get_the_name(ip,options_.TeX,M_,estim_params_,options_.varobs);
text(ip,-0.02,param_name_tex_temp,'rotation',90,'HorizontalAlignment','right','interpreter','latex')
else
text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
end
xlabel(' ')
title('Elementary effects in the moments')
dyn_saveas(hh_fig,[OutputDirectoryName,'/',fname_,'_morris_moments'],options_.nodisplay,options_.graph_format);
create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_morris_moments'],1,'Screening identification: theoretical moments','morris_moments',1)
if opt_gsa.load_ident_files==0
SAMorris=NaN(npT,3,j0);
for j=1:j0
[~, SAMorris(:,:,j)] = gsa.Morris_Measure_Groups(npT, [lpmat0 lpmat], yt(:,j),nliv);
end
SAM = squeeze(SAMorris(1:end,1,:));
SAnorm=NaN(npT,j0);
irex=NaN(j0);
for j=1:j0
SAnorm(:,j)=SAM(:,j)./max(SAM(:,j));
irex(j)=length(find(SAnorm(:,j)>0.01));
end
SAMmu = squeeze(SAMorris(1:end,2,:));
SAmunorm=NaN(npT,j0);
for j=1:j0
SAmunorm(:,j)=SAMmu(:,j)./max(SAM(:,j)); % normalised w.r.t. mu*
end
SAMsig = squeeze(SAMorris(1:end,3,:));
SAsignorm=NaN(npT,j0);
for j=1:j0
SAsignorm(:,j)=SAMsig(:,j)./max(SAMsig(:,j));
end
save([OutputDirectoryName,'/',fname_,'_morris_IDE.mat'],'SAnorm','SAmunorm','SAsignorm','-append')
else
load([OutputDirectoryName,'/',fname_,'_morris_IDE'],'SAnorm')
end
hh_fig=dyn_figure(options_.nodisplay,'name','Screening identification: model');
gsa.boxplot(SAnorm',[],'.',[],10)
set(gca,'xticklabel',' ','fontsize',10,'xtick',1:npT)
set(gca,'xlim',[0.5 npT+0.5])
set(gca,'ylim',[0 1])
set(gca,'position',[0.13 0.2 0.775 0.7])
xlabel(' ')
for ip=1:npT
if options_.TeX
[~, param_name_tex_temp]= get_the_name(ip,options_.TeX,M_,estim_params_,options_.varobs);
text(ip,-0.02,param_name_tex_temp,'rotation',90,'HorizontalAlignment','right','interpreter','latex')
else
text(ip,-0.02,bayestopt_.name{ip},'rotation',90,'HorizontalAlignment','right','interpreter','none')
end
end
xlabel(' ')
title('Elementary effects in the model')
dyn_saveas(hh_fig,[OutputDirectoryName,'/',fname_,'_morris_par'],options_.nodisplay,options_.graph_format);
create_TeX_loader(options_,[OutputDirectoryName,'/',fname_,'_morris_par'],1,'Screening identification: model','morris_par',1)
elseif opt_gsa.morris==3
return
elseif opt_gsa.morris==2 % ISKREV stuff
return
else
error('gsa/map_identification: unsupported option morris=%u',opt_gsa.morris)
end
function []=create_TeX_loader(options_,figpath,ifig_number,caption,label_name,scale_factor)
if nargin<6
scale_factor=1;
end
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([figpath '.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by map_ident_.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n\n']);
fprintf(fidTeX,'\\begin{figure}[H]\n');
fprintf(fidTeX,'\\centering \n');
fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s}\n',scale_factor,strrep(figpath,'\','/'));
fprintf(fidTeX,'\\caption{%s.}',caption);
fprintf(fidTeX,'\\label{Fig:%s:%u}\n',label_name,ifig_number);
fprintf(fidTeX,'\\end{figure}\n\n');
fprintf(fidTeX,'%% End Of TeX file. \n');
fclose(fidTeX);
end
function yr = trank(y)
% yr is the rank transformation of y
yr=NaN(size(y));
[nr, nc] = size(y);
for j=1:nc
[~, is]=sort(y(:,j));
yr(is,j)=[1:nr]'./nr;
end

60
matlab/+gsa/monte_carlo_moments.m
View File

@ -1,60 +0,0 @@
function [vdec, cc, ac] = monte_carlo_moments(mm, ss, dr, M_, options_, estim_params_)
% [vdec, cc, ac] = monte_carlo_moments(mm, ss, dr, M_, options_,estim_params_)
% Conduct Monte Carlo simulation of second moments for GSA
% Inputs:
% - dr [structure] decision rules
% - M_ [structure] model structure
% - options_ [structure] Matlab's structure describing the current options
% - estim_params_ [structure] characterizing parameters to be estimated
%
% Outputs:
% - vdec [double] variance decomposition matrix
% - cc [double] vector of unique elements of cross correlation matrix
% - ac [cell] autocorrelation matrix
% Copyright © 2012-2023 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/>.
[~, nc1, nsam] = size(mm);
nobs=length(options_.varobs);
disp('monte_carlo_moments: Computing theoretical moments ...')
h = dyn_waitbar(0,'Theoretical moments ...');
vdec = zeros(nobs,M_.exo_nbr,nsam);
cc = zeros(nobs,nobs,nsam);
ac = zeros(nobs,nobs*options_.ar,nsam);
for j=1:nsam
dr.ghx = mm(:, 1:(nc1-M_.exo_nbr),j);
dr.ghu = mm(:, (nc1-M_.exo_nbr+1):end, j);
if ~isempty(ss)
M_=gsa.set_shocks_param(M_,estim_params_,ss(j,:));
end
[vdec(:,:,j), corr, autocorr] = gsa.th_moments(dr,options_,M_);
cc(:,:,j)=triu(corr);
dum=NaN(nobs,nobs*options_.ar);
for i=1:options_.ar
dum(:,(i-1)*nobs+1:i*nobs)=autocorr{i};
end
ac(:,:,j)=dum;
if mod(j,3)==0
dyn_waitbar(j/nsam,h)
end
end
dyn_waitbar_close(h)
skipline()
disp('... done !')

8
matlab/+gui/+perfect_foresight/run.m
View File

@ -142,11 +142,11 @@ mapkeys = unique(cell2mat([keys(unanticipated_p_shocks) keys(unanticipated_t_sho
%% Simulation
options_.periods = jm.periods;
oo_=perfect_foresight_setup(M_, options_, oo_);
perfect_foresight_setup;
% no surprise shocks present
if isempty(mapkeys)
oo_=perfect_foresight_solver(M_, options_, oo_);
perfect_foresight_solver;
return
end
@ -161,7 +161,7 @@ end
if mapkeys(1) ~= 1
% if first unanticipated shock is not in period 1
% simulate until first unanticipated shock and save
oo_=perfect_foresight_solver(M_, options_, oo_);
perfect_foresight_solver;
yy = [yy oo_.endo_simul(:, 2:mapkeys(1)+1)];
end
@ -203,7 +203,7 @@ for i = 1:length(mapkeys)
last_period = this_period;
assert(rows(oo_.exo_simul) == oo_exo_simul_rows, 'error encountered setting oo_.exo_simul');
oo_.endo_simul(:, 1) = yy(:, end);
oo_=perfect_foresight_solver(M_, options_, oo_);
perfect_foresight_solver;
if next_period > 0
yy = [yy oo_.endo_simul(:, 2:next_period-this_period+1)];
else

66
matlab/+mom/Jtest.m
View File

@ -1,28 +1,22 @@
function J_test = Jtest(xparam, objective_function, Q, model_moments, m_data, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% J_test = Jtest(xparam, objective_function, Q, model_moments, m_data, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% -------------------------------------------------------------------------
% Computes the J-test statistic and p-value given a GMM/SMM estimation
function oo_ = Jtest(xparam, objective_function, Woptflag, oo_, options_mom_, bayestopt_, Bounds, estim_params_, M_, nobs)
% function oo_ = Jtest(xparam, objective_function, Woptflag, oo_, options_mom_, bayestopt_, Bounds, estim_params_, M_, nobs)
% -------------------------------------------------------------------------
% Computes the J-test statistic and p-value for a GMM/SMM estimation
% =========================================================================
% INPUTS
% xparam: [vector] estimated parameter vector
% objective_function: [function handle] objective function
% Q: [scalar] value of moments distance criterion
% model_moments: [vector] model moments
% m_data: [matrix] selected empirical moments at each point in time
% data_moments: [vector] empirical moments
% weighting_info: [struct] information on weighting matrix
% options_mom_: [struct] options
% M_: [struct] model information
% estim_params_: [struct] estimated parameters
% bayestopt_: [struct] info on prior distributions
% BoundsInfo: [struct] info bounds on parameters
% dr: [struct] reduced form model
% endo_steady_state: [vector] steady state of endogenous variables (initval)
% exo_steady_state: [vector] steady state of exogenous variables (initval)
% exo_det_steady_state: [vector] steady state of deterministic exogenous variables (initval)
% xparam: [vector] estimated parameter vector
% objective_function: [function handle] objective function
% Woptflag: [logical] flag if optimal weighting matrix has already been computed
% oo_: [struct] results
% options_mom_: [struct] options
% bayestopt_: [struct] information on priors
% Bounds: [struct] bounds on parameters
% estim_params_: [struct] information on estimated parameters
% M_: [struct] information on the model
% nobs: [scalar] number of observations
% -------------------------------------------------------------------------
% OUTPUT
% J_test: [struct] results of J test
% oo_: [struct] updated results
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
@ -30,8 +24,7 @@ function J_test = Jtest(xparam, objective_function, Q, model_moments, m_data, da
% This function calls
% o mom.objective_function
% o mom.optimal_weighting_matrix
% -------------------------------------------------------------------------
% =========================================================================
% Copyright © 2023 Dynare Team
%
% This file is part of Dynare.
@ -48,26 +41,27 @@ function J_test = Jtest(xparam, objective_function, Q, model_moments, m_data, da
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% =========================================================================
if options_mom_.mom.mom_nbr > length(xparam)
% Get optimal weighting matrix for J test, if necessary
if ~weighting_info.Woptflag
W_opt = mom.optimal_weighting_matrix(m_data, model_moments, options_mom_.mom.bartlett_kernel_lag);
weighting_info.Sw = chol(W_opt);
fval = feval(objective_function, xparam, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
if ~Woptflag
W_opt = mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
oo_J = oo_;
oo_J.mom.Sw = chol(W_opt);
fval = feval(objective_function, xparam, Bounds, oo_J, estim_params_, M_, options_mom_);
else
fval = Q;
fval = oo_.mom.Q;
end
% Compute J statistic
if strcmp(options_mom_.mom.mom_method,'SMM')
variance_correction_factor = options_mom_.mom.variance_correction_factor;
Variance_correction_factor = options_mom_.mom.variance_correction_factor;
elseif strcmp(options_mom_.mom.mom_method,'GMM')
variance_correction_factor = 1;
Variance_correction_factor = 1;
end
J_test.j_stat = options_mom_.nobs*variance_correction_factor*fval/options_mom_.mom.weighting_matrix_scaling_factor;
J_test.degrees_freedom = length(model_moments)-length(xparam);
J_test.p_val = 1-chi2cdf(J_test.j_stat, J_test.degrees_freedom);
fprintf('\nValue of J-test statistic: %f\n',J_test.j_stat);
fprintf('p-value of J-test statistic: %f\n',J_test.p_val);
oo_.mom.J_test.j_stat = nobs*Variance_correction_factor*fval/options_mom_.mom.weighting_matrix_scaling_factor;
oo_.mom.J_test.degrees_freedom = length(oo_.mom.model_moments)-length(xparam);
oo_.mom.J_test.p_val = 1-chi2cdf(oo_.mom.J_test.j_stat, oo_.mom.J_test.degrees_freedom);
fprintf('\nValue of J-test statistic: %f\n',oo_.mom.J_test.j_stat);
fprintf('p-value of J-test statistic: %f\n',oo_.mom.J_test.p_val);
end

57
matlab/+mom/check_irf_matching_file.m
View File

@ -1,57 +0,0 @@
function [irf_matching_file_name, irf_matching_file_path] = check_irf_matching_file(irf_matching_file)
% [irf_matching_file_name, irf_matching_file_path] = check_irf_matching_file(irf_matching_file)
% -------------------------------------------------------------------------
% Check if the provided irf_matching_file is a valid MATLAB function with
% .m extension and return name, path and extension of the file.
% -------------------------------------------------------------------------
% INPUTS
% - irf_matching_file: [string] user provided name (with possible path and extension)
% of the MATLAB function that transforms model IRFs
% -------------------------------------------------------------------------
% OUTPUTS
% - irf_matching_file_name: [string] name of the MATLAB function (without extension)
% - irf_matching_file_path: [string] path to irf_matching_file_name
% -------------------------------------------------------------------------
% This function is called by
% - mom.run
% -------------------------------------------------------------------------
% Copyright © 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/>.
if isempty(irf_matching_file)
% no irf_matching_file provided, so no transformations will be done
irf_matching_file_name = '';
irf_matching_file_path = '';
else
[irf_matching_file_path, irf_matching_file_name, irf_matching_file_ext] = fileparts(irf_matching_file);
% make sure file is a MATLAB function with .m extension
if ~strcmp(irf_matching_file_ext,'.m')
if strcmp(irf_matching_file_ext,'')
irf_matching_file_ext = '.m';
else
error('method_of_moments: ''irf_matching_file'' needs to point towards a MATLAB function with extension ''.m''!');
end
end
if isempty(irf_matching_file_path)
irf_matching_file_path = '.';
end
if exist([irf_matching_file_path filesep irf_matching_file_name irf_matching_file_ext],'file') ~= 2
error('method_of_moments: Could not find a ''irf_matching_file'' called ''%s''!',[irf_matching_file_path filesep irf_matching_file_name irf_matching_file_ext]);
end
end

309
matlab/+mom/default_option_mom_values.m
View File

@ -1,33 +1,33 @@
function options_mom_ = default_option_mom_values(options_mom_, options_, dname, do_bayesian_estimation)
% options_mom_ = default_option_mom_values(options_mom_, options_, dname, do_bayesian_estimation)
% -------------------------------------------------------------------------
% Returns structure containing the options for method_of_moments command.
% Note 1: options_mom_ is local and contains default and user-specified
% values for all settings needed for the method of moments estimation.
% Some options, though, are set by the preprocessor into options_ and we
% copy these over. The idea is to be independent of options_ and have full
% control of the estimation instead of possibly having to deal with options
% chosen somewhere else in the mod file.
% Note 2: we call a "mode" the minimum of the objective function, i.e.
% the parameter vector that minimizes the distance between the moments/IRFs
% computed from the model and the moments/IRFs computed from the data.
% -------------------------------------------------------------------------
function options_mom_ = default_option_mom_values(options_mom_, options_, dname, doBayesianEstimation)
% function options_mom_ = default_option_mom_values(options_mom_, options_, dname, doBayesianEstimation)
% Returns structure containing the options for method_of_moments command
% options_mom_ is local and contains default and user-specified values for
% all settings needed for the method of moments estimation. Some options,
% though, are set by the preprocessor into options_ and we copy these over.
% The idea is to be independent of options_ and have full control of the
% estimation instead of possibly having to deal with options chosen somewhere
% else in the mod file.
% =========================================================================
% INPUTS
% o options_mom_: [structure] all user-specified settings (from the method_of_moments command)
% o options_: [structure] global options
% o dname: [string] default name of directory to store results
% o do_bayesian_estimation [boolean] indicator whether we do Bayesian estimation
% o options_mom_: [structure] information about all (user-specified and updated) settings used in estimation (options_mom_)
% o options_: [structure] information on global options
% o dname: [string] name of directory to store results
% o doBayesianEstimation [boolean] indicator whether we do Bayesian estimation
% -------------------------------------------------------------------------
% OUTPUTS
% o options_mom_: [structure] all user-specified and updated settings required for method_of_moments estimation
% o oo_: [structure] storage for results (oo_)
% o options_mom_: [structure] information about all (user-specified and updated) settings used in estimation (options_mom_)
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
% -------------------------------------------------------------------------
% This function calls
% o set_default_option
% o user_has_matlab_license
% o user_has_octave_forge_package
% o set_default_option
% o user_has_matlab_license
% o user_has_octave_forge_package
% -------------------------------------------------------------------------
% Copyright © 2023 Dynare Team
@ -46,43 +46,34 @@ function options_mom_ = default_option_mom_values(options_mom_, options_, dname,
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% =========================================================================
mom_method = options_mom_.mom.mom_method; % this is a required option
% -------------------------------------------------------------------------
% LIMITATIONS
% -------------------------------------------------------------------------
if options_.logged_steady_state || options_.loglinear
error('method_of_moments: The loglinear option is not supported. Please append the required logged variables as auxiliary equations.');
error('method_of_moments: The loglinear option is not supported. Please append the required logged variables as auxiliary equations.')
else
options_mom_.logged_steady_state = 0;
options_mom_.loglinear = false;
end
if isfield(options_mom_,'hessian') && options_mom_.hessian.use_penalized_objective
warning('method_of_moments: The ''use_penalized_objective_for_hessian'' option is not supported yet and will be skipped.');
end
options_mom_.hessian.use_penalized_objective = false; % penalized objective not yet supported
options_mom_.hessian.use_penalized_objective = false; % penalized objective not yet
% options related to variable declarations
if isfield(options_,'trend_coeffs')
error('method_of_moments: %s does not allow for trend in data',mom_method);
error('method_of_moments: %s does not allow for trend in data',mom_method)
end
% options related to endogenous prior restrictions are not supported
if ~isempty(options_.endogenous_prior_restrictions.irf) && ~isempty(options_.endogenous_prior_restrictions.moment)
warning('method_of_moments: Endogenous prior restrictions are not supported yet and will be skipped.');
fprintf('method_of_moments: Endogenous prior restrictions are not supported yet and will be skipped.\n')
end
options_mom_.endogenous_prior_restrictions.irf = {};
options_mom_.endogenous_prior_restrictions.moment = {};
if isfield(options_mom_,'bayesian_irf') && options_mom_.bayesian_irf % do we need this at all??
warning('method_of_moments: The ''bayesian_irf'' option is not supported yet and will be skipped.');
end
options_mom_.bayesian_irf = false;
if strcmp(mom_method,'IRF_MATCHING')
if isfield(options_mom_.mom,'penalized_estimator') && options_mom_.mom.penalized_estimator
warning('method_of_moments: The ''penalized_estimator'' option is not supported yet for IRF_MATCHING and will be ignored.');
end
options_mom_.mom.penalized_estimator = false;
end
options_mom_.mom.analytic_jacobian_optimizers = [1, 3, 4, 13, 101]; % these are currently supported optimizers that are able to use the analytical_jacobian option
% -------------------------------------------------------------------------
% OPTIONS POSSIBLY SET BY THE USER
@ -96,7 +87,8 @@ options_mom_ = set_default_option(options_mom_,'nograph',false); % do no
options_mom_ = set_default_option(options_mom_,'noprint',false); % do not print output to console
options_mom_ = set_default_option(options_mom_,'TeX',false); % print TeX tables and graphics
options_mom_.mom = set_default_option(options_mom_.mom,'verbose',false); % display and store intermediate estimation results
if do_bayesian_estimation
%options_mom_ = set_default_option(options_mom_,'verbosity',false); %
if doBayesianEstimation
options_mom_ = set_default_option(options_mom_,'plot_priors',true); % control plotting of priors
options_mom_ = set_default_option(options_mom_,'prior_trunc',1e-10); % probability of extreme values of the prior density that is ignored when computing bounds for the parameters
end
@ -119,16 +111,6 @@ end
if strcmp(mom_method,'GMM')
options_mom_.mom = set_default_option(options_mom_.mom,'analytic_standard_errors',false); % compute standard errors numerically (0) or analytically (1). Analytical derivatives are only available for GMM.
end
if strcmp(mom_method,'IRF_MATCHING')
if ~isfield(options_mom_.mom,'irf_matching_file')
options_mom_.mom.irf_matching_file = []; % irf_matching file enables to transform model IRFs before matching them to data IRFs
end
options_mom_.mom.irf_matching_file = set_default_option(options_mom_.mom.irf_matching_file,'name','');
options_mom_.mom = set_default_option(options_mom_.mom,'simulation_method','STOCH_SIMUL'); % simulation method used to compute IRFs
options_mom_ = set_default_option(options_mom_,'add_tiny_number_to_cholesky',1e-14); % add tiny number to Cholesky factor to avoid numerical problems when computing IRFs
options_mom_ = set_default_option(options_mom_,'drop',100); % truncation / burnin for order>1 irf simulations
options_mom_ = set_default_option(options_mom_,'relative_irf',false); % requests the computation of normalized IRFs
end
% data related options
if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
@ -141,14 +123,12 @@ if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
end
% optimization related
if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
if (isoctave && user_has_octave_forge_package('optim')) || (~isoctave && user_has_matlab_license('optimization_toolbox'))
if (isoctave && user_has_octave_forge_package('optim')) || (~isoctave && user_has_matlab_license('optimization_toolbox'))
if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
options_mom_ = set_default_option(options_mom_,'mode_compute',13); % specifies lsqnonlin as default optimizer for minimization
else
options_mom_ = set_default_option(options_mom_,'mode_compute',5); % specifies newrat as fallback default option for minimization
end
elseif strcmp(mom_method,'IRF_MATCHING')
options_mom_ = set_default_option(options_mom_,'mode_compute',5); % specifies newrat as fallback default option for minimization
else
options_mom_ = set_default_option(options_mom_,'mode_compute',4); % specifies csminwel as fallback default option for minimization
end
options_mom_ = set_default_option(options_mom_,'additional_optimizer_steps',[]); % vector of additional mode-finders run after mode_compute
options_mom_ = set_default_option(options_mom_,'optim_opt',[]); % a list of NAME and VALUE pairs to set options for the optimization routines. Available options depend on mode_compute
@ -156,20 +136,9 @@ options_mom_ = set_default_option(options_mom_,'silent_optimizer',false);
options_mom_ = set_default_option(options_mom_,'huge_number',1e7); % value for replacing the infinite bounds on parameters by finite numbers. Used by some optimizers for numerical reasons
options_mom_.mom = set_default_option(options_mom_.mom,'analytic_jacobian',false); % use analytic Jacobian in optimization, only available for GMM and gradient-based optimizers
options_mom_.optimizer_vec = [options_mom_.mode_compute;num2cell(options_mom_.additional_optimizer_steps)];
options_mom_.mom.analytic_jacobian_optimizers = [1, 3, 4, 13, 101]; % these are currently supported optimizers that are able to use the analytic_jacobian option
options_mom_.analytic_derivation = 0; % force to 0 as we check this seperately in dynare_minimize_objective.m
options_mom_ = set_default_option(options_mom_,'mode_file',''); % name of the file containing initial values for the mode
options_mom_ = set_default_option(options_mom_,'cova_compute',true); % 1: computed covariance via Hessian after the computation of the mode, 0: turn off computation of covariance matrix
% perturbation related
options_mom_ = set_default_option(options_mom_,'order',1); % order of Taylor approximation in perturbation
if strcmp(mom_method,'IRF_MATCHING') % number of simulated series used to compute IRFs
if options_mom_.order == 1
options_mom_ = set_default_option(options_mom_,'replic',1);
else
options_mom_ = set_default_option(options_mom_,'replic',50);
end
end
options_mom_ = set_default_option(options_mom_,'pruning',false); % use pruned state space system at order>1
options_mom_ = set_default_option(options_mom_,'aim_solver',false); % use AIM algorithm to compute perturbation approximation instead of mjdgges
options_mom_ = set_default_option(options_mom_,'k_order_solver',false); % use k_order_perturbation instead of mjdgges
@ -191,122 +160,15 @@ options_mom_ = set_default_option(options_mom_,'lyapunov_srs',false);
options_mom_ = set_default_option(options_mom_,'lyapunov_complex_threshold',1e-15); % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
options_mom_ = set_default_option(options_mom_,'lyapunov_fixed_point_tol',1e-10); % convergence criterion used in the fixed point Lyapunov solver
options_mom_ = set_default_option(options_mom_,'lyapunov_doubling_tol',1e-16); % convergence criterion used in the doubling algorithm
options_mom_ = set_default_option(options_mom_,'sylvester_fp',false); % determines whether to use fixed point algorihtm to solve Sylvester equation (gensylv_fp), faster for large scale models
options_mom_ = set_default_option(options_mom_,'sylvester_fixed_point_tol',1e-12); % convergence criterion used in the fixed point Sylvester solver
% Bayesian MCMC related
if do_bayesian_estimation
options_mom_ = set_default_option(options_mom_,'mh_replic',0); % number of draws in Metropolis-Hastings and slice samplers
options_mom_ = set_default_option(options_mom_,'mh_posterior_mode_estimation',false); % skip optimizer-based mode-finding and instead compute the mode based on a run of a MCMC
options_mom_ = set_default_option(options_mom_,'load_mh_file',false); % add to previous Metropolis-Hastings or slice simulations instead of starting from scratch
options_mom_ = set_default_option(options_mom_,'load_results_after_load_mh',false); % load the previously computed convergence diagnostics, marginal data density, and posterior statistics from an existing mom_results file instead of recomputing them
if options_mom_.mh_replic > 0 || options_mom_.load_mh_file
options_mom_ = set_default_option(options_mom_,'sub_draws',[]);
options_mom_ = set_default_option(options_mom_,'posterior_max_subsample_draws',1200);
options_mom_ = set_default_option(options_mom_,'mh_nblck',2); % number of parallel chains for Metropolis-Hastings or slice algorithm
options_mom_ = set_default_option(options_mom_,'mh_drop',0.5); % fraction of initially generated parameter vectors to be dropped as a burn-in before using posterior simulations
options_mom_ = set_default_option(options_mom_,'mh_conf_sig',0.9); % confidence/HPD interval used for the computation of prior and posterior statistics
options_mom_ = set_default_option(options_mom_,'mh_recover',false); % attempts to recover a Metropolis-Hastings simulation that crashed prematurely
options_mom_ = set_default_option(options_mom_,'MCMC_jumping_covariance','hessian'); % which covariance to use for the proposal density of the MCMC sampler
if ~isfield(options_mom_,'mh_initialize_from_previous_mcmc')
options_mom_.mh_initialize_from_previous_mcmc.status = false; % pick initial values for new MCMC from a previous one
end
options_mom_.mh_initialize_from_previous_mcmc = set_default_option(options_mom_.mh_initialize_from_previous_mcmc,'directory',''); % pick initial values for new MCMC from a previous one: directory
options_mom_.mh_initialize_from_previous_mcmc = set_default_option(options_mom_.mh_initialize_from_previous_mcmc,'record',''); % pick initial values for new MCMC from a previous one: record file name
options_mom_.mh_initialize_from_previous_mcmc = set_default_option(options_mom_.mh_initialize_from_previous_mcmc,'prior',''); % pick initial values for new MCMC from a previous one: prior file name
if ~isfield(options_mom_,'posterior_sampler_options')
options_mom_.posterior_sampler_options = [];
end
options_mom_.posterior_sampler_options = set_default_option(options_mom_.posterior_sampler_options,'posterior_sampling_method','random_walk_metropolis_hastings'); % selects the sampler used to sample from the posterior distribution during Bayesian estimation
options_mom_.posterior_sampler_options = set_default_option(options_mom_.posterior_sampler_options,'sampling_opt',[]); % used to set options for the posterior sampling methods
switch options_mom_.posterior_sampler_options.posterior_sampling_method
case 'random_walk_metropolis_hastings'
if ~isfield(options_mom_.posterior_sampler_options,'rwmh')
options_mom_.posterior_sampler_options.rwmh = [];
end
options_mom_.posterior_sampler_options.rwmh = set_default_option(options_mom_.posterior_sampler_options.rwmh,'proposal_distribution','rand_multivariate_normal');
options_mom_.posterior_sampler_options.rwmh = set_default_option(options_mom_.posterior_sampler_options.rwmh,'student_degrees_of_freedom',3);
options_mom_.posterior_sampler_options.rwmh = set_default_option(options_mom_.posterior_sampler_options.rwmh,'use_mh_covariance_matrix',false);
options_mom_.posterior_sampler_options.rwmh = set_default_option(options_mom_.posterior_sampler_options.rwmh,'save_tmp_file',false);
case 'tailored_random_block_metropolis_hastings'
if ~isfield(options_mom_.posterior_sampler_options,'tarb')
options_mom_.posterior_sampler_options.tarb = [];
end
options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'proposal_distribution','rand_multivariate_normal');
options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'student_degrees_of_freedom',3);
options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'mode_compute',4);
options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'new_block_probability',0.25);
options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'optim_opt','');
options_mom_.posterior_sampler_options.tarb = set_default_option(options_mom_.posterior_sampler_options.tarb,'save_tmp_file',true);
case 'slice'
if ~isfield(options_mom_.posterior_sampler_options,'slice')
options_mom_.posterior_sampler_options.slice = [];
end
options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'proposal_distribution','');
options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'rotated',0);
options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'slice_initialize_with_mode',false); % must be used with rotated
options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'use_mh_covariance_matrix',false); % must be used with rotated
options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'WR',[]);
options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'mode_files',[]);
options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'mode',[]);
options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'initial_step_size',0.8);
options_mom_.posterior_sampler_options.slice = set_default_option(options_mom_.posterior_sampler_options.slice,'save_tmp_file',true);
case 'independent_metropolis_hastings'
if ~isfield(options_mom_.posterior_sampler_options,'imh')
options_mom_.posterior_sampler_options.imh = [];
end
options_mom_.posterior_sampler_options.imh = set_default_option(options_mom_.posterior_sampler_options.imh,'proposal_distribution','rand_multivariate_normal');
options_mom_.posterior_sampler_options.imh = set_default_option(options_mom_.posterior_sampler_options.imh,'use_mh_covariance_matrix',false);
options_mom_.posterior_sampler_options.imh = set_default_option(options_mom_.posterior_sampler_options.imh,'save_tmp_file',false);
end
if ~strcmp(options_mom_.posterior_sampler_options.posterior_sampling_method,'slice')
options_mom_ = set_default_option(options_mom_,'mh_init_scale_factor',2);
options_mom_ = set_default_option(options_mom_,'mh_jscale',[]);
end
% mh_tune_jscale options
if strcmp(options_mom_.posterior_sampler_options.posterior_sampling_method,'random_walk_metropolis_hastings')
if ~isfield(options_mom_,'mh_tune_jscale')
options_mom_.mh_tune_jscale = [];
end
options_mom_.mh_tune_jscale = set_default_option(options_mom_.mh_tune_jscale,'status',false);
options_mom_.mh_tune_jscale = set_default_option(options_mom_.mh_tune_jscale,'target',0.33);
options_mom_.mh_tune_jscale = set_default_option(options_mom_.mh_tune_jscale,'guess',[]);
options_mom_.mh_tune_jscale.maxiter = options_.mh_tune_jscale.maxiter;
options_mom_.mh_tune_jscale.rho = options_.mh_tune_jscale.rho;
options_mom_.mh_tune_jscale.stepsize = options_.mh_tune_jscale.stepsize;
options_mom_.mh_tune_jscale.c1 = options_.mh_tune_jscale.c1;
options_mom_.mh_tune_jscale.c2 = options_.mh_tune_jscale.c2;
options_mom_.mh_tune_jscale.c3 = options_.mh_tune_jscale.c3;
end
% convergence diagnostics
options_mom_ = set_default_option(options_mom_,'nodiagnostic',false);
if ~isfield(options_mom_,'convergence')
options_mom_.convergence = [];
end
if ~isfield(options_mom_.convergence,'geweke')
options_mom_.convergence.geweke = [];
end
if ~isfield(options_mom_.convergence,'rafterylewis')
options_mom_.convergence.rafterylewis = [];
end
if ~isfield(options_mom_.convergence,'brooksgelman')
options_mom_.convergence.brooksgelman = [];
end
options_mom_.convergence.geweke = set_default_option(options_mom_.convergence.geweke,'taper_steps', [4 8 15]);
options_mom_.convergence.geweke = set_default_option(options_mom_.convergence.geweke,'geweke_interval', [0.2 0.5]);
options_mom_.convergence.rafterylewis = set_default_option(options_mom_.convergence.rafterylewis,'indicator', false);
options_mom_.convergence.rafterylewis = set_default_option(options_mom_.convergence.rafterylewis,'qrs', [0.025 0.005 0.95]);
options_mom_.convergence.brooksgelman = set_default_option(options_mom_.convergence.brooksgelman,'plotrows',3);
end
end
% mode check plot options
% mode check plot
options_mom_.mode_check.nolik = false; % we don't do likelihood (also this initializes mode_check substructure)
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'status',false); % plot the target function for values around the computed mode for each estimated parameter in turn. This is helpful to diagnose problems with the optimizer.
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'neighbourhood_size',.5); % width of the window around the computed mode to be displayed on the diagnostic plots. This width is expressed in percentage deviation. The Inf value is allowed, and will trigger a plot over the entire domain
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'symmetric_plots',true); % ensure that the check plots are symmetric around the mode. A value of 0 allows to have asymmetric plots, which can be useful if the mode is close to a domain boundary, or in conjunction with neighbourhood_size = Inf when the domain is not the entire real line
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'number_of_points',20); % number of points around the mode where the target function is evaluated (for each parameter)
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'status',false); % plot the target function for values around the computed minimum for each estimated parameter in turn. This is helpful to diagnose problems with the optimizer.
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'neighbourhood_size',.5); % width of the window around the computed minimum to be displayed on the diagnostic plots. This width is expressed in percentage deviation. The Inf value is allowed, and will trigger a plot over the entire domain
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'symmetric_plots',true); % ensure that the check plots are symmetric around the minimum. A value of 0 allows to have asymmetric plots, which can be useful if the minimum is close to a domain boundary, or in conjunction with neighbourhood_size = Inf when the domain is not the entire real line
options_mom_.mode_check = set_default_option(options_mom_.mode_check,'number_of_points',20); % number of points around the minimum where the target function is evaluated (for each parameter)
% -------------------------------------------------------------------------
@ -320,12 +182,8 @@ options_mom_.obs_nbr = length(options_mom_.varobs); % number of observed variabl
% related to call of dynare
options_mom_.console_mode = options_.console_mode;
if options_mom_.console_mode
options_mom_.nodisplay = true;
end
options_mom_.parallel = options_.parallel;
options_mom_.parallel_info = options_.parallel_info;
options_mom_.debug = options_.debug; % debug option is needed by some functions, e.g. check_plot
% related to estimated_params and estimated_params_init blocks
options_mom_.use_calibration_initialization = options_.use_calibration_initialization;
@ -394,82 +252,19 @@ end
options_mom_.gstep = options_.gstep; % needed by hessian.m
options_mom_.trust_region_initial_step_bound_factor = options_.trust_region_initial_step_bound_factor; % used in dynare_solve for trust_region
% miscellaneous
options_mom_.threads = options_.threads;
% other
options_mom_.MaxNumberOfBytes = options_.MaxNumberOfBytes;
options_mom_.marginal_data_density = options_.marginal_data_density;
%options_mom_.MaximumNumberOfMegaBytes = options_.MaximumNumberOfMegaBytes;
% -------------------------------------------------------------------------
% DEFAULT VALUES
% -------------------------------------------------------------------------
options_mom_.mom.compute_derivs = false; % flag to compute derivs in objective function (might change for GMM with either analytic_standard_errors or analytic_jacobian (dependent on optimizer))
options_mom_.mom.vector_output = false; % specifies whether the objective function returns a vector
options_mom_.analytic_derivation = 0;
options_mom_.analytic_derivation_mode = 0; % needed by get_perturbation_params_derivs.m, ie use efficient sylvester equation method to compute analytical derivatives as in Ratto & Iskrev (2012)
options_mom_.initialize_estimated_parameters_with_the_prior_mode = 0; % needed by set_prior.m
options_mom_.figures = options_.figures; % needed by plot_priors.m
options_mom_.ramsey_policy = false; % needed by evaluate_steady_state
options_mom_.risky_steadystate = false; % needed by resol
options_mom_.jacobian_flag = true; % needed by dynare_solve
options_mom_.use_mh_covariance_matrix = false; % needed by posterior_sampler, get's overwritten by same option in options_mom_.posterior_sampler_options
% -------------------------------------------------------------------------
% CHECKS ON SETTINGS
% -------------------------------------------------------------------------
if strcmp(mom_method,'GMM') || strcmp(mom_method,'SMM')
if numel(options_mom_.nobs) > 1
error('method_of_moments: Recursive estimation is not supported. Please set an integer as ''nobs''!');
end
if numel(options_mom_.first_obs) > 1
error('method_of_moments: Recursive estimation is not supported. Please set an integer as ''first_obs''!');
end
end
if options_mom_.order < 1
error('method_of_moments: The order of the Taylor approximation cannot be 0!')
end
if options_mom_.order > 2
fprintf('Dynare will use ''k_order_solver'' as the order>2\n');
options_mom_.k_order_solver = true;
end
if strcmp(mom_method,'SMM')
if options_mom_.mom.simulation_multiple < 1
fprintf('The simulation horizon is shorter than the data. Dynare resets the simulation_multiple to 7.\n')
options_mom_.mom.simulation_multiple = 7;
end
end
if strcmp(mom_method,'GMM')
% require pruning with GMM at higher order
if options_mom_.order > 1 && ~options_mom_.pruning
fprintf('GMM at higher order only works with pruning, so we set pruning option to 1.\n');
options_mom_.pruning = true;
end
if options_mom_.order > 3
error('method_of_moments: Perturbation orders higher than 3 are not implemented for GMM estimation, try using SMM!');
end
end
if strcmp(mom_method,'IRF_MATCHING') && do_bayesian_estimation
if isfield(options_mom_,'mh_tune_jscale') && options_mom_.mh_tune_jscale.status && (options_mom_.mh_tune_jscale.maxiter<options_mom_.mh_tune_jscale.stepsize)
warning('method_of_moments: You specified mh_tune_jscale, but the maximum number of iterations is smaller than the step size. No update will take place.')
end
if options_mom_.load_results_after_load_mh
if ~exist([options_mom_.dirname filesep 'method_of_moments' filesep M_.fname '_mom_results.mat'],'file')
fprintf('\nYou specified the ''load_results_after_load_mh'' option, but no ''%s_mom_results.mat'' file\n',M_.fname);
fprintf('was found in the folder %s%smethod_of_moments.\n',options_mom_.dirname,filesep);
fprintf('Results will be recomputed and option ''load_results_after_load_mh'' is reset to false.\n');
options_mom_.load_results_after_load_mh = false;
end
end
if options_mom_.mh_replic>0 && options_mom_.mh_nblck<1
error('method_of_moments: Bayesian MCMC estimation cannot be conducted with ''mh_nblocks''=0!')
end
end
if options_mom_.mom.analytic_jacobian && ~strcmp(mom_method,'GMM')
options_mom_.mom.analytic_jacobian = false;
fprintf('\n''analytic_jacobian'' option will be dismissed as it only works with GMM.\n');
end
if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
if any(cellfun(@(x) isnumeric(x) && any(x == 13), options_mom_.optimizer_vec))
error('method_of_moments: lsqnonlin (mode_compute=13) is not yet supported for IRF matching!');
end
end
options_mom_.figures = options_.figures; % needed by plot_priors.m
options_mom_.ramsey_policy = false; % needed by evaluate_steady_state
options_mom_.risky_steadystate = false; % needed by resol
options_mom_.jacobian_flag = true; % needed by dynare_solve

74
matlab/+mom/display_comparison_moments.m Normal file
View File

@ -0,0 +1,74 @@
function display_comparison_moments(M_, options_mom_, data_moments, model_moments)
% function display_comparison_moments(M_, options_mom_, data_moments, model_moments)
% -------------------------------------------------------------------------
% Displays and saves to disk the comparison of the data moments and the model moments
% =========================================================================
% INPUTS
% M_: [structure] model information
% options_mom_: [structure] method of moments options
% data_moments: [vector] data moments
% model_moments: [vector] model moments
% -------------------------------------------------------------------------
% OUTPUT
% No output, just displays and saves to disk the comparison of the data moments and the model moments
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
% -------------------------------------------------------------------------
% This function calls
% o dyn_latex_table
% o dyntable
% o cellofchararraymaxlength
% =========================================================================
% Copyright © 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/>.
% =========================================================================
titl = ['Comparison of matched data moments and model moments (',options_mom_.mom.mom_method,')'];
headers = {'Moment','Data','Model'};
for jm = 1:size(M_.matched_moments,1)
lables_tmp = 'E[';
lables_tmp_tex = 'E \left[ ';
for jvar = 1:length(M_.matched_moments{jm,1})
lables_tmp = [lables_tmp M_.endo_names{M_.matched_moments{jm,1}(jvar)}];
lables_tmp_tex = [lables_tmp_tex, '{', M_.endo_names_tex{M_.matched_moments{jm,1}(jvar)}, '}'];
if M_.matched_moments{jm,2}(jvar) ~= 0
lables_tmp = [lables_tmp, '(', num2str(M_.matched_moments{jm,2}(jvar)), ')'];
lables_tmp_tex = [lables_tmp_tex, '_{t', num2str(M_.matched_moments{jm,2}(jvar)), '}'];
else
lables_tmp_tex = [lables_tmp_tex, '_{t}'];
end
if M_.matched_moments{jm,3}(jvar) > 1
lables_tmp = [lables_tmp, '^', num2str(M_.matched_moments{jm,3}(jvar))];
lables_tmp_tex = [lables_tmp_tex, '^{', num2str(M_.matched_moments{jm,3}(jvar)) '}'];
end
if jvar == length(M_.matched_moments{jm,1})
lables_tmp = [lables_tmp, ']'];
lables_tmp_tex = [lables_tmp_tex, ' \right]'];
else
lables_tmp = [lables_tmp, '*'];
lables_tmp_tex = [lables_tmp_tex, ' \times '];
end
end
labels{jm,1} = lables_tmp;
labels_TeX{jm,1} = lables_tmp_tex;
end
data_mat = [data_moments model_moments];
dyntable(options_mom_, titl, headers, labels, data_mat, cellofchararraymaxlength(labels)+2, 10, 7);
if options_mom_.TeX
dyn_latex_table(M_, options_mom_, titl, ['comparison_moments_', options_mom_.mom.mom_method], headers, labels_TeX, data_mat, cellofchararraymaxlength(labels)+2, 10, 7);
end

96
matlab/+mom/display_comparison_moments_irfs.m
View File

@ -1,96 +0,0 @@
function display_comparison_moments_irfs(M_, options_mom_, data_moments, model_moments)
% display_comparison_moments_irfs(M_, options_mom_, data_moments, model_moments)
% -------------------------------------------------------------------------
% Displays and saves to disk the comparison of the data moments/IRFs and the model moments/IRFs
% -------------------------------------------------------------------------
% INPUTS
% M_: [structure] model information
% options_mom_: [structure] method of moments options
% data_moments: [vector] data moments
% model_moments: [vector] model moments
% -------------------------------------------------------------------------
% OUTPUT
% No output, just displays and saves to disk the comparison of the data moments and the model moments
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
% -------------------------------------------------------------------------
% This function calls
% o dyn_latex_table
% o dyntable
% o cellofchararraymaxlength
% -------------------------------------------------------------------------
% Copyright © 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/>.
if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
titl = upper('Comparison of matched data IRFs and model IRFs');
headers = {'IRF','Data','Model'};
idx = 1;
for jj = 1:size(M_.matched_irfs,1)
irf_varname = M_.matched_irfs{jj,1};
irf_shockname = M_.matched_irfs{jj,2};
% note that periods can span over multiple rows
IRF_PERIODS = [];
for kk = 1:size(M_.matched_irfs{jj,3},1)
irf_periods = M_.matched_irfs{jj,3}{kk,1};
IRF_PERIODS = [IRF_PERIODS; irf_periods(:)];
end
for hh = 1:length(IRF_PERIODS)
labels{idx,1} = sprintf('%s %s (%u)',irf_varname,irf_shockname,IRF_PERIODS(hh));
labels_TeX{idx,1} = sprintf('%s %s (%u)',M_.endo_names_tex{ismember(M_.endo_names,irf_varname)},M_.exo_names_tex{ismember(M_.exo_names,irf_shockname)},IRF_PERIODS(hh));
idx = idx+1;
end
end
else
titl = ['Comparison of matched data moments and model moments (',options_mom_.mom.mom_method,')'];
headers = {'Moment','Data','Model'};
for jm = 1:size(M_.matched_moments,1)
lables_tmp = 'E[';
lables_tmp_tex = 'E \left[ ';
for jvar = 1:length(M_.matched_moments{jm,1})
lables_tmp = [lables_tmp M_.endo_names{M_.matched_moments{jm,1}(jvar)}];
lables_tmp_tex = [lables_tmp_tex, '{', M_.endo_names_tex{M_.matched_moments{jm,1}(jvar)}, '}'];
if M_.matched_moments{jm,2}(jvar) ~= 0
lables_tmp = [lables_tmp, '(', num2str(M_.matched_moments{jm,2}(jvar)), ')'];
lables_tmp_tex = [lables_tmp_tex, '_{t', num2str(M_.matched_moments{jm,2}(jvar)), '}'];
else
lables_tmp_tex = [lables_tmp_tex, '_{t}'];
end
if M_.matched_moments{jm,3}(jvar) > 1
lables_tmp = [lables_tmp, '^', num2str(M_.matched_moments{jm,3}(jvar))];
lables_tmp_tex = [lables_tmp_tex, '^{', num2str(M_.matched_moments{jm,3}(jvar)) '}'];
end
if jvar == length(M_.matched_moments{jm,1})
lables_tmp = [lables_tmp, ']'];
lables_tmp_tex = [lables_tmp_tex, ' \right]'];
else
lables_tmp = [lables_tmp, '*'];
lables_tmp_tex = [lables_tmp_tex, ' \times '];
end
end
labels{jm,1} = lables_tmp;
labels_TeX{jm,1} = lables_tmp_tex;
end
end
data_mat = [data_moments model_moments];
dyntable(options_mom_, titl, headers, labels, data_mat, cellofchararraymaxlength(labels)+2, 10, 7);
if options_mom_.TeX
dyn_latex_table(M_, options_mom_, titl, ['comparison_moments_', options_mom_.mom.mom_method], headers, labels_TeX, data_mat, cellofchararraymaxlength(labels)+2, 10, 7);
end

32
matlab/+mom/get_data_moments.m
View File

@ -1,8 +1,7 @@
function [data_moments, m_data] = get_data_moments(data, obs_var, inv_order_var, matched_moments_, options_mom_)
% [data_moments, m_data] = get_data_moments(data, obs_var, inv_order_var, matched_moments_, options_mom_)
% -------------------------------------------------------------------------
% Computes the user-selected empirical moments from data
% -------------------------------------------------------------------------
function [dataMoments, m_data] = get_data_moments(data, obs_var, inv_order_var, matched_moments_, options_mom_)
% [dataMoments, m_data] = get_data_moments(data, obs_var, inv_order_var, matched_moments_, options_mom_)
% This function computes the user-selected empirical moments from data
% =========================================================================
% INPUTS
% o data [T x varobs_nbr] data set
% o obs_var: [integer] index of observables
@ -11,14 +10,13 @@ function [data_moments, m_data] = get_data_moments(data, obs_var, inv_order_var,
% o options_mom_: [structure] information about all settings (specified by the user, preprocessor, and taken from global options_)
% -------------------------------------------------------------------------
% OUTPUTS
% o data_moments [numMom x 1] mean of selected empirical moments
% o dataMoments [numMom x 1] mean of selected empirical moments
% o m_data [T x numMom] selected empirical moments at each point in time
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
% o mom.objective_function
% -------------------------------------------------------------------------
% =========================================================================
% Copyright © 2020-2023 Dynare Team
%
% This file is part of Dynare.
@ -35,11 +33,15 @@ function [data_moments, m_data] = get_data_moments(data, obs_var, inv_order_var,
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% -------------------------------------------------------------------------
% Author(s):
% o Willi Mutschler (willi@mutschler.eu)
% o Johannes Pfeifer (johannes.pfeifer@unibw.de)
% =========================================================================
% Initialization
T = size(data,1); % Number of observations (T)
data_moments = NaN(options_mom_.mom.mom_nbr,1);
dataMoments = NaN(options_mom_.mom.mom_nbr,1);
m_data = NaN(T,options_mom_.mom.mom_nbr);
% Product moment for each time period, i.e. each row t contains y_t1(l1)^p1*y_t2(l2)^p2*...
% note that here we already are able to treat leads and lags and any power product moments
@ -58,11 +60,11 @@ for jm = 1:options_mom_.mom.mom_nbr
end
end
% We replace NaN (due to leads and lags and missing values) with the corresponding mean
if isoctave && octave_ver_less_than('8')
data_moments(jm,1) = nanmean(m_data_tmp);
if isoctave || matlab_ver_less_than('8.5')
dataMoments(jm,1) = nanmean(m_data_tmp);
else
data_moments(jm,1) = mean(m_data_tmp,'omitnan');
dataMoments(jm,1) = mean(m_data_tmp,'omitnan');
end
m_data_tmp(isnan(m_data_tmp)) = data_moments(jm,1);
m_data_tmp(isnan(m_data_tmp)) = dataMoments(jm,1);
m_data(:,jm) = m_data_tmp;
end
end

130
matlab/+mom/graph_comparison_irfs.m
View File

@ -1,130 +0,0 @@
function graph_comparison_irfs(matched_irfs,irf_model_varobs,varobs_id,irf_horizon,relative_irf,endo_names,endo_names_tex,exo_names,exo_names_tex,dname,fname,graph_format,TeX,nodisplay,figures_textwidth)
% graph_comparison_irfs(matched_irfs,irf_model_varobs,varobs_id,irf_horizon,relative_irf,endo_names,endo_names_tex,exo_names,exo_names_tex,dname,fname,graph_format,TeX,nodisplay,figures_textwidth)
% -------------------------------------------------------------------------
% Plots and saves to disk the comparison of the selected data IRFs and corresponding model IRfs
% -------------------------------------------------------------------------
% INPUTS
% matched_irfs: [matrix] information on matched data IRFs
% irf_model_varobs: [matrix] model IRFs for observable variables
% varobs_id: [vector] index for observable variables in endo_names
% irf_horizon: [scalar] maximum horizon of IRFs
% relative_irf: [boolean] if true, plots normalized IRFs
% endo_names: [cell] names of endogenous variables
% endo_names_tex: [cell] names of endogenous variables in latex
% exo_names: [cell] names of exogenous variables
% exo_names_tex: [cell] names of exogenous variables in latex
% dname: [string] name of the directory where to save the graphs
% fname: [string] name of the mod file
% graph_format: [cell] format of the graphs
% TeX: [boolean] if true, uses latex for plots
% nodisplay: [boolean] if true, does not display the graphs
% figures_textwidth: [scalar] textwidth used in plots
% -------------------------------------------------------------------------
% OUTPUT
% No output, just displays and saves to disk the graphs
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
% -------------------------------------------------------------------------
% This function calls
% o dyn_figure
% o dyn_saveas
% o remove_fractional_xticks
% o CheckPath
% o pltorg
% -------------------------------------------------------------------------
% Copyright © 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/>.
graph_directory_name = CheckPath('graphs',dname);
latex_directory_name = CheckPath('latex',dname);
if TeX && any(strcmp('eps',cellstr(graph_format)))
fid_TeX = fopen([latex_directory_name '/' fname '_irf_matching_plot.tex'],'w');
fprintf(fid_TeX,'%% TeX eps-loader file generated by mom.run.m (Dynare).\n');
fprintf(fid_TeX,['%% ' datestr(now,0) '\n']);
fprintf(fid_TeX,' \n');
end
unique_shock_entries = unique(matched_irfs(:, 2));
colDarkGrey = [0.3, 0.3, 0.3]; % dark grey
for jexo = unique_shock_entries' % loop over cell with shock names
unique_variables = unique(matched_irfs(ismember(matched_irfs(:, 2),jexo), 1));
[nbplt,nr,nc,lr,lc,nstar] = pltorg(length(unique_variables));
fig = 0;
for jvar = 1:length(unique_variables)
% get data points, note that periods and values can span over multiple rows
jj = ismember(matched_irfs(:,1), unique_variables(jvar)) & ismember(matched_irfs(:,2), jexo);
IRF_PERIODS = []; IRF_VALUES = [];
for kk = 1:size(matched_irfs{jj,3},1)
irf_periods = matched_irfs{jj,3}{kk,1};
irf_values = matched_irfs{jj,3}{kk,2};
if length(irf_values)==1
irf_values = repmat(irf_values,length(irf_periods),1);
end
IRF_PERIODS = [IRF_PERIODS; irf_periods(:)];
IRF_VALUES = [IRF_VALUES; irf_values(:)];
end
if jvar==1 || ~( (fig-1)*nstar<jvar && jvar<=fig*nstar )
fig = fig+1;
fig_irf = dyn_figure(nodisplay,'Name',['IRF matching shock to ' jexo{:} ' figure ' int2str(fig)]);
end
plt = jvar-(fig-1)*nstar;
if nbplt>1 && fig==nbplt
subplot(lr,lc,plt);
else
subplot(nr,nc,plt);
end
plt_data = plot(IRF_PERIODS,IRF_VALUES,'h', 'MarkerEdgeColor',colDarkGrey,'MarkerFaceColor',colDarkGrey,'MarkerSize',8);
hold on
plt_model = plot(1:irf_horizon, irf_model_varobs(:,varobs_id==find(ismember(endo_names,unique_variables(jvar))) , ismember(exo_names,jexo)),'-k','linewidth',2);
hold on
plot([1 irf_horizon],[0 0],'-r','linewidth',1);
hold off
xlim([1 irf_horizon]);
remove_fractional_xticks
if TeX
title(['$' endo_names_tex{ismember(endo_names,unique_variables(jvar))} '$'],'Interpreter','latex');
else
title(unique_variables{jvar},'Interpreter','none');
end
set(gca,'FontSize',12);
if (plt==nstar) || jvar==length(unique_variables)
% Adding a legend at the bottom
axes('Position',[0, 0, 1, 1],'Visible','off');
lgd = legend([plt_data,plt_model],{'Data', 'Model'}, 'Location', 'southeast','NumColumns',2,'FontSize',14);
if ~isoctave
lgd.Position = [0.37 0.01 lgd.Position(3) lgd.Position(4)];
end
dyn_saveas(fig_irf,[graph_directory_name filesep fname '_matched_irf_' jexo{:} int2str(fig)],nodisplay,graph_format);
if TeX && any(strcmp('eps',cellstr(graph_format)))
fprintf(fid_TeX,'\\begin{figure}[H]\n');
fprintf(fid_TeX,'\\centering \n');
fprintf(fid_TeX,'\\includegraphics[width=%2.2f\\textwidth]{%s_matched_irf_%s%s}\n',figures_textwidth*min(plt/nc,1),[graph_directory_name '/' fname],jexo{:},int2str(fig));
if relative_irf
fprintf(fid_TeX,'\\caption{Relative impulse response functions (orthogonalized shock to $%s$).}', jexo{:});
else
fprintf(fid_TeX,'\\caption{Impulse response functions (orthogonalized shock to $%s$).}', jexo{:});
end
fprintf(fid_TeX,'\\label{Fig:MatchedIRF:%s:%s}\n', jexo{:},int2str(fig));
fprintf(fid_TeX,'\\end{figure}\n');
fprintf(fid_TeX,' \n');
end
end
end
end

151
matlab/+mom/matched_irfs_blocks.m
View File

@ -1,151 +0,0 @@
function [data_irfs, weight_mat, irf_index, max_irf_horizon] = matched_irfs_blocks(matched_irfs, matched_irfs_weight, varobs_id, obs_nbr, exo_nbr, endo_names, exo_names)
% [data_irfs, weight_mat, irf_index, max_irf_horizon] = matched_irfs_blocks(matched_irfs, matched_irfs_weight, varobs_id, obs_nbr, exo_nbr, endo_names, exo_names)
% -------------------------------------------------------------------------
% Checks and transforms matched_irfs and matched_irfs_weight blocks
% for further use in the estimation.
% -------------------------------------------------------------------------
% INPUTS
% matched_irfs: [cell array] original matched_irfs block
% matched_irfs_weight: [cell array] original matched_irfs_weight block
% varobs_id: [vector] index for observable variables in endo_names
% obs_nbr: [scalar] number of observable variables
% exo_nbr: [scalar] number of exogenous variables
% endo_names: [cell array] names of endogenous variables
% exo_names: [cell array] names of exogenous variables
% -------------------------------------------------------------------------
% OUTPUT
% data_irfs: [matrix] IRFs for VAROBS as declared in matched_irfs block
% weight_mat: [matrix] weighting matrix for IRFs as declared in matched_irfs_weight block
% irf_index: [vector] index for selecting specific IRFs from full IRF matrix of observables
% max_irf_horizon: [scalar] maximum IRF horizon as declared in matched_irfs block
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
% -------------------------------------------------------------------------
% Copyright © 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/>.
% note matched_irfs block:
% - each row in the cell contains a unique combination of var and varexo,
% however the third column in each row is a nested cell with information
% on periods, values and weights
% - periods, values and weights can span several rows with different lengths of entries
% - in some cases we need to duplicate values and/or weights
% - at the end we want to have everything vectorized and the same length
% get maximum IRF horizons
max_irf_horizon = [];
for jj = 1:size(matched_irfs,1)
max_irf_horizon = [max_irf_horizon; cell2mat(cellfun(@(c) c(:), matched_irfs{jj,3}(:,1), 'UniformOutput', false))];
end
max_irf_horizon = max(max_irf_horizon);
% create full matrix where 1st dimension are IRF periods, 2nd dimension are variables as declared in VAROBS, 3rd dimension are shocks
% idea: overwrite NaN values if they are declared in matched_irfs block; at the end the remaining NaN values will be removed
data_irfs = NaN(max_irf_horizon,obs_nbr,exo_nbr);
% create full empirical weighting matrix, identity matrix by default, i.e. all IRFs are equally important
% idea: first specify full matrix and then reduce it using only entries that are declared in matched_irfs block
weight_mat = speye(max_irf_horizon*obs_nbr*exo_nbr);
for jj = 1:size(matched_irfs,1)
id_var = find(ismember(endo_names,matched_irfs{jj,1}));
id_varobs = find(varobs_id==id_var,1);
id_shock = find(ismember(exo_names,matched_irfs{jj,2}));
if isempty(id_varobs)
skipline;
error('method_of_moments: You specified an IRF matching involving variable %s, but it is not declared as a varobs!',endo_names{id_var})
end
IRF_PERIODS = []; IRF_VALUES = []; IRF_WEIGHTS = [];
for kk = 1:size(matched_irfs{jj,3},1)
irf_periods = matched_irfs{jj,3}{kk,1};
if length(unique(irf_periods)) < length(irf_periods) % row-specific check for unique periods
error('method_of_moments: You specified an IRF matching involving variable %s and shock %s, but there were duplicate ''periods'' in the specification!',endo_names{id_var},exo_names{id_shock});
end
irf_values = matched_irfs{jj,3}{kk,2};
if length(irf_values)==1
irf_values = repmat(irf_values,length(irf_periods),1);
end
if length(irf_periods) ~= length(irf_values) % row-specific check for enough values
error('method_of_moments: You specified an IRF matching involving variable %s and shock %s, but the length of ''periods'' does not match the length of ''values''!',endo_names{id_var},exo_names{id_shock});
end
irf_weights = matched_irfs{jj,3}{kk,3};
if length(irf_weights)==1
irf_weights = repmat(irf_weights,length(irf_periods),1);
end
if length(irf_periods) ~= length(irf_weights) % row-specific check for enough weights
error('method_of_moments: You specified an IRF matching involving variable %s and shock %s, but the length of ''periods'' does not match the length of ''weights''!',endo_names{id_var},exo_names{id_shock});
end
IRF_PERIODS = [IRF_PERIODS; irf_periods(:)];
IRF_VALUES = [IRF_VALUES; irf_values(:)];
IRF_WEIGHTS = [IRF_WEIGHTS; irf_weights(:)];
end
if length(unique(irf_periods)) < length(irf_periods) % overall check for unique periods
error('method_of_moments: You specified an IRF matching involving variable %s and shock %s, but there were duplicate ''periods'' in the specification!',endo_names{id_var},exo_names{id_shock});
end
for hh = 1:length(IRF_PERIODS)
data_irfs(IRF_PERIODS(hh),id_varobs,id_shock) = IRF_VALUES(hh);
if IRF_WEIGHTS(hh) ~= 1
idweight_mat = sub2ind(size(data_irfs),IRF_PERIODS(hh),id_varobs,id_shock);
weight_mat(idweight_mat,idweight_mat) = IRF_WEIGHTS(hh);
end
end
end
% fine-tune weighting matrix using matched_irfs_weights
for jj = 1:size(matched_irfs_weight,1)
id_var1 = find(ismember(endo_names,matched_irfs_weight{jj,1}));
id_var2 = find(ismember(endo_names,matched_irfs_weight{jj,4}));
id_varobs1 = find(varobs_id==id_var1,1);
id_varobs2 = find(varobs_id==id_var2,1);
if isempty(id_varobs1)
skipline;
error('method_of_moments: You specified a weight for an IRF matching involving variable %s, but it is not a varobs!',endo_names{id_var1})
end
if isempty(id_varobs2)
skipline;
error('method_of_moments: You specified a weight for an IRF matching involving variable %s, but it is not a varobs!',endo_names{id_var2})
end
id_shock1 = find(ismember(exo_names,matched_irfs_weight{jj,3}));
id_shock2 = find(ismember(exo_names,matched_irfs_weight{jj,6}));
irf_periods1 = matched_irfs_weight{jj,2};
irf_periods2 = matched_irfs_weight{jj,5};
if length(irf_periods1) ~= length(irf_periods2)
error('method_of_moments: You specified a ''matched_irfs_weights'' entry for an IRF matching involving %s/%s and %s/%s,\n but the horizons do not have the same length!',endo_names{id_var1},exo_names{id_shock1},endo_names{id_var2},exo_names{id_shock2});
end
if max([irf_periods1(:);irf_periods2(:)]) > max_irf_horizon
error('method_of_moments: You specified a ''matched_irfs_weights'' entry for an IRF matching involving %s/%s and %s/%s,\n but the horizon is larger than the maximum one declared in the ''matched_irfs'' block!',endo_names{id_var1},exo_names{id_shock1},endo_names{id_var2},exo_names{id_shock2});
end
weight_mat_values = matched_irfs_weight{jj,7};
if length(weight_mat_values)==1 && length(irf_periods1)>1
weight_mat_values = repmat(weight_mat_values,length(irf_periods1),1);
end
if length(weight_mat_values) ~= length(irf_periods1)
error('method_of_moments: You specified a ''matched_irfs_weights'' entry for an IRF matching involving %s/%s and %s/%s,\n but the horizons do not match the length of ''weights''!',endo_names{id_var1},exo_names{id_shock1},endo_names{id_var2},exo_names{id_shock2});
end
for hh = 1:length(irf_periods1)
idweight_mat1 = sub2ind(size(data_irfs),irf_periods1(hh),id_varobs1,id_shock1);
idweight_mat2 = sub2ind(size(data_irfs),irf_periods2(hh),id_varobs2,id_shock2);
weight_mat(idweight_mat1,idweight_mat2) = weight_mat_values(hh);
weight_mat(idweight_mat2,idweight_mat1) = weight_mat_values(hh); % symmetry
end
end
% remove non-specified IRFs
irf_index = find(~isnan(data_irfs));
data_irfs = data_irfs(irf_index);
weight_mat = weight_mat(irf_index,irf_index);

25
matlab/+mom/matched_moments_block.m
View File

@ -1,8 +1,8 @@
function matched_moments = matched_moments_block(matched_moments, mom_method)
% matched_moments = matched_moments_block(matched_moments, mom_method)
% -------------------------------------------------------------------------
% Checks and transforms matched_moments block for further use in the estimation
% function matched_moments = matched_moments_block(matched_moments, mom_method)
% -------------------------------------------------------------------------
% Checks and transforms matched_moments bock for further use in the estimation
% =========================================================================
% INPUTS
% matched_moments: [cell array] original matched_moments block
% mom_method: [string] method of moments method (GMM or SMM)
@ -12,8 +12,7 @@ function matched_moments = matched_moments_block(matched_moments, mom_method)
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
% -------------------------------------------------------------------------
% =========================================================================
% Copyright © 2023 Dynare Team
%
% This file is part of Dynare.
@ -30,7 +29,7 @@ function matched_moments = matched_moments_block(matched_moments, mom_method)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% =========================================================================
matched_moments_orig = matched_moments;
% higher-order product moments not supported yet for GMM
@ -60,22 +59,22 @@ for jm = 1:size(matched_moments,1)
end
% find duplicate rows in cell array by making groups according to powers as we can then use cell2mat for the unique function
powers = cellfun(@sum,matched_moments(:,3))';
unique_mom_idx = [];
UniqueMomIdx = [];
for jpow = unique(powers)
idx1 = find(powers==jpow);
[~,idx2] = unique(cell2mat(matched_moments(idx1,:)),'rows');
unique_mom_idx = [unique_mom_idx idx1(idx2)];
UniqueMomIdx = [UniqueMomIdx idx1(idx2)];
end
% remove duplicate elements
duplicate_moms = setdiff(1:size(matched_moments_orig,1),unique_mom_idx);
if ~isempty(duplicate_moms)
fprintf('Duplicate declared moments found and removed in ''matched_moments'' block in rows:\n %s.\n',num2str(duplicate_moms));
DuplicateMoms = setdiff(1:size(matched_moments_orig,1),UniqueMomIdx);
if ~isempty(DuplicateMoms)
fprintf('Duplicate declared moments found and removed in ''matched_moments'' block in rows:\n %s.\n',num2str(DuplicateMoms))
fprintf('Dynare will continue with remaining moment conditions\n');
end
if strcmp(mom_method, 'SMM')
% for SMM: keep the original structure, but get rid of duplicate moments
matched_moments = matched_moments_orig(sort(unique_mom_idx),:);
matched_moments = matched_moments_orig(sort(UniqueMomIdx),:);
elseif strcmp(mom_method, 'GMM')
% for GMM we use the transformed matched_moments structure
matched_moments = matched_moments(sort(unique_mom_idx),:);
matched_moments = matched_moments(sort(UniqueMomIdx),:);
end

84
matlab/+mom/mode_compute_gmm_smm.m
View File

@ -1,30 +1,25 @@
function [xparam1, weighting_info, mom_verbose] = mode_compute_gmm_smm(xparam0, objective_function, m_data, data_moments, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% [xparam1, weighting_info, mom_verbose] = mode_compute_gmm_smm(xparam0, objective_function, m_data, data_moments, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
function [xparam1, oo_, Woptflag] = mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, Bounds)
% function [xparam1, oo_, Woptflag] = mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, Bounds)
% -------------------------------------------------------------------------
% Iterated method of moments for GMM and SMM, computes the minimum of the
% objective function (distance between data moments and model moments)
% for a sequence of optimizers and GMM/SMM iterations with different
% weighting matrices.
% -------------------------------------------------------------------------
% =========================================================================
% INPUTS
% xparam0: [vector] vector of initialized parameters
% objective_function: [func handle] name of the objective function
% m_data: [matrix] selected data moments at each point in time
% data_moments: [vector] vector of data moments
% options_mom_: [structure] options
% M_: [structure] model information
% estim_params_: [structure] information on estimated parameters
% bayestopt_: [structure] information on priors
% BoundsInfo: [structure] bounds for optimization
% dr: [structure] reduced form model
% endo_steady_state: [vector] steady state for endogenous variables (initval)
% exo_steady_state: [vector] steady state for exogenous variables (initval)
% exo_det_steady_state: [vector] steady state for exogenous deterministic variables (initval)
% xparam0: [vector] vector of initialized parameters
% objective_function: [func handle] name of the objective function
% oo_: [structure] results
% M_: [structure] model information
% options_mom_: [structure] options
% estim_params_: [structure] information on estimated parameters
% bayestopt_: [structure] information on priors
% Bounds: [structure] bounds for optimization
% -------------------------------------------------------------------------
% OUTPUT
% xparam1: [vector] mode of objective function
% weighting_info: [structure] information on weighting matrix
% mom_verbose: [structure] information on intermediate estimation results
% xparam1: [vector] mode of objective function
% oo_: [structure] updated results
% Woptflag: [logical] true if optimal weighting matrix was computed
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
@ -34,9 +29,7 @@ function [xparam1, weighting_info, mom_verbose] = mode_compute_gmm_smm(xparam0,
% o mom.display_estimation_results_table
% o dynare_minimize_objective
% o mom.objective_function
% o prior_dist_names
% -------------------------------------------------------------------------
% =========================================================================
% Copyright © 2023 Dynare Team
%
% This file is part of Dynare.
@ -53,16 +46,15 @@ function [xparam1, weighting_info, mom_verbose] = mode_compute_gmm_smm(xparam0,
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% =========================================================================
mom_verbose = [];
if size(options_mom_.mom.weighting_matrix,1)>1 && ~(any(strcmpi('diagonal',options_mom_.mom.weighting_matrix)) || any(strcmpi('optimal',options_mom_.mom.weighting_matrix)))
fprintf('\nYou did not specify the use of an optimal or diagonal weighting matrix. There is no point in running an iterated method of moments.\n');
fprintf('\nYou did not specify the use of an optimal or diagonal weighting matrix. There is no point in running an iterated method of moments.\n')
end
for stage_iter = 1:size(options_mom_.mom.weighting_matrix,1)
fprintf('Estimation stage %u\n',stage_iter);
weighting_info.Woptflag = false;
Woptflag = false;
switch lower(options_mom_.mom.weighting_matrix{stage_iter})
case 'identity_matrix'
fprintf(' - identity weighting matrix\n');
@ -71,44 +63,44 @@ for stage_iter = 1:size(options_mom_.mom.weighting_matrix,1)
fprintf(' - diagonal of optimal weighting matrix (Bartlett kernel with %d lags)\n', options_mom_.mom.bartlett_kernel_lag);
if stage_iter == 1
fprintf(' and using data-moments as initial estimate of model-moments\n');
weighting_matrix = diag(diag( mom.optimal_weighting_matrix(m_data, data_moments, options_mom_.mom.bartlett_kernel_lag) ));
weighting_matrix = diag(diag( mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.data_moments, options_mom_.mom.bartlett_kernel_lag) ));
else
fprintf(' and using previous stage estimate of model-moments\n');
weighting_matrix = diag(diag( mom.optimal_weighting_matrix(m_data, model_moments, options_mom_.mom.bartlett_kernel_lag) ));
weighting_matrix = diag(diag( mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag) ));
end
case 'optimal'
fprintf(' - optimal weighting matrix (Bartlett kernel with %d lags)\n', options_mom_.mom.bartlett_kernel_lag);
if stage_iter == 1
fprintf(' and using data-moments as initial estimate of model-moments\n');
weighting_matrix = mom.optimal_weighting_matrix(m_data, data_moments, options_mom_.mom.bartlett_kernel_lag);
weighting_matrix = mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.data_moments, options_mom_.mom.bartlett_kernel_lag);
else
fprintf(' and using previous stage estimate of model-moments\n');
weighting_matrix = mom.optimal_weighting_matrix(m_data, model_moments, options_mom_.mom.bartlett_kernel_lag);
weighting_info.Woptflag = true;
weighting_matrix = mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
Woptflag = true;
end
otherwise % user specified matrix in file
fprintf(' - user-specified weighting matrix\n');
try
load(options_mom_.mom.weighting_matrix{stage_iter},'weighting_matrix')
catch
error(['method_of_moments: No matrix named ''weighting_matrix'' could be found in ',options_mom_.mom.weighting_matrix{stage_iter},'.mat !']);
error(['method_of_moments: No matrix named ''weighting_matrix'' could be found in ',options_mom_.mom.weighting_matrix{stage_iter},'.mat !'])
end
[nrow, ncol] = size(weighting_matrix);
if ~isequal(nrow,ncol) || ~isequal(nrow,length(data_moments)) %check if square and right size
error(['method_of_moments: ''weighting_matrix'' must be square and have ',num2str(length(data_moments)),' rows and columns!']);
if ~isequal(nrow,ncol) || ~isequal(nrow,length(oo_.mom.data_moments)) %check if square and right size
error(['method_of_moments: ''weighting_matrix'' must be square and have ',num2str(length(oo_.mom.data_moments)),' rows and columns!'])
end
end
try % check for positive definiteness of weighting_matrix
weighting_info.Sw = chol(weighting_matrix);
oo_.mom.Sw = chol(weighting_matrix);
catch
error('method_of_moments: Specified ''weighting_matrix'' is not positive definite. Check whether your model implies stochastic singularity!');
error('method_of_moments: Specified ''weighting_matrix'' is not positive definite. Check whether your model implies stochastic singularity!')
end
for optim_iter = 1:length(options_mom_.optimizer_vec)
options_mom_.current_optimizer = options_mom_.optimizer_vec{optim_iter};
if options_mom_.optimizer_vec{optim_iter} == 0
xparam1 = xparam0; % no minimization, evaluate objective at current values
fval = feval(objective_function, xparam1, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
xparam1 = xparam0; % no minimization, evaluate objective at current values
fval = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_);
else
if options_mom_.optimizer_vec{optim_iter} == 13
options_mom_.mom.vector_output = true;
@ -120,22 +112,18 @@ for stage_iter = 1:size(options_mom_.mom.weighting_matrix,1)
else
options_mom_.mom.compute_derivs = false;
end
[xparam1, fval] = dynare_minimize_objective(objective_function, xparam0, options_mom_.optimizer_vec{optim_iter}, options_mom_, [BoundsInfo.lb BoundsInfo.ub], bayestopt_.name, bayestopt_, [],...
data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
[xparam1, fval, exitflag] = dynare_minimize_objective(objective_function, xparam0, options_mom_.optimizer_vec{optim_iter}, options_mom_, [Bounds.lb Bounds.ub], bayestopt_.name, bayestopt_, [],...
Bounds, oo_, estim_params_, M_, options_mom_);
if options_mom_.mom.vector_output
fval = fval'*fval;
end
end
fprintf('\nStage %d Iteration %d: Value of minimized moment distance objective function: %12.10f.\n',stage_iter,optim_iter,fval);
fprintf('\nStage %d Iteration %d: Value of minimized moment distance objective function: %12.10f.\n',stage_iter,optim_iter,fval)
if options_mom_.mom.verbose
fprintf('\n''verbose'' option: ');
std_via_invhessian_xparam1_iter = NaN(size(xparam1));
tbl_title_iter = sprintf('FREQUENTIST %s (STAGE %d ITERATION %d) VERBOSE',options_mom_.mom.mom_method,stage_iter,optim_iter);
field_name_iter = sprintf('%s_stage_%d_iter_%d',lower(options_mom_.mom.mom_method),stage_iter,optim_iter);
mom_verbose.(field_name_iter) = display_estimation_results_table(xparam1,std_via_invhessian_xparam1_iter,M_,options_mom_,estim_params_,bayestopt_,[],prior_dist_names,tbl_title_iter,field_name_iter);
oo_.mom = display_estimation_results_table(xparam1,NaN(size(xparam1)),M_,options_mom_,estim_params_,bayestopt_,oo_.mom,prior_dist_names,sprintf('%s (STAGE %d ITERATION %d) VERBOSE',options_mom_.mom.mom_method,stage_iter,optim_iter),sprintf('verbose_%s_stage_%d_iter_%d',lower(options_mom_.mom.mom_method),stage_iter,optim_iter));
end
xparam0 = xparam1;
end
options_mom_.vector_output = false;
[~, ~, ~, ~, ~, ~, model_moments] = feval(objective_function, xparam1, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state); % get model_moments for iterated GMM/SMM to compute optimal weighting matrix
end
[~, ~, ~,~,~, oo_] = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_); % get oo_.mom.model_moments for iterated GMM/SMM to compute optimal weighting matrix
end

122
matlab/+mom/mode_compute_irf_matching.m
View File

@ -1,122 +0,0 @@
function [xparam1, hessian_xparam1, fval, mom_verbose] = mode_compute_irf_matching(xparam0, hessian_xparam0, objective_function, doBayesianEstimation, weighting_info, data_moments, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% [xparam1, hessian_xparam1, fval, mom_verbose] = mode_compute_irf_matching(xparam0, hessian_xparam0, objective_function, doBayesianEstimation, weighting_info, data_moments, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% -------------------------------------------------------------------------
% Computes the minimum of the objective function (distance between data IRFs
% and model IRFs) for a sequence of optimizers.
% Note that we call a "mode" the minimum of the objective function, i.e.
% the parameter vector that minimizes the distance between the IRFs
% computed from the model and the IRFs computed from the data.
% -------------------------------------------------------------------------
% INPUTS
% xparam0: [vector] initialized parameters
% hessian_xparam0: [matrix] initialized hessian at xparam0
% objective_function: [func handle] name of the objective function
% doBayesianEstimation: [logical] true if Bayesian estimation
% weighting_info: [structure] information on weighting matrix
% data_moments: [vector] data moments
% options_mom_: [structure] options
% M_: [structure] model information
% estim_params_: [structure] information on estimated parameters
% bayestopt_: [structure] information on priors
% BoundsInfo: [structure] bounds for optimization
% dr: [structure] information reduced-form model
% endo_steady_state: [vector] steady state of endogenous variables (initval)
% exo_steady_state: [vector] steady state of exogenous variables (initval)
% exo_det_steady_state: [vector] steady state of deterministic exogenous variables (initval)
% -------------------------------------------------------------------------
% OUTPUT
% xparam1: [vector] mode of objective function
% hessian_xparam1: [matrix] hessian at xparam1
% fval: [double] function value at mode
% mom_verbose: [structure] information on intermediate estimation results
% Also saves the computed mode and hessian to a file.
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
% -------------------------------------------------------------------------
% This function calls
% o display_estimation_results_table
% o dynare_minimize_objective
% o hessian
% o mom.objective_function
% -------------------------------------------------------------------------
% Copyright © 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/>.
mom_verbose = [];
for optim_iter = 1:length(options_mom_.optimizer_vec)
options_mom_.current_optimizer = options_mom_.optimizer_vec{optim_iter};
if options_mom_.optimizer_vec{optim_iter}==0
% no minimization, evaluate objective at current values
xparam1 = xparam0;
hessian_xparam1 = hessian_xparam0;
fval = feval(objective_function, xparam1, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
else
[xparam1, fval, exitflag, hessian_xparam1, options_mom_, Scale, new_rat_hess_info] = dynare_minimize_objective(objective_function, xparam0, options_mom_.optimizer_vec{optim_iter}, options_mom_, [BoundsInfo.lb BoundsInfo.ub], bayestopt_.name, bayestopt_, hessian_xparam0,...
data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
end
fprintf('\nMode Compute Iteration %d: Value of minimized moment distance objective function: %12.10f.\n',optim_iter,fval);
if options_mom_.mom.verbose
fprintf('\n''verbose'' option: ');
if options_mom_.cova_compute
if options_mom_.optimizer_vec{optim_iter}==0
hessian_xparam1_iter = hessian_xparam1;
else
fprintf('computing Hessian');
hessian_xparam1_iter = hessian(objective_function, xparam1, options_mom_.gstep,...
data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
hessian_xparam1_iter = reshape(hessian_xparam1_iter, length(xparam1), length(xparam1));
end
hsd_iter = sqrt(diag(hessian_xparam1_iter));
invhessian_xparam1_iter = inv(hessian_xparam1_iter./(hsd_iter*hsd_iter'))./(hsd_iter*hsd_iter');
std_via_invhessian_xparam1_iter = sqrt(diag(invhessian_xparam1_iter));
else
std_via_invhessian_xparam1_iter = NaN(size(xparam1));
end
fprintf(' and displaying intermediate results.');
if doBayesianEstimation
tbl_title_iter = sprintf('BAYESIAN %s (OPTIM ITERATION %d) VERBOSE',strrep(options_mom_.mom.mom_method,'_',' '),optim_iter);
field_name_iter = sprintf('posterior_iter_%d',optim_iter);
else
tbl_title_iter = sprintf('FREQUENTIST %s (OPTIM ITERATION %d) VERBOSE',strrep(options_mom_.mom.mom_method,'_',' '),optim_iter);
field_name_iter = sprintf('iter_%d',optim_iter);
end
mom_verbose.(field_name_iter) = display_estimation_results_table(xparam1,std_via_invhessian_xparam1_iter,M_,options_mom_,estim_params_,bayestopt_,[],prior_dist_names,tbl_title_iter,field_name_iter);
end
xparam0 = xparam1;
hessian_xparam0 = hessian_xparam1;
end
if options_mom_.cova_compute
if options_mom_.mom.verbose
hessian_xparam1 = hessian_xparam1_iter;
else
fprintf('\nComputing Hessian at the mode.\n');
hessian_xparam1 = hessian(objective_function, xparam1, options_mom_.gstep,...
data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
hessian_xparam1 = reshape(hessian_xparam1, length(xparam1), length(xparam1));
end
end
parameter_names = bayestopt_.name;
if options_mom_.cova_compute || options_mom_.mode_compute==5 || options_mom_.mode_compute==6
hh = hessian_xparam1;
save([M_.dname filesep 'method_of_moments' filesep M_.fname '_mode.mat'],'xparam1','hh','parameter_names','fval');
else
save([M_.dname filesep 'method_of_moments' filesep M_.fname '_mode.mat'],'xparam1','parameter_names','fval');
end

240
matlab/+mom/objective_function.m
View File

@ -1,33 +1,27 @@
function [fval, info, exit_flag, df, junk_hessian, Q, model_moments, model_moments_params_derivs, irf_model_varobs] = objective_function(xparam, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% [fval, info, exit_flag, df, junk_hessian, Q, model_moments, model_moments_params_derivs, irf_model_varobs] = objective_function(xparam, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
function [fval, info, exit_flag, df, junkHessian, oo_, M_] = objective_function(xparam, Bounds, oo_, estim_params_, M_, options_mom_)
% [fval, info, exit_flag, df, junk1, oo_, M_] = objective_function(xparam, Bounds, oo_, estim_params_, M_, options_mom_)
% -------------------------------------------------------------------------
% This function evaluates the objective function for method of moments estimation,
% i.e. distance between model and data moments/IRFs, possibly augmented with priors.
% -------------------------------------------------------------------------
% INPUTS (same ones as in dsge_likelihood.m and dsge_var_likelihood.m)
% - xparam: [vector] current value of estimated parameters as returned by set_prior()
% - data_moments: [vector] data with moments/IRFs to match (corresponds to dataset_ in likelihood-based estimation)
% - weighting_info: [structure] storing information on weighting matrices (corresponds to dataset_info in likelihood-based estimation)
% - options_mom_: [structure] information about all settings (specified by the user, preprocessor, and taken from global options_)
% - M_ [structure] model information
% - estim_params_: [structure] information from estimated_params block
% - bayestopt_: [structure] information on the prior distributions
% - BoundsInfo: [structure] parameter bounds
% - dr: [structure] reduced form model
% - endo_steady_state: [vector] steady state value for endogenous variables (initval)
% - exo_steady_state: [vector] steady state value for exogenous variables (initval)
% - exo_det_steady_state: [vector] steady state value for exogenous deterministic variables (initval)
% This function evaluates the objective function for method of moments estimation
% =========================================================================
% INPUTS
% o xparam: [vector] current value of estimated parameters as returned by set_prior()
% o Bounds: [structure] containing parameter bounds
% o oo_: [structure] for results
% o estim_params_: [structure] describing the estimated_parameters
% o M_ [structure] describing the model
% o options_mom_: [structure] information about all settings (specified by the user, preprocessor, and taken from global options_)
% -------------------------------------------------------------------------
% OUTPUTS
% - fval: [double] value of the quadratic form of the moment difference (except for lsqnonlin, where this is done implicitly)
% - info: [vector] information on error codes and penalties
% - exit_flag: [double] flag for exit status (0 if error, 1 if no error)
% - df: [matrix] analytical jacobian of the moment difference (wrt paramters), currently for GMM only
% - junk_hessian: [matrix] empty matrix required for optimizer interface (Hessian would typically go here)
% - Q: [double] value of the quadratic form of the moment difference
% - model_moments: [vector] model moments
% - model_moments_params_derivs: [matrix] analytical jacobian of the model moments wrt estimated parameters (currently for GMM only)
% - irf_model_varobs: [matrix] model IRFs for observable variables (used for plotting matched IRfs in mom.run)
% o fval: [double] value of the quadratic form of the moment difference (except for lsqnonlin, where this is done implicitly)
% o info: [vector] information on error codes and penalties
% o exit_flag: [double] flag for exit status (0 if error, 1 if no error)
% o df: [matrix] analytical jacobian of the moment difference (wrt paramters), currently for GMM only
% o junkHessian: [matrix] empty matrix required for optimizer interface (Hessian would typically go here)
% o oo_: [structure] results with the following updated fields:
% - oo_.mom.model_moments: [vector] model moments
% - oo_.mom.Q: [double] value of the quadratic form of the moment difference
% - oo_.mom.model_moments_params_derivs: [matrix] analytical jacobian of the model moments wrt estimated parameters (currently for GMM only)
% o M_: [structure] updated model structure
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
@ -35,18 +29,13 @@ function [fval, info, exit_flag, df, junk_hessian, Q, model_moments, model_momen
% -------------------------------------------------------------------------
% This function calls
% o check_bounds_and_definiteness_estimation
% o get_lower_cholesky_covariance
% o get_perturbation_params_derivs
% o getIrfShocksIndx
% o irf
% o mom.get_data_moments
% o priordens
% o pruned_state_space_system
% o resol
% o set_all_parameters
% o simult_
% -------------------------------------------------------------------------
% =========================================================================
% Copyright © 2020-2023 Dynare Team
%
% This file is part of Dynare.
@ -63,27 +52,28 @@ function [fval, info, exit_flag, df, junk_hessian, Q, model_moments, model_momen
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% =========================================================================
%% TO DO
% check the info values and make use of meaningful penalties
% how do we do the penalty for the prior??
%------------------------------------------------------------------------------
% Initialization of the returned variables and others...
%------------------------------------------------------------------------------
irf_model_varobs = [];
model_moments_params_derivs = [];
model_moments = [];
Q = [];
junk_hessian = [];
junkHessian = [];
df = []; % required to be empty by e.g. newrat
if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_jacobian
if options_mom_.mom.vector_output == 1
if options_mom_.mom.penalized_estimator
df = NaN(options_mom_.mom.mom_nbr+length(xparam),length(xparam));
df = nan(size(oo_.mom.data_moments,1)+length(xparam),length(xparam));
else
df = NaN(options_mom_.mom.mom_nbr,length(xparam));
df = nan(size(oo_.mom.data_moments,1),length(xparam));
end
else
df = NaN(length(xparam),1);
df = nan(length(xparam),1);
end
end
end
@ -92,13 +82,14 @@ end
%--------------------------------------------------------------------------
% Get the structural parameters and define penalties
%--------------------------------------------------------------------------
% Ensure that xparam1 is a column vector; particleswarm.m requires this.
xparam = xparam(:);
M_ = set_all_parameters(xparam, estim_params_, M_);
[fval,info,exit_flag] = check_bounds_and_definiteness_estimation(xparam, M_, estim_params_, BoundsInfo);
[fval,info,exit_flag] = check_bounds_and_definiteness_estimation(xparam, M_, estim_params_, Bounds);
if info(1)
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = ones(options_mom_.mom.mom_nbr,1)*options_mom_.huge_number;
fval = ones(size(oo_.mom.data_moments,1),1)*options_mom_.huge_number;
end
return
end
@ -107,8 +98,9 @@ end
%--------------------------------------------------------------------------
% Call resol to compute steady state and model solution
%--------------------------------------------------------------------------
% Compute linear approximation around the deterministic steady state
[dr, info, M_.params] = resol(0, M_, options_mom_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
[oo_.dr, info, M_.params] = resol(0, M_, options_mom_, oo_.dr ,oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
% Return, with endogenous penalty when possible, if resol issues an error code
if info(1)
if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 ||...
@ -119,7 +111,7 @@ if info(1)
info(4) = info(2);
exit_flag = 0;
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = ones(options_mom_.mom.mom_nbr,1)*options_mom_.huge_number;
fval = ones(size(oo_.mom.data_moments,1),1)*options_mom_.huge_number;
end
return
else
@ -127,7 +119,7 @@ if info(1)
info(4) = 0.1;
exit_flag = 0;
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = ones(options_mom_.mom.mom_nbr,1)*options_mom_.huge_number;
fval = ones(size(oo_.mom.data_moments,1),1)*options_mom_.huge_number;
end
return
end
@ -152,44 +144,44 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
indpcorr = estim_params_.corrx(:,1:2); % values correspond to varexo declaration order, row number corresponds to order in estimated_params
end
if estim_params_.nvn || estim_params_.ncn % nvn is number of stderr parameters and ncn is number of corr parameters of measurement innovations as declared in estimated_params
error('Analytic computation of standard errrors does not (yet) support measurement errors.\nInstead, define them explicitly as varexo and provide measurement equations in the model definition.\nAlternatively, use numerical standard errors.');
error('Analytic computation of standard errrors does not (yet) support measurement errors.\nInstead, define them explicitly as varexo and provide measurement equations in the model definition.\nAlternatively, use numerical standard errors.')
end
modparam_nbr = estim_params_.np; % number of model parameters as declared in estimated_params
stderrparam_nbr = estim_params_.nvx; % number of stderr parameters
corrparam_nbr = estim_params_.ncx; % number of corr parameters
totparam_nbr = stderrparam_nbr+corrparam_nbr+modparam_nbr;
dr.derivs = identification.get_perturbation_params_derivs(M_, options_mom_, estim_params_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, indpmodel, indpstderr, indpcorr, 0); %analytic derivatives of perturbation matrices
model_moments_params_derivs = NaN(options_mom_.mom.mom_nbr,totparam_nbr);
pruned_state_space = pruned_SS.pruned_state_space_system(M_, options_mom_, dr, options_mom_.mom.obs_var, options_mom_.ar, 0, 1);
oo_.dr.derivs = get_perturbation_params_derivs(M_, options_mom_, estim_params_, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, indpmodel, indpstderr, indpcorr, 0); %analytic derivatives of perturbation matrices
oo_.mom.model_moments_params_derivs = NaN(options_mom_.mom.mom_nbr,totparam_nbr);
pruned_state_space = pruned_state_space_system(M_, options_mom_, oo_.dr, oo_.mom.obs_var, options_mom_.ar, 0, 1);
else
pruned_state_space = pruned_SS.pruned_state_space_system(M_, options_mom_, dr, options_mom_.mom.obs_var, options_mom_.ar, 0, 0);
pruned_state_space = pruned_state_space_system(M_, options_mom_, oo_.dr, oo_.mom.obs_var, options_mom_.ar, 0, 0);
end
model_moments = NaN(options_mom_.mom.mom_nbr,1);
oo_.mom.model_moments = NaN(options_mom_.mom.mom_nbr,1);
for jm = 1:size(M_.matched_moments,1)
% First moments
if ~options_mom_.prefilter && (sum(M_.matched_moments{jm,3}) == 1)
idx1 = (options_mom_.mom.obs_var == find(dr.order_var==M_.matched_moments{jm,1}) );
model_moments(jm,1) = pruned_state_space.E_y(idx1);
idx1 = (oo_.mom.obs_var == find(oo_.dr.order_var==M_.matched_moments{jm,1}) );
oo_.mom.model_moments(jm,1) = pruned_state_space.E_y(idx1);
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
model_moments_params_derivs(jm,:) = pruned_state_space.dE_y(idx1,:);
oo_.mom.model_moments_params_derivs(jm,:) = pruned_state_space.dE_y(idx1,:);
end
end
% second moments
if (sum(M_.matched_moments{jm,3}) == 2)
idx1 = (options_mom_.mom.obs_var == find(dr.order_var==M_.matched_moments{jm,1}(1)) );
idx2 = (options_mom_.mom.obs_var == find(dr.order_var==M_.matched_moments{jm,1}(2)) );
idx1 = (oo_.mom.obs_var == find(oo_.dr.order_var==M_.matched_moments{jm,1}(1)) );
idx2 = (oo_.mom.obs_var == find(oo_.dr.order_var==M_.matched_moments{jm,1}(2)) );
if nnz(M_.matched_moments{jm,2}) == 0
% covariance
if options_mom_.prefilter
model_moments(jm,1) = pruned_state_space.Var_y(idx1,idx2);
oo_.mom.model_moments(jm,1) = pruned_state_space.Var_y(idx1,idx2);
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
model_moments_params_derivs(jm,:) = pruned_state_space.dVar_y(idx1,idx2,:);
oo_.mom.model_moments_params_derivs(jm,:) = pruned_state_space.dVar_y(idx1,idx2,:);
end
else
model_moments(jm,1) = pruned_state_space.Var_y(idx1,idx2) + pruned_state_space.E_y(idx1)*pruned_state_space.E_y(idx2)';
oo_.mom.model_moments(jm,1) = pruned_state_space.Var_y(idx1,idx2) + pruned_state_space.E_y(idx1)*pruned_state_space.E_y(idx2)';
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
for jp=1:totparam_nbr
model_moments_params_derivs(jm,jp) = pruned_state_space.dVar_y(idx1,idx2,jp) + pruned_state_space.dE_y(idx1,jp)*pruned_state_space.E_y(idx2)' + pruned_state_space.E_y(idx1)*pruned_state_space.dE_y(idx2,jp)';
oo_.mom.model_moments_params_derivs(jm,jp) = pruned_state_space.dVar_y(idx1,idx2,jp) + pruned_state_space.dE_y(idx1,jp)*pruned_state_space.E_y(idx2)' + pruned_state_space.E_y(idx1)*pruned_state_space.dE_y(idx2,jp)';
end
end
end
@ -197,15 +189,15 @@ if strcmp(options_mom_.mom.mom_method,'GMM')
% autocovariance
lag = -M_.matched_moments{jm,2}(2); %note that leads/lags in M_.matched_moments are transformed such that first entry is always 0 and the second is a lag
if options_mom_.prefilter
model_moments(jm,1) = pruned_state_space.Var_yi(idx1,idx2,lag);
oo_.mom.model_moments(jm,1) = pruned_state_space.Var_yi(idx1,idx2,lag);
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
model_moments_params_derivs(jm,:) = pruned_state_space.dVar_yi(idx1,idx2,lag,:);
oo_.mom.model_moments_params_derivs(jm,:) = pruned_state_space.dVar_yi(idx1,idx2,lag,:);
end
else
model_moments(jm,1) = pruned_state_space.Var_yi(idx1,idx2,lag) + pruned_state_space.E_y(idx1)*pruned_state_space.E_y(idx2)';
oo_.mom.model_moments(jm,1) = pruned_state_space.Var_yi(idx1,idx2,lag) + pruned_state_space.E_y(idx1)*pruned_state_space.E_y(idx2)';
if options_mom_.mom.compute_derivs && ( options_mom_.mom.analytic_standard_errors || options_mom_.mom.analytic_jacobian )
for jp=1:totparam_nbr
model_moments_params_derivs(jm,jp) = vec( pruned_state_space.dVar_yi(idx1,idx2,lag,jp) + pruned_state_space.dE_y(idx1,jp)*pruned_state_space.E_y(idx2)' + pruned_state_space.E_y(idx1)*pruned_state_space.dE_y(idx2,jp)');
oo_.mom.model_moments_params_derivs(jm,jp) = vec( pruned_state_space.dVar_yi(idx1,idx2,lag,jp) + pruned_state_space.dE_y(idx1,jp)*pruned_state_space.E_y(idx2)' + pruned_state_space.E_y(idx1)*pruned_state_space.dE_y(idx2,jp)');
end
end
end
@ -225,22 +217,26 @@ if strcmp(options_mom_.mom.mom_method,'SMM')
scaled_shock_series = zeros(size(options_mom_.mom.shock_series)); % initialize
scaled_shock_series(:,i_exo_var) = options_mom_.mom.shock_series(:,i_exo_var)*chol_S; % set non-zero entries
% simulate series
y_sim = simult_(M_, options_mom_, dr.ys, dr, scaled_shock_series, options_mom_.order);
% provide meaningful penalty if data is NaN or Inf
y_sim = simult_(M_, options_mom_, oo_.dr.ys, oo_.dr, scaled_shock_series, options_mom_.order);
% provide meaningful penalty if data is nan or inf
if any(any(isnan(y_sim))) || any(any(isinf(y_sim)))
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = Inf(size(oo_.mom.Sw,1),1);
else
fval = Inf;
end
info(1)=180;
info(4) = 0.1;
exit_flag = 0;
fval = Inf;
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = ones(options_mom_.mom.mom_nbr,1)*options_mom_.huge_number;
fval = ones(size(oo_.mom.data_moments,1),1)*options_mom_.huge_number;
end
return
end
% remove burn-in and focus on observables (note that y_sim is in declaration order)
y_sim = y_sim(dr.order_var(options_mom_.mom.obs_var) , end-options_mom_.mom.long+1:end)';
y_sim = y_sim(oo_.dr.order_var(oo_.mom.obs_var) , end-options_mom_.mom.long+1:end)';
if ~all(diag(M_.H)==0)
i_ME = setdiff(1:size(M_.H,1),find(diag(M_.H) == 0)); % find ME with 0 variance
i_ME = setdiff([1:size(M_.H,1)],find(diag(M_.H) == 0)); % find ME with 0 variance
chol_S = chol(M_.H(i_ME,i_ME)); % decompose rest
shock_mat=zeros(size(options_mom_.mom.ME_shock_series)); % initialize
shock_mat(:,i_ME)=options_mom_.mom.ME_shock_series(:,i_ME)*chol_S;
@ -250,119 +246,46 @@ if strcmp(options_mom_.mom.mom_method,'SMM')
if options_mom_.prefilter
y_sim = bsxfun(@minus, y_sim, mean(y_sim,1));
end
model_moments = mom.get_data_moments(y_sim, options_mom_.mom.obs_var, dr.inv_order_var, M_.matched_moments, options_mom_);
end
%------------------------------------------------------------------------------
% IRF_MATCHING using STOCH_SIMUL: Compute IRFs given model solution and Cholesky
% decomposition on shock covariance matrix; this resembles the core codes in
% stoch_simul
%------------------------------------------------------------------------------
if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING') && strcmp(options_mom_.mom.simulation_method,'STOCH_SIMUL')
cs = get_lower_cholesky_covariance(M_.Sigma_e,options_mom_.add_tiny_number_to_cholesky);
irf_shocks_indx = getIrfShocksIndx(M_, options_mom_);
model_irf = NaN(options_mom_.irf,M_.endo_nbr,M_.exo_nbr);
for i = irf_shocks_indx
if options_mom_.order>1 && options_mom_.relative_irf % normalize shock to 0.01 before IRF generation for GIRFs; multiply with 100 later
y_irf = irf(M_, options_mom_, dr, cs(:,i)./cs(i,i)/100, options_mom_.irf, options_mom_.drop, options_mom_.replic, options_mom_.order);
else % for linear model, rescaling is done later
y_irf = irf(M_, options_mom_, dr, cs(:,i), options_mom_.irf, options_mom_.drop, options_mom_.replic, options_mom_.order);
end
if any(any(isnan(y_irf))) && ~options_mom_.pruning && ~(options_mom_.order==1)
info(1) = 181;
info(4) = 0.1;
fval = Inf;
exit_flag = 0;
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = ones(options_mom_.mom.mom_nbr,1)*options_mom_.huge_number;
end
message = get_error_message(info,options_mom_);
fprintf('\n%s\n info = %d for shock %s.\n', message, info(1), M_.exo_names{i});
return
end
if options_mom_.relative_irf
if options_mom_.order==1 % multiply with 100 for backward compatibility
y_irf = 100*y_irf/cs(i,i);
end
end
model_irf(:,:,i) = transpose(y_irf);
end
% do transformations on model IRFs if irf_matching_file is provided
if ~isempty(options_mom_.mom.irf_matching_file.name)
[model_irf, check] = feval(str2func(options_mom_.mom.irf_matching_file.name), model_irf, M_, options_mom_, dr.ys);
if check
fval = Inf;
info(1) = 180;
info(4) = 0.1;
exit_flag = 0;
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = ones(options_mom_.mom.mom_nbr,1)*options_mom_.huge_number;
end
return
end
end
irf_model_varobs = model_irf(:,options_mom_.varobs_id,:); % focus only on observables (this will be used later for plotting)
model_moments = irf_model_varobs(options_mom_.mom.irfIndex); % focus only on selected IRF periods
oo_.mom.model_moments = mom.get_data_moments(y_sim, oo_.mom.obs_var, oo_.dr.inv_order_var, M_.matched_moments, options_mom_);
end
%--------------------------------------------------------------------------
% Compute quadratic target function
%--------------------------------------------------------------------------
moments_difference = data_moments - model_moments;
moments_difference = oo_.mom.data_moments - oo_.mom.model_moments;
if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
Q = transpose(moments_difference)*weighting_info.W*moments_difference;
% log-likelihood
lnlik = options_mom_.mom.mom_nbr/2*log(1/2/pi) - 1/2*weighting_info.Winv_logdet - 1/2*Q;
if isinf(lnlik)
fval = Inf; info(1) = 50; info(4) = 0.1; exit_flag = 0;
return
end
if isnan(lnlik)
fval = Inf; info(1) = 45; info(4) = 0.1; exit_flag = 0;
return
end
if imag(lnlik)~=0
fval = Inf; info(1) = 46; info(4) = 0.1; exit_flag = 0;
return
end
% add log prior if necessary
lnprior = priordens(xparam,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
fval = - (lnlik + lnprior);
elseif strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
residuals = sqrt(options_mom_.mom.weighting_matrix_scaling_factor)*weighting_info.Sw*moments_difference;
Q = residuals'*residuals;
if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
residuals = sqrt(options_mom_.mom.weighting_matrix_scaling_factor)*oo_.mom.Sw*moments_difference;
oo_.mom.Q = residuals'*residuals;
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
fval = residuals;
if options_mom_.mom.penalized_estimator
fval=[fval;(xparam-bayestopt_.p1)./bayestopt_.p2];
fval=[fval;(xparam-oo_.mom.prior.mean)./sqrt(diag(oo_.mom.prior.variance))];
end
else
fval = Q;
fval = oo_.mom.Q;
if options_mom_.mom.penalized_estimator
fval=fval+(xparam-bayestopt_.p1)'/(diag(bayestopt_.p2.^2))*(xparam-bayestopt_.p1);
fval=fval+(xparam-oo_.mom.prior.mean)'/oo_.mom.prior.variance*(xparam-oo_.mom.prior.mean);
end
end
if options_mom_.mom.compute_derivs && options_mom_.mom.analytic_jacobian
if options_mom_.mom.penalized_estimator
dxparam = eye(length(xparam));
dxparam1 = eye(length(xparam));
end
for jp=1:length(xparam)
dmoments_difference = - model_moments_params_derivs(:,jp);
dresiduals = sqrt(options_mom_.mom.weighting_matrix_scaling_factor)*weighting_info.Sw*dmoments_difference;
dmoments_difference = - oo_.mom.model_moments_params_derivs(:,jp);
dresiduals = sqrt(options_mom_.mom.weighting_matrix_scaling_factor)*oo_.mom.Sw*dmoments_difference;
if options_mom_.mom.vector_output == 1 % lsqnonlin requires vector output
if options_mom_.mom.penalized_estimator
df(:,jp)=[dresiduals;dxparam(:,jp)./bayestopt_.p2];
df(:,jp)=[dresiduals;dxparam1(:,jp)./sqrt(diag(oo_.mom.prior.variance))];
else
df(:,jp) = dresiduals;
end
else
df(jp,1) = dresiduals'*residuals + residuals'*dresiduals;
if options_mom_.mom.penalized_estimator
df(jp,1)=df(jp,1)+(dxparam(:,jp))'/(diag(bayestopt_.p2.^2))*(xparam-bayestopt_.p1)+(xparam-bayestopt_.p1)'/(diag(bayestopt_.p2.^2))*(dxparam(:,jp));
df(jp,1)=df(jp,1)+(dxparam1(:,jp))'/oo_.mom.prior.variance*(xparam-oo_.mom.prior.mean)+(xparam-oo_.mom.prior.mean)'/oo_.mom.prior.variance*(dxparam1(:,jp));
end
end
end
@ -370,4 +293,5 @@ elseif strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_
end
end % main function end
end % main function end

56
matlab/+mom/optimal_weighting_matrix.m
View File

@ -2,10 +2,9 @@ function W_opt = optimal_weighting_matrix(m_data, moments, q_lag)
% W_opt = optimal_weighting_matrix(m_data, moments, q_lag)
% -------------------------------------------------------------------------
% This function computes the optimal weigthing matrix by a Bartlett kernel with maximum lag q_lag
% Adapted from replication codes of Andreasen, Fernández-Villaverde, 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.
% -------------------------------------------------------------------------
% Adapted from replication codes of
% o Andreasen, Fernández-Villaverde, 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.
% =========================================================================
% INPUTS
% o m_data [T x numMom] selected data moments at each point in time
% o moments [numMom x 1] selected estimated moments (either data_moments or estimated model_moments)
@ -18,10 +17,9 @@ function W_opt = optimal_weighting_matrix(m_data, moments, q_lag)
% o mom.run.m
% -------------------------------------------------------------------------
% This function calls:
% o corr_matrix (embedded)
% -------------------------------------------------------------------------
% Copyright © 2020-2023 Dynare Team
% o CorrMatrix (embedded)
% =========================================================================
% Copyright © 2020-2021 Dynare Team
%
% This file is part of Dynare.
%
@ -37,42 +35,46 @@ function W_opt = optimal_weighting_matrix(m_data, moments, q_lag)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% -------------------------------------------------------------------------
% Author(s):
% o Willi Mutschler (willi@mutschler.eu)
% o Johannes Pfeifer (johannes.pfeifer@unibw.de)
% =========================================================================
% initialize
[T,num_Mom] = size(m_data); % note that in m_data NaN values (due to leads or lags in matched_moments and missing data) were replaced by the mean
% Initialize
[T,num_Mom] = size(m_data); %note that in m_data NaN values (due to leads or lags in matched_moments and missing data) were replaced by the mean
% center around moments (could be either data_moments or model_moments)
h_func = m_data - repmat(moments',T,1);
h_Func = m_data - repmat(moments',T,1);
% the required correlation matrices
gamma_array = zeros(num_Mom,num_Mom,q_lag);
gamma0 = corr_matrix(h_func,T,num_Mom,0);
% The required correlation matrices
GAMA_array = zeros(num_Mom,num_Mom,q_lag);
GAMA0 = Corr_Matrix(h_Func,T,num_Mom,0);
if q_lag > 0
for ii=1:q_lag
gamma_array(:,:,ii) = corr_matrix(h_func,T,num_Mom,ii);
GAMA_array(:,:,ii) = Corr_Matrix(h_Func,T,num_Mom,ii);
end
end
% the estimate of S
S = gamma0;
% The estimate of S
S = GAMA0;
if q_lag > 0
for ii=1:q_lag
S = S + (1-ii/(q_lag+1))*(gamma_array(:,:,ii) + gamma_array(:,:,ii)');
S = S + (1-ii/(q_lag+1))*(GAMA_array(:,:,ii) + GAMA_array(:,:,ii)');
end
end
% the estimate of W
% The estimate of W
W_opt = S\eye(size(S,1));
W_opt = (W_opt+W_opt')/2; % ensure symmetry
end % main function end
W_opt=(W_opt+W_opt')/2; %assure symmetry
end
% The correlation matrix
function gamma_corr = corr_matrix(h_func,T,num_Mom,v)
gamma_corr = zeros(num_Mom,num_Mom);
function GAMA_corr = Corr_Matrix(h_Func,T,num_Mom,v)
GAMA_corr = zeros(num_Mom,num_Mom);
for t = 1+v:T
gamma_corr = gamma_corr + h_func(t-v,:)'*h_func(t,:);
GAMA_corr = GAMA_corr + h_Func(t-v,:)'*h_Func(t,:);
end
gamma_corr = gamma_corr/T;
end % corr_matrix end
GAMA_corr = GAMA_corr/T;
end

30
matlab/+mom/print_info_on_estimation_settings.m
View File

@ -1,12 +1,11 @@
function print_info_on_estimation_settings(options_mom_, number_of_estimated_parameters, do_bayesian_estimation)
% print_info_on_estimation_settings(options_mom_, number_of_estimated_parameters, do_bayesian_estimation)
function print_info_on_estimation_settings(options_mom_, number_of_estimated_parameters)
% function print_info_on_estimation_settings(options_mom_, number_of_estimated_parameters)
% -------------------------------------------------------------------------
% Print information on the method of moments estimation settings to the console
% -------------------------------------------------------------------------
% =========================================================================
% INPUTS
% options_mom_ [struct] options for the method of moments estimation
% number_of_estimated_parameters [integer] number of estimated parameters
% do_bayesian_estimation [boolean] true if the estimation is Bayesian
% options_mom_ [struct] Options for the method of moments estimation
% number_of_estimated_parameters [integer] Number of estimated parameters
% -------------------------------------------------------------------------
% OUTPUT
% No output, just displays the chosen settings
@ -16,8 +15,7 @@ function print_info_on_estimation_settings(options_mom_, number_of_estimated_par
% -------------------------------------------------------------------------
% This function calls
% o skipline
% -------------------------------------------------------------------------
% =========================================================================
% Copyright © 2023 Dynare Team
%
% This file is part of Dynare.
@ -34,8 +32,7 @@ function print_info_on_estimation_settings(options_mom_, number_of_estimated_par
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% =========================================================================
fprintf('\n---------------------------------------------------\n')
if strcmp(options_mom_.mom.mom_method,'SMM')
fprintf('Simulated method of moments with');
@ -52,16 +49,7 @@ if strcmp(options_mom_.mom.mom_method,'SMM') || strcmp(options_mom_.mom.mom_meth
fprintf('\n - penalized estimation using deviation from prior mean and weighted with prior precision');
end
end
if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
if do_bayesian_estimation
fprintf('Bayesian Impulse Response Function Matching with');
else
fprintf('Frequentist Impulse Response Function Matching with');
end
if ~isempty(options_mom_.mom.irf_matching_file.name)
fprintf('\n - irf_matching_file: %s',[options_mom_.mom.irf_matching_file.path filesep options_mom_.mom.irf_matching_file.name '.m']);
end
end
for i = 1:length(options_mom_.optimizer_vec)
if i == 1
str = '- optimizer (mode_compute';
@ -130,8 +118,6 @@ if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_meth
fprintf('\n - standard errors: numerical derivatives');
end
fprintf('\n - number of matched moments: %d', options_mom_.mom.mom_nbr);
elseif strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
fprintf('\n - number of matched IRFs: %d', options_mom_.mom.mom_nbr);
end
fprintf('\n - number of parameters: %d', number_of_estimated_parameters);
fprintf('\n\n');

631
matlab/+mom/run.m
View File

@ -6,47 +6,31 @@ function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_,
% o Preparing local options_mom_ structure
% o Checking the options and the compatibility of the settings
% o Initializations of variables, orderings and state space representation
% o Checks and transformations for matched_moments structure
% o Checks and transformations for matched_irfs and matched_irfs_weights structure
% o Checks and transformations for matched moments structure
% o Checks and transformations for estimated parameters, priors, and bounds
% o Checks and transformations for data
% o Checks for objective function at initial parameters
% o Mode computation: optimization
% - GMM/SMM: iterated optimization
% - IRF_MATCHING: optimization
% o Bayesian MCMC estimation
% o GMM/SMM: iterated method of moments estimation
% o GMM/SMM: J-Test and fit of moments%
% o Display of results
% - GMM/SMM: J-Test and fit of moments
% - IRF_MATCHING: fit of IRFs
% o Clean up
% -------------------------------------------------------------------------
% Note that we call a "mode" the minimum of the objective function, i.e.
% the parameter vector that minimizes the distance between the moments/IRFs
% computed from the model and the moments/IRFs computed from the data.
% -------------------------------------------------------------------------
% This function is inspired by replication codes accompanied to the following papers:
% GMM/SMM:
% o Andreasen, Fernández-Villaverde, 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.
% o Born, Pfeifer (2014): "Risk Matters: Comment", American Economic Review, 104(12):4231-4239.
% o Mutschler (2018): "Higher-order statistics for DSGE models", Econometrics and Statistics, 6:44-56.
% o Ruge-Murcia (2007): "Methods to Estimate Dynamic Stochastic General Equilibrium Models", Journal of Economic Dynamics and Control, 31(8):2599-2636.
% IRF MATCHING:
% o Christiano, Trabandt, Walentin (2010): "DSGE Models for Monetary Policy Analysis." In Handbook of Monetary Economics, 3:285–367.
% o Christiano, Eichenbaum, Trabandt (2016): "Unemployment and Business Cycles." Econometrica, 84: 1523-1569.
% o Ruge-Murcia (2020): "Estimating Nonlinear Dynamic Equilibrium Models by Matching Impulse Responses", Economics Letters, 197.
% -------------------------------------------------------------------------
% =========================================================================
% INPUTS
% o bayestopt_: [structure] information about priors
% o options_: [structure] information about global options
% o oo_: [structure] results
% o oo_: [structure] storage for results
% o estim_params_: [structure] information about estimated parameters
% o M_: [structure] information about model with
% o matched_moments: [cell] information about selected moments to match in GMM/SMM estimation
% vars: matched_moments{:,1});
% lead/lags: matched_moments{:,2};
% powers: matched_moments{:,3};
% o matched_irfs: [cell] information about selected IRFs to match in IRF_MATCHING estimation
% o matched_irfs_weights: [cell] information about entries in weight matrix for an IRF_MATCHING estimation
% o options_mom_: [structure] information about settings specified by the user
% -------------------------------------------------------------------------
% OUTPUTS
@ -58,26 +42,25 @@ function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_,
% o driver.m
% -------------------------------------------------------------------------
% This function calls
% o cellofchararraymaxlength
% o check_for_calibrated_covariances
% o check_mode_file
% o check_posterior_sampler_options
% o check_prior_bounds
% o check_prior_stderr_corr
% o check_steady_state_changes_parameters
% o check_varobs_are_endo_and_declared_once
% o check_hessian_at_the_mode
% o display_estimation_results_table
% o do_parameter_initialization
% o dyn_latex_table
% o dynare_minimize_objective
% o dyntable
% o get_all_parameters
% o get_dynare_random_generator_state
% o get_matrix_entries_for_psd_check
% o M_.fname '_prior_restrictions'
% o makedataset
% o mode_check
% o mom.check_irf_matching_file
% o mom.check_plot
% o mom.default_option_mom_values
% o mom.get_data_moments
% o mom.matched_irfs_blocks
% o mom.matched_moments_block
% o mom.objective_function
% o mom.optimal_weighting_matrix
@ -94,9 +77,8 @@ function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_,
% o set_state_space
% o skipline
% o test_for_deep_parameters_calibration
% o transform_prior_to_laplace_prior
% o warning_config
% =========================================================================
% Copyright © 2020-2023 Dynare Team
%
% This file is part of Dynare.
@ -113,8 +95,34 @@ function [oo_, options_mom_, M_] = run(bayestopt_, options_, oo_, estim_params_,
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% -------------------------------------------------------------------------
% Maintaining Author(s):
% o Willi Mutschler (willi@mutschler.eu)
% o Johannes Pfeifer (johannes.pfeifer@unibw.de)
% =========================================================================
fprintf('\n==== Method of Moments Estimation (%s) ====\n\n',options_mom_.mom.mom_method);
% -------------------------------------------------------------------------
% TO DO LISTS
% -------------------------------------------------------------------------
% GENERAL
% - document all options in manual
% - document analytic_jacobian better
% - make endogenous_prior_restrictions work
% - dirname option to save output to different directory not yet implemented
% - create test for prior restrictions file
% - add mode_file option
% - implement penalty objective
% - test optimizers
% GMM/SMM
% - speed up pruned_state_space_system (by using doubling with old initial values, hardcoding zeros, other "tricks" used in e.g. nlma)
% - add option to use autocorrelations (we have useautocorr in identification toolbox already)
% - SMM with extended path
% - deal with measurement errors (once @wmutschl has implemented this in identification toolbox)
% - display scaled moments
% - enable first moments despite prefilter
% - do "true" Bayesian GMM and SMM not only penalized
fprintf('\n==== Method of Moments Estimation (%s) ====\n\n',options_mom_.mom.mom_method)
% -------------------------------------------------------------------------
@ -122,33 +130,29 @@ fprintf('\n==== Method of Moments Estimation (%s) ====\n\n',options_mom_.mom.mom
% -------------------------------------------------------------------------
if isempty(estim_params_) % structure storing the info about estimated parameters in the estimated_params block
if ~(isfield(estim_params_,'nvx') && (size(estim_params_.var_exo,1)+size(estim_params_.var_endo,1)+size(estim_params_.corrx,1)+size(estim_params_.corrn,1)+size(estim_params_.param_vals,1))==0)
error('method_of_moments: You need to provide an ''estimated_params'' block!');
error('method_of_moments: You need to provide an ''estimated_params'' block!')
else
error('method_of_moments: The ''estimated_params'' block must not be empty!');
error('method_of_moments: The ''estimated_params'' block must not be empty!')
end
end
if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
if ~isfield(M_,'matched_moments') || isempty(M_.matched_moments) % structure storing the moments used for GMM and SMM estimation
error('method_of_moments: You need to provide a ''matched_moments'' block for ''mom_method=%s''!',options_mom_.mom.mom_method);
end
elseif strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
if ~isfield(M_,'matched_irfs') || isempty(M_.matched_irfs) % structure storing the irfs used for matching
error('method_of_moments: You need to provide a ''matched_irfs'' block for ''mom_method=%s''!',options_mom_.mom.mom_method);
error('method_of_moments: You need to provide a ''matched_moments'' block for ''mom_method=%s''!',options_mom_.mom.mom_method)
end
end
if (~isempty(estim_params_.var_endo) || ~isempty(estim_params_.corrn)) && strcmp(options_mom_.mom.mom_method, 'GMM')
error('method_of_moments: GMM estimation does not support measurement error(s) yet. Please specify them as a structural shock!');
error('method_of_moments: GMM estimation does not support measurement error(s) yet. Please specify them as a structural shock!')
end
do_bayesian_estimation = [estim_params_.var_exo(:,5); estim_params_.var_endo(:,5); estim_params_.corrx(:,6); estim_params_.corrn(:,6); estim_params_.param_vals(:,5)];
if all(do_bayesian_estimation~=0)
do_bayesian_estimation = true;
elseif all(do_bayesian_estimation==0)
do_bayesian_estimation = false;
doBayesianEstimation = [estim_params_.var_exo(:,5); estim_params_.var_endo(:,5); estim_params_.corrx(:,6); estim_params_.corrn(:,6); estim_params_.param_vals(:,5)];
if all(doBayesianEstimation~=0)
doBayesianEstimation = true;
elseif all(doBayesianEstimation==0)
doBayesianEstimation = false;
else
error('method_of_moments: Estimation must be either fully Frequentist or fully Bayesian. Maybe you forgot to specify a prior distribution!');
error('method_of_moments: Estimation must be either fully Frequentist or fully Bayesian. Maybe you forgot to specify a prior distribution!')
end
if ~isfield(options_,'varobs')
error('method_of_moments: VAROBS statement is missing!');
error('method_of_moments: VAROBS statement is missing!')
end
check_varobs_are_endo_and_declared_once(options_.varobs,M_.endo_names);
@ -162,12 +166,28 @@ check_varobs_are_endo_and_declared_once(options_.varobs,M_.endo_names);
% The idea is to be independent of options_ and have full control of the
% estimation instead of possibly having to deal with options chosen somewhere
% else in the mod file.
options_mom_ = mom.default_option_mom_values(options_mom_, options_, M_.dname, do_bayesian_estimation);
options_mom_ = mom.default_option_mom_values(options_mom_, options_, M_.dname, doBayesianEstimation);
% -------------------------------------------------------------------------
% workarounds
% -------------------------------------------------------------------------
% The TeX option crashes MATLAB R2014a run with "-nodisplay" option
% (as is done from the testsuite).
% Since we can’t directly test whether "-nodisplay" has been passed,
% we test for the "source_root" environment variable, which is set
% by the testsuite.
% Note that it was not tested whether the crash happens with more
% recent MATLAB versions, so when OLD_MATLAB_VERSION is increased,
% one should make a test before removing this workaround.
if options_.TeX && ~isoctave && matlab_ver_less_than('8.4') && ~isempty(getenv('source_root'))
warning('Disabling TeX option due to a bug in MATLAB R2014a with -nodisplay')
options_.TeX = false;
end
if isfield(options_mom_, 'TeX') && options_mom_.TeX && ~isoctave && matlab_ver_less_than('8.4') && ~isempty(getenv('source_root'))
warning('Disabling TeX option due to a bug in MATLAB R2014a with -nodisplay')
options_mom_.TeX = false;
end
if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
% temporary workaround for https://git.dynare.org/Dynare/dseries/-/issues/51
if options_mom_.xls_sheet~=1
@ -179,27 +199,60 @@ if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_meth
end
% -------------------------------------------------------------------------
% checks on settings
% -------------------------------------------------------------------------
if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
if numel(options_mom_.nobs) > 1
error('method_of_moments: Recursive estimation and forecast for samples is not supported. Please set an integer as ''nobs''!');
end
if numel(options_mom_.first_obs) > 1
error('method_of_moments: Recursive estimation and forecast for samples is not supported. Please set an integer as ''first_obs''!');
end
end
if options_mom_.order < 1
error('method_of_moments: The order of the Taylor approximation cannot be 0!')
end
if options_mom_.order > 2
fprintf('Dynare will use ''k_order_solver'' as the order>2\n');
options_mom_.k_order_solver = true;
end
if strcmp(options_mom_.mom.mom_method,'SMM')
if options_mom_.mom.simulation_multiple < 1
fprintf('The simulation horizon is shorter than the data. Dynare resets the simulation_multiple to 7.\n')
options_mom_.mom.simulation_multiple = 7;
end
end
if strcmp(options_mom_.mom.mom_method,'GMM')
% require pruning with GMM at higher order
if options_mom_.order > 1 && ~options_mom_.pruning
fprintf('GMM at higher order only works with pruning, so we set pruning option to 1.\n');
options_mom_.pruning = true;
end
if options_mom_.order > 3
error('method_of_moments: Perturbation orders higher than 3 are not implemented for GMM estimation, try using SMM!');
end
end
if options_mom_.mom.analytic_jacobian && ~strcmp(options_mom_.mom.mom_method,'GMM')
options_mom_.mom.analytic_jacobian = false;
fprintf('\n''analytic_jacobian'' option will be dismissed as it only works with GMM.\n');
end
% -------------------------------------------------------------------------
% initializations
% -------------------------------------------------------------------------
% create output directories to store results
M_.dname = options_mom_.dirname;
CheckPath(M_.dname,'.');
CheckPath('method_of_moments',M_.dname);
CheckPath('graphs',M_.dname);
if do_bayesian_estimation
oo_.mom.posterior.optimization.mode = [];
oo_.mom.posterior.optimization.Variance = [];
oo_.mom.posterior.optimization.log_density=[];
end
do_bayesian_estimation_mcmc = do_bayesian_estimation && ( (options_mom_.mh_replic>0) || options_mom_.load_mh_file );
invhess = [];
CheckPath('graphs',options_mom_.dirname);
% initialize options that might change
options_mom_.mom.compute_derivs = false; % flag to compute derivs in objective function (might change for GMM with either analytic_standard_errors or analytic_jacobian (dependent on optimizer))
options_mom_.mom.vector_output = false; % specifies whether the objective function returns a vector
% decision rule
oo_.dr = set_state_space(oo_.dr,M_); % get state-space representation
options_mom_.mom.obs_var = []; % create index of observed variables in DR order
oo_.mom.obs_var = []; % create index of observed variables in DR order
for i = 1:options_mom_.obs_nbr
options_mom_.mom.obs_var = [options_mom_.mom.obs_var; find(strcmp(options_mom_.varobs{i}, M_.endo_names(oo_.dr.order_var)))];
oo_.mom.obs_var = [oo_.mom.obs_var; find(strcmp(options_mom_.varobs{i}, M_.endo_names(oo_.dr.order_var)))];
end
@ -218,76 +271,37 @@ if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_meth
% Get maximum lag number for autocovariances/autocorrelations
options_mom_.ar = max(cellfun(@max,M_.matched_moments(:,2))) - min(cellfun(@min,M_.matched_moments(:,2)));
% Check that only observed variables are involved in moments
not_observed_variables=setdiff(oo_.dr.inv_order_var([M_.matched_moments{:,1}]),options_mom_.mom.obs_var);
not_observed_variables=setdiff(oo_.dr.inv_order_var([M_.matched_moments{:,1}]),oo_.mom.obs_var);
if ~isempty(not_observed_variables)
skipline;
error('method_of_moments: You specified moments involving %s, but it is not a varobs!',M_.endo_names{oo_.dr.order_var(not_observed_variables)});
error('method_of_moments: You specified moments involving %s, but it is not a varobs!',M_.endo_names{oo_.dr.order_var(not_observed_variables)})
end
end
% -------------------------------------------------------------------------
% matched_irfs: checks and transformations
% -------------------------------------------------------------------------
if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
[oo_.mom.data_moments, oo_.mom.weighting_info.W, options_mom_.mom.irfIndex, options_mom_.irf] = mom.matched_irfs_blocks(M_.matched_irfs, M_.matched_irfs_weights, options_mom_.varobs_id, options_mom_.obs_nbr, M_.exo_nbr, M_.endo_names, M_.exo_names);
% compute inverse of weighting matrix
try
oo_.mom.weighting_info.Winv = inv(oo_.mom.weighting_info.W);
catch
error('method_of_moments: Something wrong while computing inv(W), check your weighting matrix!');
end
if any(isnan(oo_.mom.weighting_info.Winv(:))) || any(isinf(oo_.mom.weighting_info.Winv(:)))
error('method_of_moments: There are NaN or Inf values in inv(W), check your weighting matrix!');
end
% compute log determinant of inverse of weighting matrix in a robust way to avoid Inf or NaN
try
oo_.mom.weighting_info.Winv_logdet = 2*sum(log(diag(chol(oo_.mom.weighting_info.Winv))));
catch
error('method_of_moments: Something wrong while computing log(det(inv(W))), check your weighting matrix!');
end
if any(isnan(oo_.mom.weighting_info.Winv_logdet(:))) || any(isinf(oo_.mom.weighting_info.Winv_logdet(:)))
error('method_of_moments: There are NaN or Inf values in log(det(inv(W))), check your weighting matrix!');
end
options_mom_.mom.mom_nbr = length(options_mom_.mom.irfIndex);
end
% -------------------------------------------------------------------------
% irf_matching_file: checks and transformations
% -------------------------------------------------------------------------
if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
[options_mom_.mom.irf_matching_file.name, options_mom_.mom.irf_matching_file.path] = mom.check_irf_matching_file(options_mom_.mom.irf_matching_file.name);
% check for irf_matching_file
if ~( isempty(options_mom_.mom.irf_matching_file.path) || strcmp(options_mom_.mom.irf_matching_file.path,'.') )
fprintf('\nAdding %s to MATLAB''s path.\n',options_mom_.mom.irf_matching_file.path);
addpath(options_mom_.mom.irf_matching_file.path);
end
end
% -------------------------------------------------------------------------
% estimated parameters: checks and transformations on values, priors, bounds, posterior options
% estimated parameters: checks and transformations on values, priors, bounds
% -------------------------------------------------------------------------
% set priors and bounds over the estimated parameters
[xparam0, estim_params_, bayestopt_, lb, ub, M_] = set_prior(estim_params_, M_, options_mom_);
number_of_estimated_parameters = length(xparam0);
hessian_xparam0 = []; % initialize hessian
% check if enough moments for estimation
if options_mom_.mom.mom_nbr < length(xparam0)
skipline;
error('method_of_moments: There must be at least as many moments as parameters for a %s estimation!',options_mom_.mom.mom_method);
if strcmp(options_mom_.mom.mom_method, 'GMM') || strcmp(options_mom_.mom.mom_method, 'SMM')
if options_mom_.mom.mom_nbr < length(xparam0)
skipline;
error('method_of_moments: There must be at least as many moments as parameters for a %s estimation!',options_mom_.mom.mom_method);
end
skipline(2);
end
skipline(2);
% check if a _prior_restrictions.m file exists
if exist([M_.fname '_prior_restrictions.m'],'file')
options_mom_.prior_restrictions.status = 1;
options_mom_.prior_restrictions.routine = str2func([M_.fname '_prior_restrictions']);
end
% check that the provided mode_file is compatible with the current estimation settings
if ~isempty(options_mom_.mode_file) && ( ~do_bayesian_estimation || (do_bayesian_estimation && ~options_mom_.mh_posterior_mode_estimation) )
[xparam0, hessian_xparam0] = check_mode_file(xparam0, hessian_xparam0, options_mom_, bayestopt_);
end
% check on specified priors and penalized estimation (which uses Laplace approximated priors)
if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
bayestopt_orig = bayestopt_;
@ -300,6 +314,7 @@ if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_meth
bayestopt_ = mom.transform_prior_to_laplace_prior(bayestopt_);
end
end
% check for calibrated covariances before updating parameters
estim_params_ = check_for_calibrated_covariances(estim_params_,M_);
@ -311,104 +326,89 @@ else
estim_params_.full_calibration_detected = false;
end
if options_mom_.use_calibration_initialization % set calibration as starting values
if ~isempty(bayestopt_) && ~do_bayesian_estimation && any(all(isnan([xparam_calib xparam0]),2))
if ~isempty(bayestopt_) && ~doBayesianEstimation && any(all(isnan([xparam_calib xparam0]),2))
error('method_of_moments: When using the use_calibration option with %s without prior, the parameters must be explicitly initialized!',options_mom_.mom.mom_method);
else
[xparam0,estim_params_] = do_parameter_initialization(estim_params_,xparam_calib,xparam0); % get explicitly initialized parameters that have precedence over calibrated values
end
end
% check initialization
if ~isempty(bayestopt_) && ~do_bayesian_estimation && any(isnan(xparam0))
if ~isempty(bayestopt_) && ~doBayesianEstimation && any(isnan(xparam0))
error('method_of_moments: Frequentist %s requires all estimated parameters to be initialized, either in an estimated_params or estimated_params_init-block!',options_mom_.mom.mom_method);
end
% set and check parameter bounds
if ~isempty(bayestopt_) && do_bayesian_estimation
if ~isempty(bayestopt_) && doBayesianEstimation
% plot prior densities
if ~options_mom_.nograph && options_mom_.plot_priors
if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
plot_priors(bayestopt_orig,M_,estim_params_,options_mom_,'Original priors'); % only for visual inspection (not saved to disk, because overwritten in next call to plot_priors)
plot_priors(bayestopt_,M_,estim_params_,options_mom_,'Laplace approximated priors');
clear('bayestopt_orig'); % make sure stale structure cannot be used
else
plot_priors(bayestopt_,M_,estim_params_,options_mom_,'Priors');
end
end
% set prior bounds
BoundsInfo = prior_bounds(bayestopt_, options_mom_.prior_trunc);
BoundsInfo.lb = max(BoundsInfo.lb,lb);
BoundsInfo.ub = min(BoundsInfo.ub,ub);
Bounds = prior_bounds(bayestopt_, options_mom_.prior_trunc);
Bounds.lb = max(Bounds.lb,lb);
Bounds.ub = min(Bounds.ub,ub);
else
% no priors are declared so Dynare will estimate the parameters with Frequentist methods using inequality constraints for the parameters
BoundsInfo.lb = lb;
BoundsInfo.ub = ub;
if (strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')) && options_mom_.mom.penalized_estimator
fprintf('Penalized estimation turned off as you did not declare priors\n');
% no priors are declared so Dynare will estimate the parameters with
% Frequentist methods using inequality constraints for the parameters
Bounds.lb = lb;
Bounds.ub = ub;
if options_mom_.mom.penalized_estimator
fprintf('Penalized estimation turned off as you did not declare priors\n')
options_mom_.mom.penalized_estimator = 0;
else
if isfield(options_mom_,'mh_replic') && options_mom_.mh_replic > 0
fprintf('Setting ''mh_replic=0'' as you did not declare priors.\n');
options_mom_.mh_replic = 0;
end
end
end
% set correct bounds for standard deviations and correlations
BoundsInfo = mom.set_correct_bounds_for_stderr_corr(estim_params_,BoundsInfo);
Bounds = mom.set_correct_bounds_for_stderr_corr(estim_params_,Bounds);
% test if initial values of the estimated parameters are all between the prior lower and upper bounds
if options_mom_.use_calibration_initialization
try
check_prior_bounds(xparam0,BoundsInfo,M_,estim_params_,options_mom_,bayestopt_);
check_prior_bounds(xparam0,Bounds,M_,estim_params_,options_mom_,bayestopt_);
catch last_error
fprintf('Cannot use parameter values from calibration as they violate the prior bounds.');
fprintf('Cannot use parameter values from calibration as they violate the prior bounds.')
rethrow(last_error);
end
else
check_prior_bounds(xparam0,BoundsInfo,M_,estim_params_,options_mom_,bayestopt_);
check_prior_bounds(xparam0,Bounds,M_,estim_params_,options_mom_,bayestopt_);
end
% check for positive definiteness
estim_params_ = get_matrix_entries_for_psd_check(M_,estim_params_);
% set sigma_e_is_diagonal flag (needed if the shocks block is not declared in the mod file)
M_.sigma_e_is_diagonal = true;
if estim_params_.ncx || any(nnz(tril(M_.Correlation_matrix,-1))) || isfield(estim_params_,'calibrated_covariances')
M_.sigma_e_is_diagonal = false;
end
% storing prior parameters in results structure
if do_bayesian_estimation || ( (strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')) && options_mom_.mom.penalized_estimator)
oo_.mom.prior.mean = bayestopt_.p1;
oo_.mom.prior.mode = bayestopt_.p5;
oo_.mom.prior.variance = diag(bayestopt_.p2.^2);
oo_.mom.prior.hyperparameters.first = bayestopt_.p6;
oo_.mom.prior.hyperparameters.second = bayestopt_.p7;
end
% storing prior parameters in results
oo_.mom.prior.mean = bayestopt_.p1;
oo_.mom.prior.mode = bayestopt_.p5;
oo_.mom.prior.variance = diag(bayestopt_.p2.^2);
oo_.mom.prior.hyperparameters.first = bayestopt_.p6;
oo_.mom.prior.hyperparameters.second = bayestopt_.p7;
% set all parameters
M_ = set_all_parameters(xparam0,estim_params_,M_);
% provide warning if there is NaN in parameters
test_for_deep_parameters_calibration(M_);
% set jscale
if do_bayesian_estimation_mcmc
if ~strcmp(options_mom_.posterior_sampler_options.posterior_sampling_method,'slice')
if isempty(options_mom_.mh_jscale)
options_mom_.mh_jscale = 2.38/sqrt(number_of_estimated_parameters); % use optimal value for univariate normal distribution (check_posterior_sampler_options and mode_compute=6 may overwrite this setting)
end
bayestopt_.jscale(find(isnan(bayestopt_.jscale))) = options_mom_.mh_jscale;
end
end
% initialization of posterior sampler options
if do_bayesian_estimation_mcmc
[current_options, options_mom_, bayestopt_] = check_posterior_sampler_options([], M_.fname, M_.dname, options_mom_, BoundsInfo, bayestopt_);
options_mom_.posterior_sampler_options.current_options = current_options;
if strcmp(current_options.posterior_sampling_method,'slice') && current_options.use_mh_covariance_matrix && ~current_options.rotated
error('method_of_moments: Using the slice sampler with the ''use_mh_covariance_matrix'' option requires also setting the ''rotated'' option!');
end
end
% warning if prior allows that stderr parameters are negative or corr parameters are outside the unit circle
if do_bayesian_estimation
if doBayesianEstimation
% warning if prior allows that stderr parameters are negative or corr parameters are outside the unit circle
check_prior_stderr_corr(estim_params_,bayestopt_);
% check value of prior density
[~,~,~,info] = priordens(xparam0,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
[~,~,~,info]= priordens(xparam0,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
if any(info)
fprintf('The prior density evaluated at the initial values is Inf for the following parameters: %s\n',bayestopt_.name{info,1});
error('The initial value of the prior is -Inf!');
fprintf('The prior density evaluated at the initial values is Inf for the following parameters: %s\n',bayestopt_.name{info,1})
error('The initial value of the prior is -Inf!')
end
end
@ -416,34 +416,34 @@ end
% -------------------------------------------------------------------------
% datafile: checks and transformations
% -------------------------------------------------------------------------
% build dataset
% Build dataset
if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
% check if datafile has same name as mod file
[~,name] = fileparts(options_mom_.datafile);
% Check if datafile has same name as mod file
[~,name,~] = fileparts(options_mom_.datafile);
if strcmp(name,M_.fname)
error('method_of_moments: ''datafile'' and mod file are not allowed to have the same name; change the name of the ''datafile''!');
error('method_of_moments: ''datafile'' and mod file are not allowed to have the same name; change the name of the ''datafile''!')
end
dataset_ = makedataset(options_mom_);
% set options for old interface from the ones for new interface
if ~isempty(dataset_)
options_mom_.nobs = dataset_.nobs;
end
% check length of data for estimation of second moments
% Check length of data for estimation of second moments
if options_mom_.ar > options_mom_.nobs+1
error('method_of_moments: Dataset is too short to compute higher than first moments!');
end
% provide info on data moments handling
% Provide info on data moments handling
fprintf('Computing data moments. Note that NaN values in the moments (due to leads and lags or missing data) are replaced by the mean of the corresponding moment.\n');
% get data moments for the method of moments
[oo_.mom.data_moments, oo_.mom.m_data] = mom.get_data_moments(dataset_.data, options_mom_.mom.obs_var, oo_.dr.inv_order_var, M_.matched_moments, options_mom_);
% Get data moments for the method of moments
[oo_.mom.data_moments, oo_.mom.m_data] = mom.get_data_moments(dataset_.data, oo_.mom.obs_var, oo_.dr.inv_order_var, M_.matched_moments, options_mom_);
if ~isreal(dataset_.data)
error('method_of_moments: The data moments contain complex values!');
error('method_of_moments: The data moments contain complex values!')
end
end
% -------------------------------------------------------------------------
% SMM: Get shock series and set variance correction factor
% SMM: Get shock series fand set variance correction factor
% -------------------------------------------------------------------------
if strcmp(options_mom_.mom.mom_method,'SMM')
options_mom_.mom.long = round(options_mom_.mom.simulation_multiple*options_mom_.nobs);
@ -477,6 +477,7 @@ end
% -------------------------------------------------------------------------
% checks for steady state at initial parameters
% -------------------------------------------------------------------------
% check if steady state solves static model and if steady-state changes estimated parameters
if options_mom_.steadystate.nocheck
steadystate_check_flag_vec = [0 1];
@ -485,7 +486,7 @@ else
end
[oo_.steady_state, info, steady_state_changes_parameters] = check_steady_state_changes_parameters(M_, estim_params_, oo_, options_mom_, steadystate_check_flag_vec);
if info(1)
fprintf('\nThe steady state at the initial parameters cannot be computed.\n');
fprintf('\nThe steady state at the initial parameters cannot be computed.\n')
print_info(info, 0, options_mom_);
end
if steady_state_changes_parameters && strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_standard_errors
@ -493,6 +494,7 @@ if steady_state_changes_parameters && strcmp(options_mom_.mom.mom_method,'GMM')
fprintf('because the steady-state changes estimated parameters. Option ''analytic_derivation_mode'' reset to -2.');
options_mom_.analytic_derivation_mode = -2;
end
% display warning if some parameters are still NaN
test_for_deep_parameters_calibration(M_);
@ -502,25 +504,17 @@ test_for_deep_parameters_calibration(M_);
% -------------------------------------------------------------------------
objective_function = str2func('mom.objective_function');
try
% check for NaN or complex values of moment-distance-funtion evaluated at initial parameters
% Check for NaN or complex values of moment-distance-funtion evaluated at initial parameters
if strcmp(options_mom_.mom.mom_method,'SMM') || strcmp(options_mom_.mom.mom_method,'GMM')
oo_.mom.weighting_info.Sw = eye(options_mom_.mom.mom_nbr); % initialize with identity weighting matrix
oo_.mom.Sw = eye(options_mom_.mom.mom_nbr); % initialize with identity weighting matrix
end
tic_id = tic;
[fval, info] = feval(objective_function, xparam0, oo_.mom.data_moments, oo_.mom.weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
[fval, info, ~, ~, ~, oo_, M_] = feval(objective_function, xparam0, Bounds, oo_, estim_params_, M_, options_mom_);
elapsed_time = toc(tic_id);
if isnan(fval)
if strcmp(options_mom_.mom.mom_method,'SMM') || strcmp(options_mom_.mom.mom_method,'GMM')
error('method_of_moments: The initial value of the objective function with identity weighting matrix is NaN!');
else
error('method_of_moments: The initial value of the objective function is NaN!');
end
error('method_of_moments: The initial value of the objective function with identity weighting matrix is NaN!')
elseif imag(fval)
if strcmp(options_mom_.mom.mom_method,'SMM') || strcmp(options_mom_.mom.mom_method,'GMM')
error('method_of_moments: The initial value of the objective function with identity weighting matrix is complex!');
else
error('method_of_moments: The initial value of the objective function is complex!');
end
error('method_of_moments: The initial value of the objective function with identity weighting matrix is complex!')
end
if info(1) > 0
disp('method_of_moments: Error in computing the objective function for initial parameter values')
@ -535,10 +529,10 @@ try
catch last_error % if check fails, provide info on using calibration if present
if estim_params_.full_calibration_detected %calibrated model present and no explicit starting values
skipline(1);
fprintf('There was an error in computing the moments for initial parameter values.\n');
fprintf('If this is not a problem with the setting of options (check the error message below),\n');
fprintf('you should try using the calibrated version of the model as starting values. To do\n');
fprintf('this, add an empty estimated_params_init-block with use_calibration option immediately before the estimation\n');
fprintf('There was an error in computing the moments for initial parameter values.\n')
fprintf('If this is not a problem with the setting of options (check the error message below),\n')
fprintf('you should try using the calibrated version of the model as starting values. To do\n')
fprintf('this, add an empty estimated_params_init-block with use_calibration option immediately before the estimation\n')
fprintf('command (and after the estimated_params-block so that it does not get overwritten):\n');
skipline(2);
end
@ -549,281 +543,60 @@ end
% -------------------------------------------------------------------------
% print some info to console
% -------------------------------------------------------------------------
mom.print_info_on_estimation_settings(options_mom_, number_of_estimated_parameters, do_bayesian_estimation);
mom.print_info_on_estimation_settings(options_mom_, number_of_estimated_parameters);
% -------------------------------------------------------------------------
% compute mode for GMM/SMM
% -------------------------------------------------------------------------
if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
[xparam1, oo_.mom.weighting_info, oo_.mom.verbose] = mom.mode_compute_gmm_smm(xparam0, objective_function, oo_.mom.m_data, oo_.mom.data_moments, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
end
% -------------------------------------------------------------------------
% compute mode for IRF matching
% -------------------------------------------------------------------------
if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
if ~do_bayesian_estimation || (do_bayesian_estimation && ~options_mom_.mh_posterior_mode_estimation)
[xparam1, hessian_xparam1, fval, oo_.mom.verbose] = mom.mode_compute_irf_matching(xparam0, hessian_xparam0, objective_function, do_bayesian_estimation, oo_.mom.weighting_info, oo_.mom.data_moments, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
else
xparam1 = xparam0;
hessian_xparam1 = hessian_xparam0;
end
end
% -------------------------------------------------------------------------
% compute standard errors and initialize covariance of the proposal distribution
% GMM/SMM: iterated estimation
% -------------------------------------------------------------------------
if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_method,'SMM')
% compute mode
[xparam1, oo_, Woptflag] = mom.mode_compute_gmm_smm(xparam0, objective_function, oo_, M_, options_mom_, estim_params_, bayestopt_, Bounds);
% compute standard errors at mode
options_mom_.mom.vector_output = false; % make sure flag is reset
M_ = set_all_parameters(xparam1,estim_params_,M_); % update M_ and oo_ (in particular to get oo_.mom.model_moments)
if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_standard_errors
options_mom_.mom.compute_derivs = true; % for GMM we compute derivatives analytically in the objective function with this flag
end
[~, ~, ~, ~, ~, oo_.mom.Q, oo_.mom.model_moments, oo_.mom.model_moments_params_derivs] = feval(objective_function, xparam1, oo_.mom.data_moments, oo_.mom.weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
[~, ~, ~,~,~, oo_] = feval(objective_function, xparam1, Bounds, oo_, estim_params_, M_, options_mom_); % compute model moments and oo_.mom.model_moments_params_derivs
options_mom_.mom.compute_derivs = false; % reset to not compute derivatives in objective function during optimization
[stdh, invhess] = mom.standard_errors(xparam1, objective_function, oo_.mom.model_moments, oo_.mom.model_moments_params_derivs, oo_.mom.m_data, oo_.mom.data_moments, oo_.mom.weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
if options_mom_.cova_compute
hessian_xparam1 = inv(invhess);
end
else
if ~do_bayesian_estimation || ~options_mom_.mh_posterior_mode_estimation
if do_bayesian_estimation
oo_.mom.posterior.optimization.mode = xparam1;
if exist('fval','var')
oo_.mom.posterior.optimization.log_density = -fval;
end
end
if options_mom_.cova_compute
hsd = sqrt(diag(hessian_xparam1)); % represent the curvature (or second derivatives) of the likelihood with respect to each parameter being estimated.
invhess = inv(hessian_xparam1./(hsd*hsd'))./(hsd*hsd'); % before taking the inverse scale the Hessian matrix by dividing each of its elements by the outer product of hsd such that the diagonal of the resulting matrix is approximately 1. This kind of scaling can help in regularizing the matrix and potentially improves its condition number, which in turn can make the matrix inversion more stable.
stdh = sqrt(diag(invhess));
if do_bayesian_estimation
oo_.mom.posterior.optimization.Variance = invhess;
end
end
else
variances = bayestopt_.p2.*bayestopt_.p2;
id_Inf = isinf(variances);
variances(id_Inf) = 1;
invhess = options_mom_.mh_posterior_mode_estimation*diag(variances);
xparam1 = bayestopt_.p5;
id_NaN = isnan(xparam1);
xparam1(id_NaN) = bayestopt_.p1(id_NaN);
outside_bound_pars=find(xparam1 < BoundsInfo.lb | xparam1 > BoundsInfo.ub);
xparam1(outside_bound_pars) = bayestopt_.p1(outside_bound_pars);
end
if ~options_mom_.cova_compute
stdh = NaN(length(xparam1),1);
end
end
% -------------------------------------------------------------------------
% display estimation results at mode
% -------------------------------------------------------------------------
if do_bayesian_estimation && ~options_mom_.mom.penalized_estimator && ~options_mom_.mh_posterior_mode_estimation
% display table with Bayesian mode estimation results and store parameter estimates and standard errors in oo_
oo_.mom = display_estimation_results_table(xparam1, stdh, M_, options_mom_, estim_params_, bayestopt_, oo_.mom, prior_dist_names, 'Posterior', 'posterior');
% Laplace approximation to the marginal log density
if options_mom_.cova_compute
estim_params_nbr = size(xparam1,1);
if ispd(invhess)
log_det_invhess = log(det(invhess./(stdh*stdh')))+2*sum(log(stdh));
likelihood = feval(objective_function, xparam1, oo_.mom.data_moments, oo_.mom.weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
oo_.mom.MarginalDensity.LaplaceApproximation = .5*estim_params_nbr*log(2*pi) + .5*log_det_invhess - likelihood;
else
oo_.mom.MarginalDensity.LaplaceApproximation = NaN;
end
fprintf('\nLog data density [Laplace approximation] is %f.\n',oo_.mom.MarginalDensity.LaplaceApproximation);
end
elseif ~do_bayesian_estimation || (do_bayesian_estimation && options_mom_.mom.penalized_estimator)
% display table with Frequentist estimation results and store parameter estimates and standard errors in oo_
oo_.mom = display_estimation_results_table(xparam1, stdh, M_, options_mom_, estim_params_, bayestopt_, oo_.mom, prior_dist_names, options_mom_.mom.mom_method, lower(options_mom_.mom.mom_method));
[stdh,hessian_xparam1] = mom.standard_errors(xparam1, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Woptflag);
end
% -------------------------------------------------------------------------
% checks for mode and hessian at the mode
% -------------------------------------------------------------------------
if (~do_bayesian_estimation && options_mom_.cova_compute) || (do_bayesian_estimation && ~options_mom_.mh_posterior_mode_estimation && options_mom_.cova_compute)
check_hessian_at_the_mode(hessian_xparam1, xparam1, M_, estim_params_, options_, BoundsInfo);
end
if options_mom_.mode_check.status
if ~do_bayesian_estimation || (do_bayesian_estimation && ~options_mom_.mh_posterior_mode_estimation)
mode_check(objective_function, xparam1, diag(stdh), options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, true,... % use diag(stdh) instead of hessian_xparam1 as mode_check uses diagonal elements
oo_.mom.data_moments, oo_.mom.weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
end
end
% -------------------------------------------------------------------------
% Bayesian MCMC estimation
% -------------------------------------------------------------------------
if do_bayesian_estimation_mcmc
invhess = set_mcmc_jumping_covariance(invhess, length(xparam1), options_mom_.MCMC_jumping_covariance, bayestopt_, 'method_of_moments');
% reset bounds as lb and ub must only be operational during mode-finding
BoundsInfo = set_mcmc_prior_bounds(xparam1, bayestopt_, options_mom_, 'method_of_moments');
% tunes the jumping distribution's scale parameter
if isfield(options_mom_,'mh_tune_jscale') && options_mom_.mh_tune_jscale.status
if strcmp(options_mom_.posterior_sampler_options.posterior_sampling_method, 'random_walk_metropolis_hastings')
options_mom_.mh_jscale = tune_mcmc_mh_jscale_wrapper(invhess, options_mom_, M_, objective_function, xparam1, BoundsInfo,...
oo_.mom.data_moments, oo_.mom.weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
bayestopt_.jscale(:) = options_mom_.mh_jscale;
fprintf('mh_tune_jscale: mh_jscale has been set equal to %s.\n', num2str(options_mom_.mh_jscale));
else
warning('mh_tune_jscale is only available with ''random_walk_metropolis_hastings''!')
end
end
% run MCMC sampling
posterior_sampler_options = options_mom_.posterior_sampler_options.current_options;
posterior_sampler_options.invhess = invhess;
[posterior_sampler_options, options_mom_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, M_.fname, M_.dname, options_mom_, BoundsInfo, bayestopt_,'method_of_moments');
options_mom_.posterior_sampler_options.current_options = posterior_sampler_options; % store current options
if options_mom_.mh_replic>0
posterior_sampler(objective_function,posterior_sampler_options.proposal_distribution,xparam1,posterior_sampler_options,BoundsInfo,oo_.mom.data_moments,oo_.mom.weighting_info,options_mom_,M_,estim_params_,bayestopt_,oo_,'method_of_moments::mcmc');
end
CutSample(M_, options_mom_, 'method_of_moments::mcmc'); % discard first mh_drop percent of the draws
if options_mom_.mh_posterior_mode_estimation
% skip optimizer-based mode-finding and instead compute the mode based on a run of a MCMC
[~,~,posterior_mode,~] = compute_mh_covariance_matrix(bayestopt_,M_.fname,M_.dname,'method_of_moments');
oo_.mom = fill_mh_mode(posterior_mode',NaN(length(posterior_mode),1),M_,options_mom_,estim_params_,bayestopt_,oo_.mom,'posterior');
return
else
% get stored results if required
if options_mom_.load_mh_file && options_mom_.load_results_after_load_mh
oo_load_mh = load([M_.dname filesep 'method_of_moments' filesep M_.fname '_mom_results'],'oo_');
end
% convergence diagnostics
if ~options_mom_.nodiagnostic
if (options_mom_.mh_replic>0 || (options_mom_.load_mh_file && ~options_mom_.load_results_after_load_mh))
oo_.mom = mcmc_diagnostics(options_mom_, estim_params_, M_, oo_.mom);
elseif options_mom_.load_mh_file && options_mom_.load_results_after_load_mh
if isfield(oo_load_mh.oo_.mom,'convergence')
oo_.mom.convergence = oo_load_mh.oo_.mom.convergence;
end
end
end
% statistics and plots for posterior draws
if options_mom_.mh_replic || (options_mom_.load_mh_file && ~options_mom_.load_results_after_load_mh)
[~,oo_.mom] = marginal_density(M_, options_mom_, estim_params_, oo_.mom, bayestopt_, 'method_of_moments');
oo_.mom = GetPosteriorParametersStatistics(estim_params_, M_, options_mom_, bayestopt_, oo_.mom, prior_dist_names);
if ~options_mom_.nograph
oo_.mom = PlotPosteriorDistributions(estim_params_, M_, options_mom_, bayestopt_, oo_.mom);
end
[oo_.mom.posterior.metropolis.mean,oo_.mom.posterior.metropolis.Variance] = GetPosteriorMeanVariance(options_mom_,M_);
elseif options_mom_.load_mh_file && options_mom_.load_results_after_load_mh
% load fields from previous MCMC run stored in results-file
field_names={'posterior_mode','posterior_std_at_mode',...% fields set by marginal_density
'posterior_mean','posterior_hpdinf','posterior_hpdsup','posterior_median','posterior_variance','posterior_std','posterior_deciles','posterior_density',...% fields set by GetPosteriorParametersStatistics
'prior_density',...% fields set by PlotPosteriorDistributions
};
for field_iter=1:size(field_names,2)
if isfield(oo_load_mh.oo_.mom,field_names{1,field_iter})
oo_.mom.(field_names{1,field_iter}) = oo_load_mh.oo_.mom.(field_names{1,field_iter});
end
end
if isfield(oo_load_mh.oo_.mom,'MarginalDensity') && isfield(oo_load_mh.oo_.mom.MarginalDensity,'ModifiedHarmonicMean') % field set by marginal_density
oo_.mom.MarginalDensity.ModifiedHarmonicMean = oo_load_mh.oo_.mom.MarginalDensity.ModifiedHarmonicMean;
end
if isfield(oo_load_mh.oo_.mom,'posterior') && isfield(oo_load_mh.oo_.mom.posterior,'metropolis') % field set by GetPosteriorMeanVariance
oo_.mom.posterior.metropolis = oo_load_mh.oo_.mom.posterior.metropolis;
end
end
[error_flag,~,options_mom_]= metropolis_draw(1,options_mom_,estim_params_,M_);
if ~(~isempty(options_mom_.sub_draws) && options_mom_.sub_draws==0)
% THIS IS PROBABLY NOT USEFUL HERE AND CAN BE REMOVED (PREPROCESSOR: REMOVE bayesian_irf, moments_varendo)
%if options_mom_.bayesian_irf
% if error_flag
% error('method_of_moments: Cannot compute the posterior IRFs!');
% end
% PosteriorIRF('posterior','method_of_moments::mcmc');
%end
% if options_mom_.moments_varendo
% if error_flag
% error('method_of_moments: Cannot compute the posterior moments for the endogenous variables!');
% end
% if options_mom_.load_mh_file && options_mom_.mh_replic==0 %user wants to recompute results
% [MetropolisFolder, info] = CheckPath('metropolis',options_mom_.dirname);
% if ~info
% generic_post_data_file_name={'Posterior2ndOrderMoments','decomposition','PosteriorVarianceDecomposition','correlation','PosteriorCorrelations','conditional decomposition','PosteriorConditionalVarianceDecomposition'};
% for ii=1:length(generic_post_data_file_name)
% delete_stale_file([MetropolisFolder filesep M_.fname '_' generic_post_data_file_name{1,ii} '*']);
% end
% % 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*' ]));
% if NumberOfDrawsFiles>0
% temp=load([M_.dname '/metropolis/' M_.fname '_posterior_draws1']);
% if ~isfield(temp,'estim_params_')
% for file_iter=1:NumberOfDrawsFiles
% 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_,var_list_);
% end
else
fprintf('''sub_draws'' was set to 0. Skipping posterior computations.');
end
xparam1 = get_posterior_parameters('mean',M_,estim_params_,oo_.mom,options_);
end
% MAYBE USEFUL????
% % Posterior correlations
% extreme_corr_bound = 0.7;
% if ~isnan(extreme_corr_bound)
% tril_para_correlation_matrix=tril(para_correlation_matrix,-1);
% [row_index,col_index]=find(abs(tril_para_correlation_matrix)>extreme_corr_bound);
% extreme_corr_params=cell(length(row_index),3);
% for i=1:length(row_index)
% extreme_corr_params{i,1}=char(parameter_names(row_index(i),:));
% extreme_corr_params{i,2}=char(parameter_names(col_index(i),:));
% extreme_corr_params{i,3}=tril_para_correlation_matrix(row_index(i),col_index(i));
% end
% end
% disp(' ');
% disp(['Correlations of Parameters (at Posterior Mode) > ',num2str(extreme_corr_bound)]);
% disp(extreme_corr_params)
mode_check(objective_function, xparam1, hessian_xparam1, options_mom_, M_, estim_params_, bayestopt_, Bounds, true,...
Bounds, oo_, estim_params_, M_, options_mom_);
end
% -------------------------------------------------------------------------
% display final estimation results
% -------------------------------------------------------------------------
M_ = set_all_parameters(xparam1,estim_params_,M_); % update parameters
[~, ~, ~, ~, ~, oo_.mom.Q, oo_.mom.model_moments, oo_.mom.model_moments_params_derivs, oo_.mom.irf_model_varobs] = objective_function(xparam1, oo_.mom.data_moments, oo_.mom.weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state); % store final results in oo_.mom
if strcmp(options_mom_.mom.mom_method,'SMM') || strcmp(options_mom_.mom.mom_method,'GMM')
% Store results in output structure
oo_.mom = display_estimation_results_table(xparam1,stdh,M_,options_mom_,estim_params_,bayestopt_,oo_.mom,prior_dist_names,options_mom_.mom.mom_method,lower(options_mom_.mom.mom_method));
% J test
oo_.mom.J_test = mom.Jtest(xparam1, objective_function, oo_.mom.Q, oo_.mom.model_moments, oo_.mom.m_data, oo_.mom.data_moments, oo_.mom.weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
elseif strcmp(options_mom_.mom.mom_method,'IRF_MATCHING')
if ~options_mom_.nograph
mom.graph_comparison_irfs(M_.matched_irfs,oo_.mom.irf_model_varobs,options_mom_.varobs_id,options_mom_.irf,options_mom_.relative_irf,M_.endo_names,M_.endo_names_tex,M_.exo_names,M_.exo_names_tex,M_.dname,M_.fname,options_mom_.graph_format,options_mom_.TeX,options_mom_.nodisplay,options_mom_.figures.textwidth)
end
oo_ = mom.Jtest(xparam1, objective_function, Woptflag, oo_, options_mom_, bayestopt_, Bounds, estim_params_, M_, dataset_.nobs);
% display comparison of model moments and data moments
mom.display_comparison_moments(M_, options_mom_, oo_.mom.data_moments, oo_.mom.model_moments);
end
% display comparison of model moments/IRFs and data moments/IRFs
mom.display_comparison_moments_irfs(M_, options_mom_, oo_.mom.data_moments, oo_.mom.model_moments);
% save results to _mom_results.mat
save([M_.dname filesep 'method_of_moments' filesep M_.fname '_mom_results.mat'], 'oo_', 'options_mom_', 'M_', 'estim_params_', 'bayestopt_');
fprintf('\n==== Method of Moments Estimation (%s) Completed ====\n\n',options_mom_.mom.mom_method);
% -------------------------------------------------------------------------
% clean up
% -------------------------------------------------------------------------
warning_config; %reset warning state
fprintf('\n==== Method of Moments Estimation (%s) Completed ====\n\n',options_mom_.mom.mom_method)
%reset warning state
warning_config;
if isoctave && isfield(options_mom_, 'prior_restrictions') && ...
isfield(options_mom_.prior_restrictions, 'routine')
% Octave crashes if it tries to save function handles (to the _results.mat file)
% See https://savannah.gnu.org/bugs/?43215
options_mom_.prior_restrictions.routine = [];
end
if strcmp(options_mom_.mom.mom_method,'SMM') || strcmp(options_mom_.mom.mom_method,'GMM')
if isfield(oo_.mom,'irf_model_varobs') && isempty(oo_.mom.irf_model_varobs)
oo_.mom = rmfield(oo_.mom,'irf_model_varobs'); % remove empty field
end
end
if strcmp(options_mom_.mom.mom_method,'IRF_MATCHING') && ~isempty(options_mom_.mom.irf_matching_file.path) && ~strcmp(options_mom_.mom.irf_matching_file.path,'.')
rmpath(options_mom_.irf_matching_file.path); % remove path to irf_matching_file
end

27
matlab/+mom/set_correct_bounds_for_stderr_corr.m
View File

@ -1,19 +1,18 @@
function BoundsInfo = set_correct_bounds_for_stderr_corr(estim_params_,BoundsInfo)
% BoundsInfo = set_correct_bounds_for_stderr_corr(estim_params_,BoundsInfo)
function Bounds = set_correct_bounds_for_stderr_corr(estim_params_,Bounds)
% function Bounds = set_correct_bounds_for_stderr_corr(estim_params_,Bounds)
% -------------------------------------------------------------------------
% Set correct bounds for standard deviation and corrrelation parameters
% -------------------------------------------------------------------------
% =========================================================================
% INPUTS
% o estim_params_ [struct] information on estimated parameters
% o BoundsInfo [struct] information on bounds
% o Bounds [struct] information on bounds
% -------------------------------------------------------------------------
% OUTPUT
% o BoundsInfo [struct] updated bounds
% o Bounds [struct] updated bounds
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
% -------------------------------------------------------------------------
% =========================================================================
% Copyright © 2023 Dynare Team
%
% This file is part of Dynare.
@ -30,15 +29,15 @@ function BoundsInfo = set_correct_bounds_for_stderr_corr(estim_params_,BoundsInf
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% =========================================================================
number_of_estimated_parameters = estim_params_.nvx+estim_params_.nvn+estim_params_.ncx+estim_params_.ncn+estim_params_.np;
% set correct bounds for standard deviations and corrrelations
param_of_interest = (1:number_of_estimated_parameters)'<=estim_params_.nvx+estim_params_.nvn;
LB_below_0 = (BoundsInfo.lb<0 & param_of_interest);
BoundsInfo.lb(LB_below_0) = 0;
LB_below_0 = (Bounds.lb<0 & param_of_interest);
Bounds.lb(LB_below_0) = 0;
param_of_interest = (1:number_of_estimated_parameters)'> estim_params_.nvx+estim_params_.nvn & (1:number_of_estimated_parameters)'<estim_params_.nvx+estim_params_.nvn +estim_params_.ncx + estim_params_.ncn;
LB_below_minus_1 = (BoundsInfo.lb<-1 & param_of_interest);
UB_above_1 = (BoundsInfo.ub>1 & param_of_interest);
BoundsInfo.lb(LB_below_minus_1) = -1;
BoundsInfo.ub(UB_above_1) = 1;
LB_below_minus_1 = (Bounds.lb<-1 & param_of_interest);
UB_above_1 = (Bounds.ub>1 & param_of_interest);
Bounds.lb(LB_below_minus_1) = -1;
Bounds.ub(UB_above_1) = 1;

114
matlab/+mom/standard_errors.m
View File

@ -1,32 +1,23 @@
function [stderr_values, asympt_cov_mat] = standard_errors(xparam, objective_function, model_moments, model_moments_params_derivs, m_data, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
% [stderr_values, asympt_cov_mat] = standard_errors(xparam, objective_function, model_moments, model_moments_params_derivs, m_data, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
function [SE_values, Asympt_Var] = standard_errors(xparam, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Wopt_flag)
% [SE_values, Asympt_Var] = standard_errors(xparam, objective_function, Bounds, oo_, estim_params_, M_, options_mom_, Wopt_flag)
% -------------------------------------------------------------------------
% This function computes standard errors to the method of moments estimates
% Adapted from replication codes of Andreasen, Fernández-Villaverde, 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.
% -------------------------------------------------------------------------
% Adapted from replication codes of
% o Andreasen, Fernández-Villaverde, 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.
% =========================================================================
% INPUTS
% - xparam: [vector] value of estimated parameters as returned by set_prior()
% - objective_function [func] function handle with string of objective function
% - model_moments: [vector] model moments
% - model_moments_params_derivs: [matrix] analytical jacobian of the model moments wrt estimated parameters (currently for GMM only)
% - m_data [matrix] selected empirical moments at each point in time
% - data_moments: [vector] data with moments/IRFs to match
% - weighting_info: [structure] storing information on weighting matrices
% - options_mom_: [structure] information about all settings (specified by the user, preprocessor, and taken from global options_)
% - M_ [structure] model information
% - estim_params_: [structure] information from estimated_params block
% - bayestopt_: [structure] information on the prior distributions
% - BoundsInfo: [structure] parameter bounds
% - dr: [structure] reduced form model
% - endo_steady_state: [vector] steady state value for endogenous variables (initval)
% - exo_steady_state: [vector] steady state value for exogenous variables (initval)
% - exo_det_steady_state: [vector] steady state value for exogenous deterministic variables (initval)
% o xparam: value of estimated parameters as returned by set_prior()
% o objective_function string of objective function
% o Bounds: structure containing parameter bounds
% o oo_: structure for results
% o estim_params_: structure describing the estimated_parameters
% o M_ structure describing the model
% o options_mom_: structure information about all settings (specified by the user, preprocessor, and taken from global options_)
% o Wopt_flag: indicator whether the optimal weighting is actually used
% -------------------------------------------------------------------------
% OUTPUTS
% o stderr_values [nparam x 1] vector of standard errors
% o asympt_cov_mat [nparam x nparam] asymptotic covariance matrix
% o SE_values [nparam x 1] vector of standard errors
% o Asympt_Var [nparam x nparam] asymptotic covariance matrix
% -------------------------------------------------------------------------
% This function is called by
% o mom.run.m
@ -35,10 +26,9 @@ function [stderr_values, asympt_cov_mat] = standard_errors(xparam, objective_fun
% o get_the_name
% o get_error_message
% o mom.objective_function
% o mom.optimal_weighting_matrix
% -------------------------------------------------------------------------
% Copyright © 2020-2023 Dynare Team
% o mom.optimal_weighting_matrix
% =========================================================================
% Copyright © 2020-2021 Dynare Team
%
% This file is part of Dynare.
%
@ -54,74 +44,84 @@ function [stderr_values, asympt_cov_mat] = standard_errors(xparam, objective_fun
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% -------------------------------------------------------------------------
% Author(s):
% o Willi Mutschler (willi@mutschler.eu)
% o Johannes Pfeifer (johannes.pfeifer@unibw.de)
% =========================================================================
% Some dimensions
num_mom = size(model_moments,1);
num_mom = size(oo_.mom.model_moments,1);
dim_params = size(xparam,1);
D = zeros(num_mom,dim_params);
eps_value = options_mom_.mom.se_tolx;
if strcmp(options_mom_.mom.mom_method,'GMM') && options_mom_.mom.analytic_standard_errors
fprintf('\nComputing standard errors using analytical derivatives of moments\n');
D = model_moments_params_derivs; % already computed in objective function via get_perturbation_params.m
D = oo_.mom.model_moments_params_derivs; %already computed in objective function via get_perturbation_params.m
idx_nan = find(any(isnan(D)));
if any(idx_nan)
for i = idx_nan
fprintf('No standard errors available for parameter %s\n',get_the_name(i,options_mom_.TeX, M_, estim_params_, options_mom_.varobs))
fprintf('No standard errors available for parameter %s\n',get_the_name(i,options_mom_.TeX, M_, estim_params_, options_mom_))
end
warning('There are NaN in the analytical Jacobian of Moments. Check your bounds and/or priors, or use a different optimizer.')
asympt_cov_mat = NaN(length(xparam),length(xparam));
stderr_values = NaN(length(xparam),1);
Asympt_Var = NaN(length(xparam),length(xparam));
SE_values = NaN(length(xparam),1);
return
end
else
fprintf('\nComputing standard errors using numerical derivatives of moments\n');
for i=1:dim_params
% positive step
%Positive step
xparam_eps_p = xparam;
xparam_eps_p(i,1) = xparam_eps_p(i) + eps_value;
[~, info_p, ~, ~, ~, ~, model_moments_p] = feval(objective_function, xparam_eps_p, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
% negative step
xparam_eps_p(i,1) = xparam_eps_p(i) + eps_value;
[~, info_p, ~, ~,~, oo__p] = feval(objective_function, xparam_eps_p, Bounds, oo_, estim_params_, M_, options_mom_);
% Negative step
xparam_eps_m = xparam;
xparam_eps_m(i,1) = xparam_eps_m(i) - eps_value;
[~, info_m, ~, ~, ~, ~, model_moments_m] = feval(objective_function, xparam_eps_m, data_moments, weighting_info, options_mom_, M_, estim_params_, bayestopt_, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
% the Jacobian
xparam_eps_m(i,1) = xparam_eps_m(i) - eps_value;
[~, info_m, ~, ~,~, oo__m] = feval(objective_function, xparam_eps_m, Bounds, oo_, estim_params_, M_, options_mom_);
% The Jacobian:
if nnz(info_p)==0 && nnz(info_m)==0
D(:,i) = (model_moments_p - model_moments_m)/(2*eps_value);
D(:,i) = (oo__p.mom.model_moments - oo__m.mom.model_moments)/(2*eps_value);
else
problematic_parameter = get_the_name(i,options_mom_.TeX, M_, estim_params_, options_mom_.varobs);
problpar = get_the_name(i,options_mom_.TeX, M_, estim_params_, options_mom_);
if info_p(1)==42
warning('method_of_moments:info','Cannot compute the Jacobian using finite differences for parameter %s due to hitting the upper bound - no standard errors available.\n',problematic_parameter)
warning('method_of_moments:info','Cannot compute the Jacobian using finite differences for parameter %s due to hitting the upper bound - no standard errors available.\n',problpar)
else
message_p = get_error_message(info_p, options_mom_);
end
if info_m(1)==41
warning('method_of_moments:info','Cannot compute the Jacobian using finite differences for parameter %s due to hitting the lower bound - no standard errors available.\n',problematic_parameter)
warning('method_of_moments:info','Cannot compute the Jacobian using finite differences for parameter %s due to hitting the lower bound - no standard errors available.\n',problpar)
else
message_m = get_error_message(info_m, options_mom_);
message_m = get_error_message(info_m, options_mom_);
end
if info_m(1)~=41 && info_p(1)~=42
warning('method_of_moments:info','Cannot compute the Jacobian using finite differences for parameter %s - no standard errors available\n %s %s\nCheck your priors or use a different optimizer.\n',problematic_parameter, message_p, message_m)
warning('method_of_moments:info','Cannot compute the Jacobian using finite differences for parameter %s - no standard errors available\n %s %s\nCheck your priors or use a different optimizer.\n',problpar, message_p, message_m)
end
asympt_cov_mat = NaN(length(xparam),length(xparam));
stderr_values = NaN(length(xparam),1);
Asympt_Var = NaN(length(xparam),length(xparam));
SE_values = NaN(length(xparam),1);
return
end
end
end
T = options_mom_.nobs;
T = options_mom_.nobs; %Number of observations
if isfield(options_mom_,'variance_correction_factor')
T = T*options_mom_.variance_correction_factor;
end
WW = weighting_info.Sw'*weighting_info.Sw;
if weighting_info.Woptflag
% we already have the optimal weighting matrix
asympt_cov_mat = 1/T*((D'*WW*D)\eye(dim_params));
WW = oo_.mom.Sw'*oo_.mom.Sw;
if Wopt_flag
% We have the optimal weighting matrix
Asympt_Var = 1/T*((D'*WW*D)\eye(dim_params));
else
% we do not have the optimal weighting matrix yet
WWopt = mom.optimal_weighting_matrix(m_data, model_moments, options_mom_.mom.bartlett_kernel_lag);
% We do not have the optimal weighting matrix yet
WWopt = mom.optimal_weighting_matrix(oo_.mom.m_data, oo_.mom.model_moments, options_mom_.mom.bartlett_kernel_lag);
S = WWopt\eye(size(WWopt,1));
AA = (D'*WW*D)\eye(dim_params);
asympt_cov_mat = 1/T*AA*D'*WW*S*WW*D*AA;
Asympt_Var = 1/T*AA*D'*WW*S*WW*D*AA;
end
stderr_values = sqrt(diag(asympt_cov_mat));
SE_values = sqrt(diag(Asympt_Var));

9
matlab/+mom/transform_prior_to_laplace_prior.m
View File

@ -1,10 +1,10 @@
function bayestopt_ = transform_prior_to_laplace_prior(bayestopt_)
% bayestopt_ = transform_prior_to_laplace_prior(bayestopt_)
% function bayestopt_ = transform_prior_to_laplace_prior(bayestopt_)
% -------------------------------------------------------------------------
% Transforms the prior specification to a Laplace type of approximation:
% only the prior mean and standard deviation are relevant, all other shape
% information, except for the parameter bounds, is ignored.
% -------------------------------------------------------------------------
% =========================================================================
% INPUTS
% bayestopt_ [structure] prior information
% -------------------------------------------------------------------------
@ -13,8 +13,7 @@ function bayestopt_ = transform_prior_to_laplace_prior(bayestopt_)
% -------------------------------------------------------------------------
% This function is called by
% o mom.run
% -------------------------------------------------------------------------
% =========================================================================
% Copyright © 2023 Dynare Team
%
% This file is part of Dynare.
@ -31,7 +30,7 @@ function bayestopt_ = transform_prior_to_laplace_prior(bayestopt_)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% =========================================================================
if any(setdiff([0;bayestopt_.pshape],[0,3]))
fprintf('\nNon-normal priors specified. Penalized estimation uses a Laplace type of approximation:');
fprintf('\nOnly the prior mean and standard deviation are relevant, all other shape information, except for the parameter bounds, is ignored.\n\n');

134
matlab/+occbin/DSGE_smoother.m
View File

@ -1,5 +1,5 @@
function [alphahat,etahat,epsilonhat,ahat0,SteadyState,trend_coeff,aKK,T0,R0,P,PKK,decomp,Trend,state_uncertainty,oo_,bayestopt_,alphahat0,state_uncertainty0] = DSGE_smoother(xparam1,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_,dataset_, dataset_info)
% [alphahat,etahat,epsilonhat,ahat0,SteadyState,trend_coeff,aKK,T0,R0,P,PKK,decomp,Trend,state_uncertainty,oo_,bayestopt_,alphahat0,state_uncertainty0] = DSGE_smoother(xparam1,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_,dataset_, dataset_info)
%function [alphahat,etahat,epsilonhat,ahat0,SteadyState,trend_coeff,aKK,T0,R0,P,PKK,decomp,Trend,state_uncertainty,M_,oo_,bayestopt_] = DSGE_smoother(xparam1,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_,dataset_, dataset_info)
% Runs a DSGE smoother with occasionally binding constraints
%
% INPUTS
@ -65,10 +65,8 @@ regime_history=[];
if options_.occbin.smoother.linear_smoother && nargin==12
%% linear smoother
options_.occbin.smoother.status=false;
[alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T0,R0,P,PK,decomp,Trend,state_uncertainty,oo_,bayestopt_,alphahat0,state_uncertainty0] = ...
DsgeSmoother(xparam1,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_);
tmp_smoother=store_smoother_results(M_,oo_,options_,bayestopt_,dataset_,dataset_info,alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,...
aK,P,PK,decomp,Trend,state_uncertainty);
[alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T0,R0,P,PK,decomp,Trend,state_uncertainty,oo_,bayestopt_] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_);
tmp_smoother=store_smoother_results(M_,oo_,options_,bayestopt_,dataset_,dataset_info,alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,P,PK,decomp,Trend,state_uncertainty);
for jf=1:length(smoother_field_list)
oo_.occbin.linear_smoother.(smoother_field_list{jf}) = tmp_smoother.(smoother_field_list{jf});
end
@ -82,9 +80,7 @@ if options_.occbin.smoother.linear_smoother && nargin==12
oo_.occbin.linear_smoother.T0=T0;
oo_.occbin.linear_smoother.R0=R0;
oo_.occbin.linear_smoother.decomp=decomp;
oo_.occbin.linear_smoother.alphahat0=alphahat0;
oo_.occbin.linear_smoother.state_uncertainty0=state_uncertainty0;
fprintf('\nOccbin: linear smoother done.\n')
options_.occbin.smoother.status=true;
end
@ -116,46 +112,15 @@ opts_simul.max_check_ahead_periods = options_.occbin.smoother.max_check_ahead_pe
opts_simul.periodic_solution = options_.occbin.smoother.periodic_solution;
opts_simul.full_output = options_.occbin.smoother.full_output;
opts_simul.piecewise_only = options_.occbin.smoother.piecewise_only;
% init_mode = options_.occbin.smoother.init_mode; % 0 = standard; 1 = unconditional frcsts zero shocks+smoothed states in each period
% init_mode = 0;
occbin_options = struct();
occbin_options.first_period_occbin_update = options_.occbin.smoother.first_period_occbin_update;
occbin_options.opts_simul = opts_simul; % this builds the opts_simul options field needed by occbin.solver
occbin_options.opts_regime.binding_indicator = options_.occbin.smoother.init_binding_indicator;
occbin_options.opts_regime.regime_history=options_.occbin.smoother.init_regime_history;
error_indicator=false;
try
%blanket try-catch should be replaced be proper error handling, see https://git.dynare.org/Dynare/dynare/-/merge_requests/2226#note_20318
[alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T0,R0,P,PK,decomp,Trend,state_uncertainty,oo_,bayestopt_,alphahat0,state_uncertainty0] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_,occbin_options);% T1=TT;
catch ME
error_indicator=true;
disp(ME.message)
for iter = 1:numel(ME.stack)
ME.stack(iter)
end
end
if error_indicator || isempty(alphahat0)
etahat= oo_.occbin.linear_smoother.etahat;
alphahat0= oo_.occbin.linear_smoother.alphahat0;
base_regime = struct();
if M_.occbin.constraint_nbr==1
base_regime.regime = 0;
base_regime.regimestart = 1;
else
base_regime.regime1 = 0;
base_regime.regimestart1 = 1;
base_regime.regime2 = 0;
base_regime.regimestart2 = 1;
end
oo_.occbin.smoother.regime_history = [];
for jper=1:size(alphahat,2)+1
if jper == 1
oo_.occbin.smoother.regime_history = base_regime;
else
oo_.occbin.smoother.regime_history(jper) = base_regime;
end
end
end
occbin_options.opts_regime = opts_simul; % this builds the opts_simul options field needed by occbin.solver
occbin_options.opts_regime.binding_indicator = options_.occbin.likelihood.init_binding_indicator;
occbin_options.opts_regime.regime_history=options_.occbin.likelihood.init_regime_history;
[alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T0,R0,P,PK,decomp,Trend,state_uncertainty,oo_,bayestopt_,alphahat0,state_uncertainty0, diffuse_steps] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_,occbin_options);% T1=TT;
oo_.occbin.smoother.realtime_regime_history = oo_.occbin.smoother.regime_history;
regime_history = oo_.occbin.smoother.regime_history;
@ -176,7 +141,6 @@ opts_simul.SHOCKS = [etahat(:,1:end)'; zeros(1,M_.exo_nbr)];
opts_simul.exo_pos = 1:M_.exo_nbr;
opts_simul.endo_init = alphahat0(oo_.dr.inv_order_var,1);
opts_simul.init_regime=regime_history; % use realtime regime for guess, to avoid multiple solution issues!
opts_simul.periods = size(opts_simul.SHOCKS,1);
options_.occbin.simul=opts_simul;
options_.noprint = true;
[~, out, ss] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
@ -192,10 +156,15 @@ if options_.smoother_redux
[T0,R0] = dynare_resolve(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
oo_.occbin.linear_smoother.T0=T0;
oo_.occbin.linear_smoother.R0=R0;
% oo_.occbin.linear_smoother.T0=ss.T(oo_.dr.order_var,oo_.dr.order_var,1);
% oo_.occbin.linear_smoother.R0=ss.R(oo_.dr.order_var,:,1);
end
TT = ss.T(oo_.dr.order_var,oo_.dr.order_var,:);
RR = ss.R(oo_.dr.order_var,:,:);
CC = ss.C(oo_.dr.order_var,:);
% TT = cat(3,TT(:,:,1),TT);
% RR = cat(3,RR(:,:,1),RR);
% CC = cat(2,CC(:,1),CC);
opts_regime.regime_history = regime_history;
opts_regime.binding_indicator = [];
@ -217,7 +186,6 @@ sto_etahat={etahat};
sto_CC = CC;
sto_RR = RR;
sto_TT = TT;
sto_eee=NaN(size(TT,1),size(TT,3));
for k=1:size(TT,3)
sto_eee(:,k) = eig(TT(:,:,k));
end
@ -227,12 +195,11 @@ while is_changed && maxiter>iter && ~is_periodic
iter=iter+1;
fprintf('Occbin smoother iteration %u.\n', iter)
occbin_options.opts_regime.regime_history=regime_history;
[alphahat,etahat,epsilonhat,~,SteadyState,trend_coeff,~,T0,R0,P,~,decomp,Trend,state_uncertainty,oo_,bayestopt_,alphahat0,state_uncertainty0]...
= DsgeSmoother(xparam1,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_,occbin_options,TT,RR,CC);
[alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T0,R0,P,PK,decomp,Trend,state_uncertainty,oo_,bayestopt_,alphahat0,state_uncertainty0, diffuse_steps] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value,M_,oo_,options_,bayestopt_,estim_params_,occbin_options,TT,RR,CC);% T1=TT;
sto_etahat(iter)={etahat};
regime_history0(iter,:) = regime_history;
if occbin_smoother_debug
save('Occbin_smoother_debug_regime_history','regime_history0');
save('info1','regime_history0');
end
sto_CC = CC;
@ -253,13 +220,13 @@ while is_changed && maxiter>iter && ~is_periodic
TT = ss.T(oo_.dr.order_var,oo_.dr.order_var,:);
RR = ss.R(oo_.dr.order_var,:,:);
CC = ss.C(oo_.dr.order_var,:);
% TT = cat(3,TT(:,:,1),TT);
% RR = cat(3,RR(:,:,1),RR);
% CC = cat(2,CC(:,1),CC);
opts_regime.regime_history = regime_history;
[TT, RR, CC, regime_history] = occbin.check_regimes(TT, RR, CC, opts_regime, M_, options_ , oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
is_changed = ~isequal(regime_history0(iter,:),regime_history);
isdiff_regime=NaN(size(regime_history0,2),M_.occbin.constraint_nbr);
isdiff_start=NaN(size(isdiff_regime));
isdiff_=NaN(size(isdiff_regime));
if M_.occbin.constraint_nbr==2
for k=1:size(regime_history0,2)
isdiff_regime(k,1) = ~isequal(regime_history0(end,k).regime1,regime_history(k).regime1);
@ -269,16 +236,16 @@ while is_changed && maxiter>iter && ~is_periodic
isdiff_start(k,2) = ~isequal(regime_history0(end,k).regimestart2,regime_history(k).regimestart2);
isdiff_(k,2) = isdiff_regime(k,2) || isdiff_start(k,2);
end
is_changed_regime = any(isdiff_regime(:,1)) || any(isdiff_regime(:,2));
is_changed_start = any(isdiff_start(:,1)) || any(isdiff_start(:,2));
is_changed_regime = ~isempty(find(isdiff_regime(:,1))) || ~isempty(find(isdiff_regime(:,2)));
is_changed_start = ~isempty(find(isdiff_start(:,1))) || ~isempty(find(isdiff_start(:,2)));
else
for k=1:size(regime_history0,2)
isdiff_regime(k,1) = ~isequal(regime_history0(end,k).regime,regime_history(k).regime);
isdiff_start(k,1) = ~isequal(regime_history0(end,k).regimestart,regime_history(k).regimestart);
isdiff_(k,1) = isdiff_regime(k,1) || isdiff_start(k,1);
end
is_changed_regime = any(isdiff_regime(:,1));
is_changed_start = any(isdiff_start(:,1));
is_changed_regime = ~isempty(find(isdiff_regime(:,1)));
is_changed_start = ~isempty(find(isdiff_start(:,1)));
end
if occbin_smoother_fast
is_changed = is_changed_regime;
@ -294,11 +261,9 @@ while is_changed && maxiter>iter && ~is_periodic
end
if is_changed
eee=NaN(size(TT,1),size(TT,3));
for k=1:size(TT,3)
eee(:,k) = eig(TT(:,:,k));
end
if options_.debug
err_eig(iter-1) = max(max(abs(sort(eee)-sort(sto_eee))));
err_alphahat(iter-1) = max(max(max(abs(alphahat-sto_alphahat))));
err_etahat(iter-1) = max(max(max(abs(etahat-sto_etahat{iter-1}))));
@ -306,7 +271,6 @@ while is_changed && maxiter>iter && ~is_periodic
err_RR(iter-1) = max(max(max(abs(RR-sto_RR))));
err_TT(iter-1) = max(max(max(abs(TT-sto_TT))));
end
end
if occbin_smoother_debug || is_periodic
regime_ = cell(0);
@ -386,7 +350,7 @@ regime_history0(max(iter+1,1),:) = regime_history;
oo_.occbin.smoother.regime_history=regime_history0(end,:);
oo_.occbin.smoother.regime_history_iter=regime_history0;
if occbin_smoother_debug
save('Occbin_smoother_debug_regime_history','regime_history0')
save('info1','regime_history0')
end
if (maxiter==iter && is_changed) || is_periodic
@ -429,24 +393,12 @@ if (~is_changed || occbin_smoother_debug) && nargin==12
oo_.occbin.smoother.T0=TT;
oo_.occbin.smoother.R0=RR;
oo_.occbin.smoother.C0=CC;
oo_.occbin.smoother.simul.piecewise = out.piecewise(1:end-1,:);
if ~options_.occbin.simul.piecewise_only
oo_.occbin.smoother.simul.linear = out.linear(1:end-1,:);
end
if options_.occbin.smoother.plot
GraphDirectoryName = CheckPath('graphs',M_.fname);
latexFolder = CheckPath('latex',M_.dname);
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fidTeX = fopen([latexFolder filesep M_.fname '_OccBin_smoother_plots.tex'],'w');
fprintf(fidTeX,'%% TeX eps-loader file generated by occbin.DSGE_smoother.m (Dynare).\n');
fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
fprintf(fidTeX,' \n');
end
j1=0;
ifig=0;
for j=1:M_.exo_nbr
if max(abs(oo_.occbin.smoother.etahat(j,:)))>1.e-8
if M_.Sigma_e(j,j)
j1=j1+1;
if mod(j1,9)==1
hh_fig = dyn_figure(options_.nodisplay,'name','Occbin smoothed shocks');
@ -462,49 +414,23 @@ if (~is_changed || occbin_smoother_debug) && nargin==12
plot(oo_.occbin.smoother.etahat(j,:)','r--','linewidth',2)
hold on, plot([0 options_.nobs],[0 0],'k--')
set(gca,'xlim',[0 options_.nobs])
if options_.TeX
title(['$' M_.exo_names_tex{j,:} '$'],'interpreter','latex')
else
title(M_.exo_names{j,:},'interpreter','none')
end
title(deblank(M_.exo_names(j,:)),'interpreter','none')
if mod(j1,9)==0
if options_.occbin.smoother.linear_smoother
annotation('textbox', [0.1,0,0.35,0.05],'String', 'Linear','Color','Blue','horizontalalignment','center','interpreter','none');
end
annotation('textbox', [0.55,0,0.35,0.05],'String', 'Piecewise','Color','Red','horizontalalignment','center','interpreter','none');
dyn_saveas(gcf,[GraphDirectoryName filesep M_.fname,'_smoothedshocks_occbin',int2str(ifig)],options_.nodisplay,options_.graph_format);
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
% TeX eps loader file
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,'\\label{Fig:smoothedshocks_occbin:%s}\n',int2str(ifig));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,' \n');
end
end
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);
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
% TeX eps loader file
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,'\\label{Fig:smoothedshocks_occbin:%s}\n',int2str(ifig));
fprintf(fidTeX,'\\end{figure}\n');
fprintf(fidTeX,' \n');
end
end
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fclose(fidTeX);
end
end
end

6
matlab/+occbin/IVF_posterior.m
View File

@ -71,7 +71,7 @@ end
if ~isempty(xparam1)
M_ = set_all_parameters(xparam1,estim_params_,M_);
[fval,info,exit_flag]=check_bounds_and_definiteness_estimation(xparam1, M_, estim_params_, BoundsInfo);
[fval,info,exit_flag,Q,H]=check_bounds_and_definiteness_estimation(xparam1, M_, estim_params_, BoundsInfo);
if info(1)
return
end
@ -81,7 +81,7 @@ err_index=options_.occbin.likelihood.IVF_shock_observable_mapping; % err_index=
COVMAT1 = M_.Sigma_e(err_index,err_index);
% Linearize the model around the deterministic steady state and extract the matrices of the state equation (T and R).
[~,~,SteadyState,info,dr, M_.params] = dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,'restrict');
[T,R,SteadyState,info,dr, M_.params] = dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,'restrict');
% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
if info(1)
@ -195,4 +195,4 @@ end
% remember that the likelihood has already been multiplied by -1
% hence, posterior is -1 times the log of the prior
fval = like+prior;
fval = like+prior;

87
matlab/+occbin/backward_map_regime.m
View File

@ -1,87 +0,0 @@
function [binding_indicator, A, regime_string] = backward_map_regime(regime, regime_start)
% [binding_indicator, A, regime_string] = backward_map_regime(regime, regime_start)
% Map regime information into regime indicator
%
% Inputs:
% - regime [integer] [1 by n_transitions] vector of regime number indices
% - regime_start [integer] [1 by n_transitions] vectors with period numbers in which regime starts
%
% Outputs:
% - binding_indicator [integer] [nperiods by 1] vector of regime indices
% - A [bin] binary representation of binding indicator
% - error_flag [boolean] 1 if regime never leaves 1 or is still there at the end of nperiods
% 0 otherwise
% Copyright © 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
if nargin ==1
% polymorphism
if ~isstruct(regime)
disp('error::backward_map_regime')
disp('input arguments may be 1 structure with regime info')
disp('or two arrays: regime and regimestart ')
error('wrong input')
end
fnam = fieldnames(regime);
if length(fnam) == 2
[binding_indicator, A, regime_string] = occbin.backward_map_regime(regime.regime, regime.regimestart);
else
for k=1:2
nperiods(k) = regime.(['regimestart' int2str(k)])(end);
number_of_binary_tokens(k) = ceil((nperiods(k)-1)/50);
end
binding_indicator = false(max(nperiods),2);
A = int64(zeros(max(number_of_binary_tokens),2));
for k=1:2
[binding_indicator(1:nperiods(k),k), A(1:number_of_binary_tokens(k),k), tmp{k}] = ...
occbin.backward_map_regime(regime.(['regime' int2str(k)]), regime.(['regimestart' int2str(k)]));
end
regime_string = char(tmp{1},tmp{2});
end
return
else
if isstruct(regime)
disp('error::backward_map_regime')
disp('input arguments may be ONE structure with regime info')
disp('or TWO arrays: regime and regimestart ')
error('wrong input')
end
end
regime_string = char(mat2str(double(regime)),mat2str(regime_start));
nperiods_0 = regime_start(end);
number_of_binary_tokens = max(1,ceil((nperiods_0-1)/50));
A = int64(zeros(number_of_binary_tokens,1));
binding_indicator = false(nperiods_0,1);
if length(regime)>1
for ir=1:length(regime)-1
binding_indicator(regime_start(ir):regime_start(ir+1)-1,1) = regime(ir);
for k=regime_start(ir):regime_start(ir+1)-1
this_token = ceil(k/50);
A(this_token) = int64(bitset(A(this_token),k-50*(this_token-1),regime(ir)));
end
end
end
binding_indicator = logical(binding_indicator);
% to convert regime in a readable string array
% a = dec2bin(A);
% bindicator = [a(end:-1:1) '0'];

50
matlab/+occbin/cost_function.m
View File

@ -1,50 +0,0 @@
function [cost, out] = cost_function(err_0, current_obs, weights, opts_simul,...
M_, dr,endo_steady_state,exo_steady_state,exo_det_steady_state, options_)
% [cost, out] = cost_function(err_0, current_obs, opts_simul,...
% M_, dr,endo_steady_state,exo_steady_state,exo_det_steady_state, options_)
% Outputs:
% - cost [double] penalty
% - out [structure] Occbin's results structure
%
% Inputs
% - err_0 [double] value of shocks
% - current_obs [double] [1 by n_obs] current value of observables
% - weights [double] [1 by n_obs] variance of observables,
% - opts_simul [structure] Structure with simulation options
% used in cost function
% - M_ [structure] Matlab's structure describing the model (M_).
% - dr_ [structure] model information structure
% - endo_steady_state [vector] steady state value for endogenous variables
% - exo_steady_state [vector] steady state value for exogenous variables
% - exo_det_steady_state [vector] steady state value for exogenous deterministic variables
% - options_ [structure] Matlab's structure describing the current options (options_).
% Copyright © 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
opts_simul.SHOCKS = err_0';
options_.occbin.simul=opts_simul;
options_.occbin.simul.full_output=1;
options_.noprint = 1;
[~, out] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
cost = 0;
if ~out.error_flag
cost = mean((out.piecewise(1,opts_simul.varobs_id)'-current_obs').^2./weights);
else
cost = cost+1.e10;
end

63
matlab/+occbin/findmin.m
View File

@ -1,63 +0,0 @@
function [y, out, cost] = findmin(d_index, a0, P1, Qt, Y, ZZ, opts_simul,M_, dr,endo_steady_state,exo_steady_state,exo_det_steady_state, options_)
% [y, out, cost] = findmin(d_index, a0, P1, Qt, Y, ZZ, opts_simul,M_, dr,endo_steady_state,exo_steady_state,exo_det_steady_state, options_)
% Outputs:
% - cost [double] penalty
% - out [structure] Occbin's results structure
%
% Inputs
% - opts_simul [structure] Structure with simulation options
% used in cost function
% - M_ [structure] Matlab's structure describing the model (M_).
% - dr_ [structure] model information structure
% - endo_steady_state [vector] steady state value for endogenous variables
% - exo_steady_state [vector] steady state value for exogenous variables
% - exo_det_steady_state [vector] steady state value for exogenous deterministic variables
% - options_ [structure] Matlab's structure describing the current options (options_).
% Copyright © 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
current_obs = Y(d_index,2)'+dr.ys(options_.varobs_id(d_index))';
err_index = find(diag(Qt(:,:,2))~=0);
F = ZZ(d_index,:)*P1(:,:,2)*ZZ(d_index,:)' ;
weights=diag(F);
filtered_errs_init = zeros(1,length(err_index));
opts_simul.varobs_id=options_.varobs_id(d_index)';
opts_simul.exo_pos=err_index; %err_index is predefined mapping from observables to shocks
opts_simul.SHOCKS = filtered_errs_init;
if opts_simul.restrict_state_space
tmp=zeros(M_.endo_nbr,1);
tmp(dr.restrict_var_list,1)=a0(:,1); %updated state
opts_simul.endo_init = tmp(dr.inv_order_var,1);
else
opts_simul.endo_init = a0(dr.inv_order_var,1);
end
[y] = fminsearch(@cost_function,filtered_errs_init');
[cost, out] = occbin.cost_function(y, current_obs, weights, opts_simul,...
M_, dr,endo_steady_state,exo_steady_state,exo_det_steady_state, options_);
function cost = cost_function(x)
cost = occbin.cost_function(x, current_obs, weights, opts_simul,...
M_, dr,endo_steady_state,exo_steady_state,exo_det_steady_state, options_);
end
end

169
matlab/+occbin/forecast.m
View File

@ -1,169 +0,0 @@
function [forecast, error_flag] = forecast(options_,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,forecast_horizon)
% [forecast, error_flag] = forecast(options_,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,forecast_horizon)
% Occbin forecasts
%
% 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;
shocks_input = opts.SHOCKS0;
if ~isempty(shocks_input)
n_shocks=size(shocks_input,1);
if iscell(shocks_input)
inds=NaN(n_shocks,1);
periods=length(shocks_input{1}{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)
shock_mat=shocks_input;
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
[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);
else
SHOCKS_add = randn(forecast_horizon*effective_exo_nbr,opts.replic);
end
SHOCKS_add=reshape(SHOCKS_add,effective_exo_nbr,forecast_horizon,opts.replic);
z.linear=NaN(forecast_horizon,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;
[~, out] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
error_flag(iter)=out.error_flag;
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
dyn_waitbar(iter/opts.replic,h,['OccBin MC forecast replic ',int2str(iter),'/',int2str(opts.replic)])
end
dyn_waitbar_close(h);
if options_.debug
save('Occbin_forecast_debug','simul_SHOCKS','z','iter','frcst_regime_history','error_flag')
end
inx=find(error_flag==0);
z.linear=z.linear(:,:,inx);
z.piecewise=z.piecewise(:,:,inx);
z.min.piecewise = min(z.piecewise,[],3);
z.max.piecewise = max(z.piecewise,[],3);
z.min.linear = min(z.linear,[],3);
z.max.linear = max(z.linear,[],3);
field_names={'linear','piecewise'};
post_mean=NaN(forecast_horizon,1);
post_median=NaN(forecast_horizon,1);
post_var=NaN(forecast_horizon,1);
hpd_interval=NaN(forecast_horizon,2);
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
else
options_.occbin.simul.irfshock = M_.exo_names;
options_.occbin.simul.SHOCKS = shocks_base;
[~, out] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
error_flag=out.error_flag;
if out.error_flag
fprintf('occbin.forecast: forecast simulation failed.')
return;
end
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);
forecast.piecewise.Mean.(M_.endo_names{i})= out.piecewise(:,i);
end
end
forecast.regimes=frcst_regime_history;

2
matlab/+occbin/graph.m
View File

@ -109,7 +109,7 @@ for fig = 1:nbplt
if ndim==2
legend([h1,h2],legend_list,'box','off')
else
legend(h1,legend_list,'box','off')
legend([h1],legend_list,'box','off')
end
end
if options_.TeX

124
matlab/+occbin/irf.m
View File

@ -1,124 +0,0 @@
function irfs = 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'
shocksize = options_.occbin.irf.shocksize;
t_0 = 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
if t_0 == 0
shocks_base = zeros(options_.occbin.simul.periods+1,M_.exo_nbr);
options_.occbin.simul.endo_init = [];
else
if ~isfield(oo_.occbin,'smoother')
error('occbin.irfs: smoother must be run before requesting GIRFs based on smoothed results')
end
% GIRF conditional on smoothed states in t_0 and shocks in t_0+1
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;
[~, out_base] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
if out_base.error_flag
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');
end
% cs=get_lower_cholesky_covariance(M_.Sigma_e,options_.add_tiny_number_to_cholesky);
% irf_shocks_indx = getIrfShocksIndx(M_, options_);
% Set shock size
if isempty(shocksize)
shocksize = sqrt(diag(M_.Sigma_e(exo_index,exo_index)));
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 IRF_counter = 1:length(exo_index)
jexo = exo_index(IRF_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));
end
shocks1=shocks_base;
if ismember('pos',shocksigns{sign_iter})
shocks1(1,jexo)=shocks_base(1,jexo)+shocksize(IRF_counter);
elseif ismember('neg',shocksigns{sign_iter})
shocks1(1,jexo)=shocks_base(1,jexo)-shocksize(IRF_counter);
end
options_.occbin.simul.SHOCKS=shocks1;
if t_0 == 0
options_.occbin.simul.endo_init = [];
else
options_.occbin.simul.endo_init = oo_.occbin.smoother.alphahat(oo_.dr.inv_order_var,t_0);
end
[~, out_sim] = occbin.solver(M_,options_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
if out_sim.error_flag
warning('occbin.irfs: simulation failed')
skip
end
% Substract inital conditions + other shocks
zdiff.linear.(shocksigns{sign_iter}) = out_sim.linear-out_base.linear;
zdiff.piecewise.(shocksigns{sign_iter}) = out_sim.piecewise-out_base.piecewise;
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

234
matlab/+occbin/kalman_update_algo_1.m
View File

@ -1,4 +1,4 @@
function [a, a1, P, P1, v, T, R, C, regimes_, error_flag, M_, lik, etahat, alphahat, V] = kalman_update_algo_1(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,T0,R0,TT,RR,CC,regimes0,M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_,occbin_options)
function [a, a1, P, P1, v, T, R, C, regimes_, error_flag, M_, lik, etahat] = kalman_update_algo_1(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,T0,R0,TT,RR,CC,regimes0,M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_,occbin_options)
% function [a, a1, P, P1, v, T, R, C, regimes_, error_flag, M_, lik, etahat] = kalman_update_algo_1(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,T0,R0,TT,RR,CC,regimes0,M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_,occbin_options)
% INPUTS
% - a [N by 1] t-1's state estimate
@ -86,8 +86,6 @@ else
base_regime.regimestart2 = 1;
end
regimes_ = [base_regime base_regime base_regime];
opts_simul = occbin_options.opts_simul;
options_.occbin.simul=opts_simul;
mm=size(a,1);
%% store info in t=1
@ -105,46 +103,16 @@ PZI = P1(:,:,t)*ZZ'*iF(di,di,t);
% L(:,:,t) = T-K(:,di,t)*ZZ;
L(:,:,t) = eye(mm)-PZI*ZZ;
if ~isempty(fieldnames(regimes0))
if options_.occbin.filter.guess_regime
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state, regimes0(1),'reduced_state_space',T0,R0);
if M_.occbin.constraint_nbr==1
bindx = occbin.backward_map_regime(regimes0(1).regime, regimes0(1).regimestart);
bindx = bindx(2:end);
[regimes0(2).regime, regimes0(2).regimestart, error_flag]=occbin.map_regime(bindx);
bindx = bindx(2:end);
[regimes0(3).regime, regimes0(3).regimestart, error_flag]=occbin.map_regime(bindx);
else
bindx1 = occbin.backward_map_regime(regimes0(1).regime1, regimes0(1).regimestart1);
bindx2 = occbin.backward_map_regime(regimes0(1).regime2, regimes0(1).regimestart2);
bindx1 = bindx1(2:end);
bindx2 = bindx2(2:end);
[regimes0(2).regime1, regimes0(2).regimestart1, error_flag]=occbin.map_regime(bindx1);
[regimes0(2).regime2, regimes0(2).regimestart2, error_flag]=occbin.map_regime(bindx2);
bindx1 = bindx1(2:end);
bindx2 = bindx2(2:end);
[regimes0(3).regime1, regimes0(3).regimestart1, error_flag]=occbin.map_regime(bindx1);
[regimes0(3).regime2, regimes0(3).regimestart2, error_flag]=occbin.map_regime(bindx2);
end
% regimes0=[regimes0 base_regime base_regime];
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
end
[a, a1, P, P1, v, alphahat, etahat, lik, V, error_flag] = occbin_kalman_update0(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,iF,L,mm, options_.rescale_prediction_error_covariance, options_.occbin.likelihood.IF_likelihood, options_.occbin.filter.state_covariance);
if ~options_.occbin.filter.use_relaxation
[a, a1, P, P1, v, alphahat, etahat, lik, error_flag] = occbin_kalman_update0(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,iF,L,mm, options_.rescale_prediction_error_covariance, options_.occbin.likelihood.IF_likelihood);
else
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state, base_regime,'reduced_state_space',T0,R0);
if isempty(fieldnames(regimes0))
regimes0 = regimes_;
else
regimes0(1)=base_regime;
end
regimes0(1)=base_regime;
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
[a, a1, P, P1, v, alphahat, etahat, lik, V, error_flag] = occbin_kalman_update0(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,iF,L,mm, options_.rescale_prediction_error_covariance, options_.occbin.likelihood.IF_likelihood, options_.occbin.filter.state_covariance);
[a, a1, P, P1, v, alphahat, etahat, lik, error_flag] = occbin_kalman_update0(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,iF,L,mm, options_.rescale_prediction_error_covariance, options_.occbin.likelihood.IF_likelihood);
end
if error_flag
etahat=NaN(size(QQQ,1),1);
@ -152,6 +120,7 @@ if error_flag
end
%% run here the occbin simul
opts_simul = occbin_options.opts_simul;
opts_simul.SHOCKS = zeros(3,M_.exo_nbr);
opts_simul.exo_pos=1:M_.exo_nbr;
opts_simul.SHOCKS(1,:) = etahat(:,2)';
@ -165,24 +134,14 @@ else
my_order_var = dr.order_var;
end
options_.occbin.simul=opts_simul;
if options_.occbin.filter.guess_regime
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if out.error_flag
options_.occbin.simul.init_regime=regimes0;
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if out.error_flag
options_.occbin.simul=opts_simul;
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
end
else
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if out.error_flag
options_.occbin.simul.init_regime=regimes0;
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
end
end
if out.error_flag
error_flag = out.error_flag;
etahat=etahat(:,2);
lik=inf;
return;
end
@ -200,18 +159,53 @@ end
lik_hist=lik;
niter=1;
is_periodic=0;
if options_.occbin.filter.use_relaxation || isequal(regimes0(1),base_regime)
nguess=1;
else
nguess=0;
end
newguess=0;
if any(myregime) || ~isequal(regimes_(1),regimes0(1))
while ~isequal(regimes_(1),regimes0(1)) && ~is_periodic && ~out.error_flag && niter<=options_.occbin.likelihood.max_number_of_iterations
niter=niter+1;
TT(:,:,2)=ss.T(my_order_var,my_order_var,1);
RR(:,:,2)=ss.R(my_order_var,:,1);
CC(:,2)=ss.C(my_order_var,1);
oldstart=1;
if M_.occbin.constraint_nbr==1 && length(regimes0(1).regimestart)>1
oldstart = regimes0(1).regimestart(end);
end
newstart=1;
if M_.occbin.constraint_nbr==1 && length(regimes_(1).regimestart)>1
newstart = regimes_(1).regimestart(end);
end
if M_.occbin.constraint_nbr==1 && (newstart-oldstart)>2 && options_.occbin.filter.use_relaxation
regimestart = max(oldstart+2,round(0.5*(newstart+oldstart)));
regimestart = min(regimestart,oldstart+4);
if regimestart<=regimes_(1).regimestart(end-1)
if length(regimes_(1).regimestart)<=3
regimestart = max(regimestart, min(regimes_(1).regimestart(end-1)+2,newstart));
else
regimes_(1).regime = regimes_(1).regime(1:end-2);
regimes_(1).regimestart = regimes_(1).regimestart(1:end-2);
regimestart = max(regimestart, regimes_(1).regimestart(end-1)+1);
end
end
regimes_(1).regimestart(end)=regimestart;
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state, [base_regime regimes_(1)],'reduced_state_space', T0, R0);
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
elseif newguess==0
TT(:,:,2)=ss.T(my_order_var,my_order_var,1);
RR(:,:,2)=ss.R(my_order_var,:,1);
CC(:,2)=ss.C(my_order_var,1);
end
newguess=0;
regime_hist(niter) = {regimes_(1)};
if M_.occbin.constraint_nbr==1
regime_end(niter) = regimes_(1).regimestart(end);
end
[a, a1, P, P1, v, alphahat, etahat, lik, V] = occbin_kalman_update0(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,iF,L,mm, options_.rescale_prediction_error_covariance, options_.occbin.likelihood.IF_likelihood, options_.occbin.filter.state_covariance);
[a, a1, P, P1, v, alphahat, etahat, lik] = occbin_kalman_update0(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,iF,L,mm, options_.rescale_prediction_error_covariance, options_.occbin.likelihood.IF_likelihood);
etahat_hist(niter) = {etahat};
lik_hist(niter) = lik;
opts_simul.SHOCKS(1,:) = etahat(:,2)';
@ -222,7 +216,9 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
else
opts_simul.endo_init = alphahat(dr.inv_order_var,1);
end
opts_simul.init_regime=regimes_(1);
if not(options_.occbin.filter.use_relaxation)
opts_simul.init_regime=regimes_(1);
end
if M_.occbin.constraint_nbr==1
myregimestart = [regimes_.regimestart];
else
@ -231,14 +227,9 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
opts_simul.periods = max(opts_simul.periods,max(myregimestart));
options_.occbin.simul=opts_simul;
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if out.error_flag
options_.occbin.simul.init_regime=[];
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
end
if out.error_flag
error_flag = out.error_flag;
etahat=etahat(:,2);
lik=inf;
return;
end
regimes0=regimes_;
@ -247,14 +238,36 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
for kiter=1:niter-1
is_periodic(kiter) = isequal(regime_hist{kiter}, regimes_(1));
end
is_periodic_iter = find(is_periodic);
is_periodic = any(is_periodic);
if is_periodic
% re-set to previous regime
if options_.occbin.filter.periodic_solution
% force projection conditional on most likely regime
[m, im]=min(lik_hist(is_periodic_iter:end));
opts_simul.init_regime=regime_hist{is_periodic_iter+im-1};
if nguess<3 && M_.occbin.constraint_nbr==1
newguess=1;
is_periodic=0;
nguess=nguess+1;
if nguess==1
% change starting regime
regimes_(1).regime=0;
regimes_(1).regimestart=1;
elseif nguess==2
% change starting regime
regimes_(1).regime=[0 1 0];
regimes_(1).regimestart=[1 2 3];
else
regimes_(1).regime=[1 0];
regimes_(1).regimestart=[1 2];
end
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state, [base_regime regimes_(1)],'reduced_state_space',T0,R0);
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
regime_hist = regime_hist(1);
niter=1;
else
% re-set to previous regime
regimes_ = regimes0;
% force projection conditional on previous regime
opts_simul.init_regime=regimes0(1);
if M_.occbin.constraint_nbr==1
myregimestart = [regimes0.regimestart];
else
@ -267,20 +280,8 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
if out.error_flag
error_flag = out.error_flag;
etahat=etahat(:,2);
lik=inf;
return;
else
regimes_ = out.regime_history;
TT(:,:,2)=ss.T(my_order_var,my_order_var,1);
RR(:,:,2)=ss.R(my_order_var,:,1);
CC(:,2)=ss.C(my_order_var,1);
[a, a1, P, P1, v, alphahat, etahat, lik, V] = occbin_kalman_update0(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,iF,L,mm, options_.rescale_prediction_error_covariance, options_.occbin.likelihood.IF_likelihood, options_.occbin.filter.state_covariance);
end
else
error_flag = 330;
etahat=etahat(:,2);
lik=inf;
return;
end
end
end
@ -290,6 +291,62 @@ end
error_flag = out.error_flag;
if ~error_flag && niter>options_.occbin.likelihood.max_number_of_iterations && ~isequal(regimes_(1),regimes0(1))
error_flag = 1;
if M_.occbin.constraint_nbr==1 % try some other regime
[ll, il]=sort(lik_hist);
[ll, il]=sort(regime_end);
rr=regime_hist(il(2:3));
newstart=1;
if length(rr{1}.regimestart)>1
newstart = rr{1}.regimestart(end)-rr{1}.regimestart(end-1)+1;
end
oldstart=1;
if length(rr{2}.regimestart)>1
oldstart = rr{2}.regimestart(end)-rr{2}.regimestart(end-1)+1;
end
nstart=sort([newstart oldstart]);
regimes_=rr{1}(1);
for k=(nstart(1)+1):(nstart(2)-1)
niter=niter+1;
regimes_(1).regimestart(end)=k;
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state, [base_regime regimes_(1)],'reduced_state_space',T0,R0);
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
[a, a1, P, P1, v, alphahat, etahat, lik] = occbin_kalman_update0(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,iF,L,mm, options_.rescale_prediction_error_covariance, options_.occbin.likelihood.IF_likelihood);
etahat_hist(niter) = {etahat};
lik_hist(niter) = lik;
regime_hist(niter) = {regimes_(1)};
opts_simul.SHOCKS(1,:) = etahat(:,2)';
if opts_simul.restrict_state_space
tmp=zeros(M_.endo_nbr,1);
tmp(dr.restrict_var_list,1)=alphahat(:,1);
opts_simul.endo_init = tmp(dr.inv_order_var,1);
else
opts_simul.endo_init = alphahat(dr.inv_order_var,1);
end
% opts_simul.init_regime=regimes_(1);
if M_.occbin.constraint_nbr==1
myregimestart = [regimes_.regimestart];
else
myregimestart = [regimes_.regimestart1 regimes_.regimestart2];
end
opts_simul.periods = max(opts_simul.periods,max(myregimestart));
options_.occbin.simul=opts_simul;
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if out.error_flag
error_flag = out.error_flag;
etahat=etahat(:,2);
return;
end
if isequal(out.regime_history(1),regimes_(1))
error_flag=0;
break
end
end
regimes_ = out.regime_history;
end
end
if ~error_flag
@ -302,26 +359,17 @@ C = ss.C(my_order_var,1:2);
QQ = R(:,:,2)*QQQ(:,:,3)*transpose(R(:,:,2));
P(:,:,1) = P(:,:,2);
P(:,:,2) = T(:,:,2)*P(:,:,1)*transpose(T(:,:,2))+QQ;
if nargout>=13
etahat=etahat(:,2);
end
etahat=etahat(:,2);
warning_config;
end
function [a, a1, P, P1, v, alphahat, etahat, lik, V, error_flag] = occbin_kalman_update0(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,iF,L,mm, rescale_prediction_error_covariance, IF_likelihood, state_uncertainty_flag)
function [a, a1, P, P1, v, alphahat, etahat, lik, error_flag] = occbin_kalman_update0(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,iF,L,mm, rescale_prediction_error_covariance, IF_likelihood)
alphahat=NaN(size(a));
etahat=NaN(size(QQQ,1),2);
lik=Inf;
error_flag=0;
if state_uncertainty_flag
V = zeros(mm,mm,2);
N = zeros(mm,mm,3);
else
V=[];
end
warning off
if nargin<18
IF_likelihood=0;
@ -397,15 +445,9 @@ while t > 1
if isempty(di)
% in this case, L is simply T due to Z=0, so that DK (2012), eq. 4.93 obtains
r(:,t) = L(:,:,t)'*r(:,t+1); %compute r_{t-1}, DK (2012), eq. 4.38 with Z=0
if state_uncertainty_flag
N(:,:,t)=L(:,:,t)'*N(:,:,t+1)*L(:,:,t); %compute N_{t-1}, DK (2012), eq. 4.42 with Z=0
end
else
ZZ = Z(di,:);
r(:,t) = ZZ'*iF(di,di,t)*v(di,t) + L(:,:,t)'*r(:,t+1); %compute r_{t-1}, DK (2012), eq. 4.38
if state_uncertainty_flag
N(:,:,t)=ZZ'*iF(di,di,t)*ZZ+L(:,:,t)'*N(:,:,t+1)*L(:,:,t); %compute N_{t-1}, DK (2012), eq. 4.42
end
end
Q=QQQ(:,:,t);
QRt = Q*transpose(RR(:,:,t));
@ -413,10 +455,6 @@ while t > 1
alphahat(:,t) = a1(:,t) + P1(:,:,t)*r(:,t); %DK (2012), eq. 4.35
etahat(:,t) = QRt*r(:,t); %DK (2012), eq. 4.63
r(:,t) = T'*r(:,t); % KD (2003), eq. (23), equation for r_{t-1,p_{t-1}}
if state_uncertainty_flag
V(:,:,t) = P1(:,:,t)-P1(:,:,t)*N(:,:,t)*P1(:,:,t); %DK (2012), eq. 4.43
N(:,:,t) = T'*N(:,:,t)*T; %DK (2012), eq. 4.43
end
if IF_likelihood && t==2 && not(isempty(di))
ishocks = any(ZZ*RR(:,:,t));

247
matlab/+occbin/kalman_update_algo_3.m
View File

@ -1,4 +1,4 @@
function [a, a1, P, P1, v, Fi, Ki, T, R, C, regimes_, error_flag, M_, lik, alphahat, etahat, TT, RR, CC] = kalman_update_algo_3(a,a1,P,P1,data_index,Z,v,Fi,Ki,Y,H,QQQ,T0,R0,TT,RR,CC,regimes0,M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_,occbin_options,kalman_tol,nk)
function [a, a1, P, P1, v, Fi, Ki, T, R, C, regimes_, error_flag, M_, alphahat, etahat, TT, RR, CC] = kalman_update_algo_3(a,a1,P,P1,data_index,Z,v,Fi,Ki,Y,H,QQQ,T0,R0,TT,RR,CC,regimes0,M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_,occbin_options,kalman_tol,nk)
% function [a, a1, P, P1, v, Fi, Ki, T, R, C, regimes_, error_flag, M_, alphahat, etahat, TT, RR, CC] = kalman_update_algo_3(a,a1,P,P1,data_index,Z,v,Fi,Ki,Y,H,QQQ,T0,R0,TT,RR,CC,regimes0,M_,dr, endo_steady_state, exo_steady_state, exo_det_steady_state,options_,occbin_options,kalman_tol,nk)
%
% INPUTS
@ -85,8 +85,7 @@ if isempty(nk)
end
nk=max(nk,1);
opts_simul = occbin_options.opts_simul;
options_.occbin.simul=opts_simul;
opts_simul = occbin_options.opts_regime;
base_regime = struct();
if M_.occbin.constraint_nbr==1
base_regime.regime = 0;
@ -97,7 +96,6 @@ else
base_regime.regime2 = 0;
base_regime.regimestart2 = 1;
end
regimes_ = [base_regime base_regime base_regime];
myrestrict=[];
if options_.smoother_redux
opts_simul.restrict_state_space =1;
@ -105,46 +103,15 @@ if options_.smoother_redux
end
mm=size(a,1);
if ~isempty(fieldnames(regimes0))
if options_.occbin.filter.guess_regime
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state, regimes0(1),myrestrict,T0,R0);
if M_.occbin.constraint_nbr==1
bindx = occbin.backward_map_regime(regimes0(1).regime, regimes0(1).regimestart);
bindx = bindx(2:end);
[regimes0(2).regime, regimes0(2).regimestart, error_flag]=occbin.map_regime(bindx);
bindx = bindx(2:end);
[regimes0(3).regime, regimes0(3).regimestart, error_flag]=occbin.map_regime(bindx);
else
bindx1 = occbin.backward_map_regime(regimes0(1).regime1, regimes0(1).regimestart1);
bindx2 = occbin.backward_map_regime(regimes0(1).regime2, regimes0(1).regimestart2);
bindx1 = bindx1(2:end);
bindx2 = bindx2(2:end);
[regimes0(2).regime1, regimes0(2).regimestart1, error_flag]=occbin.map_regime(bindx1);
[regimes0(2).regime2, regimes0(2).regimestart2, error_flag]=occbin.map_regime(bindx2);
bindx1 = bindx1(2:end);
bindx2 = bindx2(2:end);
[regimes0(3).regime1, regimes0(3).regimestart1, error_flag]=occbin.map_regime(bindx1);
[regimes0(3).regime2, regimes0(3).regimestart2, error_flag]=occbin.map_regime(bindx2);
end
% regimes0=[regimes0 base_regime base_regime];
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
end
[a, a1, P, P1, v, Fi, Ki, alphahat, etahat, lik] = occbin_kalman_update(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,Ki,Fi,mm,kalman_tol);
if ~options_.occbin.filter.use_relaxation
[a, a1, P, P1, v, Fi, Ki, alphahat, etahat] = occbin_kalman_update(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,Ki,Fi,mm,kalman_tol);
else
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state, base_regime,myrestrict,T0,R0);
if isempty(fieldnames(regimes0))
regimes0 = regimes_;
else
regimes0(1)=base_regime;
end
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
[a, a1, P, P1, v, Fi, Ki, alphahat, etahat, lik] = occbin_kalman_update(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,Ki,Fi,mm,kalman_tol);
[a, a1, P, P1, v, Fi, Ki, alphahat, etahat] = occbin_kalman_update(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,Ki,Fi,mm,kalman_tol);
regimes0(1)=base_regime;
end
@ -165,23 +132,9 @@ else
end
options_.occbin.simul=opts_simul;
if options_.occbin.filter.guess_regime
options_.occbin.simul.init_regime=regimes0;
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if out.error_flag
options_.occbin.simul=opts_simul;
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
end
else
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if out.error_flag
options_.occbin.simul.init_regime=regimes0;
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
end
end
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if out.error_flag
error_flag = out.error_flag;
etahat=etahat(:,2);
return;
end
@ -195,23 +148,65 @@ regime_hist = {regimes0(1)};
if M_.occbin.constraint_nbr==1
regime_end = regimes0(1).regimestart(end);
end
lik_hist=lik;
niter=1;
is_periodic=0;
if options_.occbin.filter.use_relaxation || isequal(regimes0(1),base_regime)
nguess=1;
else
nguess=0;
end
newguess=0;
if any(myregime) || ~isequal(regimes_(1),regimes0(1))
while ~isequal(regimes_(1),regimes0(1)) && ~is_periodic && ~out.error_flag && niter<=options_.occbin.likelihood.max_number_of_iterations
niter=niter+1;
TT(:,:,2)=ss.T(my_order_var,my_order_var,1);
RR(:,:,2)=ss.R(my_order_var,:,1);
CC(:,2)=ss.C(my_order_var,1);
newstart=1;
if M_.occbin.constraint_nbr==1 && length(regimes_(1).regimestart)>1
newstart = regimes_(1).regimestart(end);
end
oldstart=1;
if M_.occbin.constraint_nbr==1 && length(regimes0(1).regimestart)>1
oldstart = regimes0(1).regimestart(end);
end
if M_.occbin.constraint_nbr==1 && (newstart-oldstart)>2 && options_.occbin.filter.use_relaxation
regimestart = max(oldstart+2,round(0.5*(newstart+oldstart)));
regimestart = min(regimestart,oldstart+4);
if regimestart<=regimes_(1).regimestart(end-1)
if length(regimes_(1).regimestart)<=3
regimestart = max(regimestart, min(regimes_(1).regimestart(end-1)+2,newstart));
else
regimes_(1).regime = regimes_(1).regime(1:end-2);
regimes_(1).regimestart = regimes_(1).regimestart(1:end-2);
regimestart = max(regimestart, regimes_(1).regimestart(end-1)+1);
end
end
% % if (newstart-oldstart)>3
% % regimestart = regimes_(1).regimestart(end-1)+oldstart+2;
% % % regimestart = regimes_(1).regimestart(end-1)+round(0.5*(newstart+oldstart))-1;
regimes_(1).regimestart(end)=regimestart;
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state, [base_regime regimes_(1)],myrestrict,T0,R0);
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
elseif newguess==0
TT(:,:,2)=ss.T(my_order_var,my_order_var,1);
RR(:,:,2)=ss.R(my_order_var,:,1);
CC(:,2)=ss.C(my_order_var,1);
end
newguess=0;
regime_hist(niter) = {regimes_(1)};
if M_.occbin.constraint_nbr==1
regime_end(niter) = regimes_(1).regimestart(end);
end
[a, a1, P, P1, v, Fi, Ki, alphahat, etahat, lik] = occbin_kalman_update(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,Ki,Fi,mm,kalman_tol);
lik_hist(niter) = lik;
[a, a1, P, P1, v, Fi, Ki, alphahat, etahat] = occbin_kalman_update(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,Ki,Fi,mm,kalman_tol);
opts_simul.SHOCKS(1,:) = etahat(:,2)';
% if opts_simul.restrict_state_space
% tmp=zeros(M_.endo_nbr,1);
% tmp(dr.restrict_var_list,1)=alphahat(:,1);
% opts_simul.endo_init = tmp(dr.inv_order_var,1);
% else
if opts_simul.restrict_state_space
tmp=zeros(M_.endo_nbr,1);
tmp(dr.restrict_var_list,1)=alphahat(:,1);
@ -219,7 +214,10 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
else
opts_simul.endo_init = alphahat(dr.inv_order_var,1);
end
opts_simul.init_regime=regimes_(1);
% end
if not(options_.occbin.filter.use_relaxation)
opts_simul.init_regime=regimes_(1);
end
if M_.occbin.constraint_nbr==1
myregimestart = [regimes_.regimestart];
else
@ -230,8 +228,6 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if out.error_flag
error_flag = out.error_flag;
etahat=etahat(:,2);
lik=inf;
return;
end
regimes0=regimes_;
@ -240,14 +236,36 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
for kiter=1:niter-1
is_periodic(kiter) = isequal(regime_hist{kiter}, regimes_(1));
end
is_periodic_iter = find(is_periodic);
is_periodic = any(is_periodic);
if is_periodic
if nguess<3 && M_.occbin.constraint_nbr==1
newguess=1;
is_periodic=0;
nguess=nguess+1;
if nguess==1
% change starting regime
regimes_(1).regime=0;
regimes_(1).regimestart=1;
elseif nguess==2
% change starting regime
regimes_(1).regime=[0 1 0];
regimes_(1).regimestart=[1 2 3];
else
regimes_(1).regime=[1 0];
regimes_(1).regimestart=[1 2];
end
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state, [base_regime regimes_(1)],myrestrict,T0,R0);
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
regime_hist = regime_hist(1);
niter=1;
else
% re-set to previous regime
if options_.occbin.filter.periodic_solution
% force projection conditional on most likely regime
[m, im]=min(lik_hist(is_periodic_iter:end));
opts_simul.init_regime=regime_hist{is_periodic_iter+im-1};
regimes_ = regimes0;
% force projection conditional on previous regime
opts_simul.init_regime=regimes0(1);
if M_.occbin.constraint_nbr==1
myregimestart = [regimes0.regimestart];
else
@ -256,39 +274,68 @@ if any(myregime) || ~isequal(regimes_(1),regimes0(1))
opts_simul.periods = max(opts_simul.periods,max(myregimestart));
opts_simul.maxit=1;
options_.occbin.simul=opts_simul;
[~, out, ss] = occbin.solver(M_,oo_,options_);
if out.error_flag
error_flag = out.error_flag;
etahat=etahat(:,2);
lik=inf;
return;
else
regimes_ = out.regime_history;
TT(:,:,2)=ss.T(my_order_var,my_order_var,1);
RR(:,:,2)=ss.R(my_order_var,:,1);
CC(:,2)=ss.C(my_order_var,1);
[a, a1, P, P1, v, Fi, Ki, alphahat, etahat, lik] = occbin_kalman_update(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,Ki,Fi,mm,kalman_tol);
end
else
error_flag = 330;
etahat=etahat(:,2);
lik=inf;
return;
end
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
end
end
end
end
end
error_flag = out.error_flag;
if ~error_flag && niter>options_.occbin.likelihood.max_number_of_iterations && ~isequal(regimes_(1),regimes0(1)) %fixed point algorithm did not converge
if error_flag==0 && niter>options_.occbin.likelihood.max_number_of_iterations && ~isequal(regimes_(1),regimes0(1)) %fixed point algorithm did not converge
error_flag = 1;
if M_.occbin.constraint_nbr==1
% try some other regime before giving up
[ll, il]=sort(regime_end);
rr=regime_hist(il(2:3));
newstart=1;
if length(rr{1}(1).regimestart)>1
newstart = rr{1}(1).regimestart(end)-rr{1}(1).regimestart(end-1)+1;
end
oldstart=1;
if length(rr{2}(1).regimestart)>1
oldstart = rr{2}(1).regimestart(end)-rr{2}(1).regimestart(end-1)+1;
end
nstart=sort([newstart oldstart]);
regimes_=rr{1}(1);
for k=(nstart(1)+1):(nstart(2)-1)
niter=niter+1;
regimes_(1).regimestart(end)=k;
[~,~,~,~,~,~, TTx, RRx, CCx] ...
= occbin.dynare_resolve(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state, [base_regime regimes_(1)],myrestrict,T0,R0);
TT(:,:,2) = TTx(:,:,end);
RR(:,:,2) = RRx(:,:,end);
CC(:,2) = CCx(:,end);
[a, a1, P, P1, v, Fi, Ki, alphahat, etahat] = occbin_kalman_update(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,Ki,Fi,mm,kalman_tol);
opts_simul.SHOCKS(1,:) = etahat(:,2)';
if opts_simul.restrict_state_space
tmp=zeros(M_.endo_nbr,1);
tmp(dr.restrict_var_list,1)=alphahat(:,1);
opts_simul.endo_init = tmp(dr.inv_order_var,1);
else
opts_simul.endo_init = alphahat(dr.inv_order_var,1);
end
if M_.occbin.constraint_nbr==1
myregimestart = [regimes_.regimestart];
else
myregimestart = [regimes_.regimestart1 regimes_.regimestart2];
end
opts_simul.periods = max(opts_simul.periods,max(myregimestart));
options_.occbin.simul=opts_simul;
[~, out, ss] = occbin.solver(M_,options_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state);
if isequal(out.regime_history(1),regimes_(1))
error_flag=0;
break
end
end
regimes_ = out.regime_history;
end
end
if ~error_flag
a = out.piecewise(1:nk+1,my_order_var)' - repmat(out.ys(my_order_var),1,nk+1);
regimes_=regimes_(1:3);
end
regimes_=regimes_(1:3);
a = out.piecewise(1:nk+1,my_order_var)' - repmat(out.ys(my_order_var),1,nk+1);
T = ss.T(my_order_var,my_order_var,:);
R = ss.R(my_order_var,:,:);
C = ss.C(my_order_var,:);
@ -305,7 +352,7 @@ end
end
function [a, a1, P, P1, v, Fi, Ki, alphahat, etahat, lik] = occbin_kalman_update(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,Ki,Fi,mm,kalman_tol)
function [a, a1, P, P1, v, Fi, Ki, alphahat, etahat] = occbin_kalman_update(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,Ki,Fi,mm,kalman_tol)
% [a, a1, P, P1, v, Fi, Ki, alphahat, etahat] = occbin_kalman_update(a,a1,P,P1,data_index,Z,v,Y,H,QQQ,TT,RR,CC,Ki,Fi,mm,kalman_tol)
% - a
% - a1
@ -330,18 +377,9 @@ a(:,t) = a1(:,t);
P1(:,:,t) = T*P(:,:,t-1)*T' + QQ; %transition according to (6.14) in DK (2012)
P(:,:,t) = P1(:,:,t);
di = data_index{t}';
% store info for lik
if not(isempty(di))
vv = Y(di,t) - Z(di,:)*a(:,t);
F = Z(di,:)*P(:,:,t)*Z(di,:)' + diag(H(di));
sig=sqrt(diag(F));
lik=inf;
end
if isempty(di)
Fi(:,t) = 0;
Ki(:,:,t) = 0;
lik =0;
end
for i=di
Zi = Z(i,:);
@ -356,11 +394,6 @@ for i=di
% p. 157, DK (2012)
end
end
if not(isempty(di))
log_dF = log(det(F./(sig*sig')))+2*sum(log(sig));
iF = inv(F./(sig*sig'))./(sig*sig');
lik = log_dF + transpose(vv)*iF*vv + length(di)*log(2*pi);
end
%% do backward pass
ri=zeros(mm,1);
@ -383,4 +416,4 @@ while t > 1
ri = T'*ri; % KD (2003), eq. (23), equation for r_{t-1,p_{t-1}}
end
end
end

331
matlab/+occbin/kalman_update_engine.m
View File

@ -1,331 +0,0 @@
function [ax, a1x, Px, P1x, vx, Tx, Rx, Cx, regx, info, M_, likx, etahat, alphahat, V, Fix, Kix, TTx,RRx,CCx] = ...
kalman_update_engine(a0,a1,P0,P1,t,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,regimes_,base_regime,d_index,M_,...
dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options, Fi,Ki,kalman_tol,nk)
% [ax, a1x, Px, P1x, vx, Tx, Rx, Cx, regx, info, M_, likx, etahat, alphahat, V, Fix, Kix, TTx,RRx,CCx] = kalman_update_engine(
% a0,a1,P0,P1,t,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,regimes_,base_regime,d_index,M_,
% dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options, Fi,Ki,kalman_tol,nk)
% Copyright © 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
is_multivariate = true;
Fix=[];
Kix=[];
TTx=[];
RRx=[];
CCx=[];
V=[];
if nargin>26
is_multivariate = false;
end
use_relaxation = false;
if is_multivariate
[ax, a1x, Px, P1x, vx, Tx, Rx, Cx, regx, info, M_, likx, etahat, alphahat, V] = occbin.kalman_update_algo_1(a0,a1,P0,P1,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,struct(),M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
else
[ax, a1x, Px, P1x, vx, Fix, Kix, Tx, Rx, Cx, regx, info, M_, likx, alphahat, etahat,TTx,RRx,CCx] = occbin.kalman_update_algo_3(a0,a1,P0,P1,data_index,Z,vv,Fi,Ki,Y,H,Qt,T0,R0,TT,RR,CC,struct(),M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
end
info0=info;
if info
if ~isequal(regimes_(1:2),[base_regime base_regime])
if is_multivariate
[ax, a1x, Px, P1x, vx, Tx, Rx, Cx, regx, info, M_, likx, etahat, alphahat, V] = occbin.kalman_update_algo_1(a0,a1,P0,P1,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,regimes_(1:2),M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
else
[ax, a1x, Px, P1x, vx, Fix, Kix, Tx, Rx, Cx, regx, info, M_, likx, alphahat, etahat,TTx,RRx,CCx] = occbin.kalman_update_algo_3(a0,a1,P0,P1,data_index,Z,vv,Fi,Ki,Y,H,Qt,T0,R0,TT,RR,CC,regimes_(1:2),M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
end
end
info1=info;
else
if ~isequal(regimes_(1:2),[base_regime base_regime])
if is_multivariate
[ax1, a1x1, Px1, P1x1, vx1, Tx1, Rx1, Cx1, regx1, info1, M_1, likx1, etahat1, alphahat1, V1] = occbin.kalman_update_algo_1(a0,a1,P0,P1,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,regimes_(1:2),M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
else
[ax1, a1x1, Px1, P1x1, vx1, Fix1, Kix1, Tx1, Rx1, Cx1, regx1, info1, M_1, likx1, alphahat1, etahat1,TTx1,RRx1,CCx1] = occbin.kalman_update_algo_3(a0,a1,P0,P1,data_index,Z,vv,Fi,Ki,Y,H,Qt,T0,R0,TT,RR,CC,regimes_(1:2),M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
end
if info1==0 && likx1<likx
ax=ax1;
a1x=a1x1;
Px=Px1;
P1x=P1x1;
vx=vx1;
Tx=Tx1;
Rx=Rx1;
Cx=Cx1;
regx=regx1;
info=info1;
M_= M_1;
likx=likx1;
etahat=etahat1;
alphahat=alphahat1;
if is_multivariate
V=V1;
else
Fix = Fix1;
Kix = Kix1;
TTx = TTx1;
RRx = RRx1;
CCx = CCx1;
end
end
else
if t>options_.occbin.likelihood.number_of_initial_periods_with_extra_regime_guess
info1=0;
else
% may help in first 2 periods to try some other guess regime, due to
% larger state uncertainty
info1=1;
options_.occbin.likelihood.brute_force_regime_guess = true;
options_.occbin.likelihood.loss_function_regime_guess = true;
end
end
end
diffstart=0;
if info==0
if M_.occbin.constraint_nbr==1
oldstart = regimes_(1).regimestart(end);
newstart = regx(1).regimestart(end);
diffstart = newstart-oldstart;
else
newstart1 = regx(1).regimestart1(end);
newstart2 = regx(1).regimestart2(end);
oldstart1 = regimes_(1).regimestart1(end);
oldstart2 = regimes_(1).regimestart2(end);
diffstart = max(newstart1-oldstart1,newstart2-oldstart2);
end
end
if options_.occbin.filter.use_relaxation && diffstart>2
if info0==0
% make sure we match criteria to enter further solution attempts
info1=1;
end
options_.occbin.likelihood.brute_force_regime_guess = true;
options_.occbin.likelihood.loss_function_regime_guess = true;
use_relaxation = true;
end
if options_.occbin.likelihood.brute_force_regime_guess && (info0 || info1) %|| (info==0 && ~isequal(regx(1),base_regime))
guess_regime = [base_regime base_regime];
options_.occbin.filter.guess_regime = true;
use_index = 0;
if M_.occbin.constraint_nbr==1
for k=1:5
guess_regime(1).regimestart=[1 5 5+4*k];
guess_regime(1).regime=[0 1 0];
if is_multivariate
[ax2{1}, a1x2{1}, Px2{1}, P1x2{1}, vx2{1}, Tx2{1}, Rx2{1}, Cx2{1}, regx2{1}, info2, M_2{1}, likx2{1}, etahat2{1}, alphahat2{1}, V2{1}] = occbin.kalman_update_algo_1(a0,a1,P0,P1,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
else
[ax2{1}, a1x2{1}, Px2{1}, P1x2{1}, vx2{1}, Fix2{1}, Kix2{1}, Tx2{1}, Rx2{1}, Cx2{1}, regx2{1}, info2, M_2{1}, likx2{1}, alphahat2{1}, etahat2{1},TTx2{1},RRx2{1},CCx2{1}] = occbin.kalman_update_algo_3(a0,a1,P0,P1,data_index,Z,vv,Fi,Ki,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
end
if info2==0
use_index= 1;
if not(info==0 && isequal(regx2{1},regx)) && not(use_relaxation && likx2{1}>=likx)
% found a solution, different from previous or
% use_relaxation and likelihood is better
break
end
end
guess_regime(1).regimestart=[1 1+4*k];
guess_regime(1).regime=[1 0];
if is_multivariate
[ax2{2}, a1x2{2}, Px2{2}, P1x2{2}, vx2{2}, Tx2{2}, Rx2{2}, Cx2{2}, regx2{2}, info2, M_2{2}, likx2{2}, etahat2{2}, alphahat2{2}, V2{2}] = occbin.kalman_update_algo_1(a0,a1,P0,P1,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
else
[ax2{2}, a1x2{2}, Px2{2}, P1x2{2}, vx2{2}, Fix2{2}, Kix2{2}, Tx2{2}, Rx2{2}, Cx2{2}, regx2{2}, info2, M_2{2}, likx2{2}, alphahat2{2}, etahat2{2},TTx2{2},RRx2{2},CCx2{2}] = occbin.kalman_update_algo_3(a0,a1,P0,P1,data_index,Z,vv,Fi,Ki,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
end
if info2==0
use_index = 2;
% if use_relaxation and we are here, previous guess did not
% improve solution, so we test for this one
end
if use_index
% in case the second guess does not find a solution!
info2=0;
% a solution was found
break
end
end
end
if M_.occbin.constraint_nbr==2
for jk=0:1 % loop over other regime duration. this loop is shorter for parsimony. one may add an option ...
for k=1:5 % loop over current regime duration
gindex = 0;
for jr=1:2 % loop over current regime 1 or 2
if jr==1
regstart1 = 'regimestart1';
reg1 = 'regime1';
regstart2 = 'regimestart2';
reg2 = 'regime2';
else
regstart1 = 'regimestart2';
reg1 = 'regime2';
regstart2 = 'regimestart1';
reg2 = 'regime1';
end
for kk=1:2 % loop over current regime binding in expectation vs binding in current period
if kk==1
guess_regime(1).(regstart1)=[1 5 5+4*k];
guess_regime(1).(reg1)=[0 1 0];
else
guess_regime(1).(regstart1)=[1 1+4*k];
guess_regime(1).(reg1)=[1 0];
end
for kj=1:1+1*(jk>0)
% loop over other regime slack or binding in current period or binding in
% expectation
if jk==0
% other regime is slack
guess_regime(1).(regstart2) = 1;
guess_regime(1).(reg2) = 0;
else % jk>0
if kj==1
% other regime binding in current period
guess_regime(1).(regstart2)=[1 1+4*jk];
guess_regime(1).(reg2) = [1 0];
else
% other regime binding in expectation
guess_regime(1).(regstart2)=[1 5 5+4*jk];
guess_regime(1).(reg2) = [0 1 0];
end
end
gindex = gindex+1;
if is_multivariate
[ax2{gindex}, a1x2{gindex}, Px2{gindex}, P1x2{gindex}, vx2{gindex}, Tx2{gindex}, Rx2{gindex}, Cx2{gindex}, regx2{gindex}, info2, M_2{gindex}, likx2{gindex}, etahat2{gindex}, alphahat2{gindex}, V2{gindex}] = occbin.kalman_update_algo_1(a0,a1,P0,P1,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
else
[ax2{gindex}, a1x2{gindex}, Px2{gindex}, P1x2{gindex}, vx2{gindex}, Fix2{gindex}, Kix2{gindex}, Tx2{gindex}, Rx2{gindex}, Cx2{gindex}, regx2{gindex}, info2, M_2{gindex}, likx2{gindex}, alphahat2{gindex}, etahat2{gindex},TTx2{gindex},RRx2{gindex},CCx2{gindex}] = occbin.kalman_update_algo_3(a0,a1,P0,P1,data_index,Z,vv,Fi,Ki,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
end
if info2==0
use_index= gindex;
if not(info==0 && isequal(regx2{gindex},regx)) && not(use_relaxation && likx2{gindex}>=likx)
% found a solution, different from previous one
% use_relaxation and likelihood improves
break
end
end
end % loop over other regime slack, binding in expectation or binding in current period
if info2==0
if not(info==0 && isequal(regx2{gindex},regx)) && not(use_relaxation && likx2{gindex}>=likx)
% found a solution, different from previous one
% use_relaxation and likelihood improves
break
end
end
end % loop over current regime binding in expectation vs binding in current period
if info2==0
if not(info==0 && isequal(regx2{gindex},regx)) && not(use_relaxation && likx2{gindex}>=likx)
% found a solution, different from previous one
% use_relaxation and likelihood improves
break
end
end
end % loop over current regime 1 or 2
if use_index
info2=0;
break
end
end % loop over current regime duration
if use_index
break
end
end % loop over other regime duration
end % 2 constraints
if info2==0
% so that we DO NOT enter IVF step
info0=0;
info1=0;
end
if info2==0 && likx2{use_index}<likx
ax=ax2{use_index};
a1x=a1x2{use_index};
Px=Px2{use_index};
P1x=P1x2{use_index};
vx=vx2{use_index};
Tx=Tx2{use_index};
Rx=Rx2{use_index};
Cx=Cx2{use_index};
regx=regx2{use_index};
info=info2;
M_= M_2{use_index};
likx=likx2{use_index};
etahat=etahat2{use_index};
alphahat=alphahat2{use_index};
if is_multivariate
V=V2{use_index};
else
Fix = Fix2{use_index};
Kix = Kix2{use_index};
TTx = TTx2{use_index};
RRx = RRx2{use_index};
CCx = CCx2{use_index};
end
end
options_.occbin.filter.guess_regime = false;
end
if options_.occbin.likelihood.loss_function_regime_guess && (info0 || info1) %|| (info==0 && ~isequal(regx(1),base_regime))
[~, out] = occbin.findmin(d_index, a0, P1, Qt, Y, Z, occbin_options.opts_simul,M_, dr,endo_steady_state,exo_steady_state,exo_det_steady_state, options_);
if out.error_flag==0
options_.occbin.filter.guess_regime = true;
guess_regime=out.regime_history;
guess_regime = [guess_regime base_regime];
if is_multivariate
[ax2, a1x2, Px2, P1x2, vx2, Tx2, Rx2, Cx2, regx2, info2, M_2, likx2, etahat2, alphahat2, V2] = occbin.kalman_update_algo_1(a0,a1,P0,P1,data_index,Z,vv,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options);
else
[ax2, a1x2, Px2, P1x2, vx2, Fix2, Kix2, Tx2, Rx2, Cx2, regx2, info2, M_2, likx2, alphahat2, etahat2,TTx2,RRx2,CCx2] = occbin.kalman_update_algo_3(a0,a1,P0,P1,data_index,Z,vv,Fi,Ki,Y,H,Qt,T0,R0,TT,RR,CC,guess_regime,M_,dr,endo_steady_state,exo_steady_state,exo_det_steady_state,options_,occbin_options,kalman_tol,nk);
end
options_.occbin.filter.guess_regime = false;
if info2==0 && likx2<likx
ax=ax2;
a1x=a1x2;
Px=Px2;
P1x=P1x2;
vx=vx2;
Tx=Tx2;
Rx=Rx2;
Cx=Cx2;
regx=regx2;
info=info2;
likx=likx2;
M_= M_2;
etahat=etahat2;
alphahat=alphahat2;
if is_multivariate
V=V2;
else
Fix = Fix2;
Kix = Kix2;
TTx = TTx2;
RRx = RRx2;
CCx = CCx2;
end
end
end
end
end

3
matlab/+occbin/map_regime.m
View File

@ -26,9 +26,6 @@ function [regime, regime_start, error_flag]=map_regime(binding_indicator,debug_s
% Journal of Monetary Economics 70, 22-38
error_flag=0;
if isempty(binding_indicator)
binding_indicator = false;
end
% analyse violvec and isolate contiguous periods in the other regime.
regime(1) = binding_indicator(1);
regime_index = 1;

12
matlab/+occbin/mkdatap_anticipated_2constraints_dyn.m
View File

@ -98,7 +98,11 @@ if T_max > 0
% check if last binding regime was already stored
tmp = 0*binding_indicator;
tmp(1:end-T_max+1,:) = binding_indicator(T_max:end,:);
itmp = find(~any(dictionary.binding_indicator(1:length(tmp)*2,:)-tmp(:)));
if ~isoctave && matlab_ver_less_than('9.1') % Automatic broadcasting was introduced in MATLAB R2016b
itmp = find(~any(bsxfun(@minus, dictionary.binding_indicator(1:length(tmp)*2,:), tmp(:))));
else
itmp = find(~any(dictionary.binding_indicator(1:length(tmp)*2,:)-tmp(:)));
end
if ~isempty(itmp)
ireg(T_max) = itmp;
else
@ -130,7 +134,11 @@ if T_max > 0
for i = T_max-1:-1:1
tmp = 0*binding_indicator;
tmp(1:end-i+1,:) = binding_indicator(i:end,:);
itmp = find(~any(dictionary.binding_indicator(1:length(tmp)*2,:)-tmp(:)));
if ~isoctave && matlab_ver_less_than('9.1') % Automatic broadcasting was introduced in MATLAB R2016b
itmp = find(~any(bsxfun(@minus, dictionary.binding_indicator(1:length(tmp)*2,:), tmp(:))));
else
itmp = find(~any(dictionary.binding_indicator(1:length(tmp)*2,:)-tmp(:)));
end
if ~isempty(itmp)
ireg(i) = itmp;
else

6
matlab/+occbin/mkdatap_anticipated_dyn.m
View File

@ -89,7 +89,11 @@ if T_max > 0
tmp = 0*binding_indicator;
tmp(1:end-i+1) = binding_indicator(i:end);
itmp = find(~any(dictionary.binding_indicator-tmp));
if ~isoctave && matlab_ver_less_than('9.1') % Automatic broadcasting was introduced in MATLAB R2016b
itmp = find(~any(bsxfun(@minus, dictionary.binding_indicator, tmp)));
else
itmp = find(~any(dictionary.binding_indicator-tmp));
end
if ~isempty(itmp)
ireg(i) = itmp;
else

179
matlab/+occbin/plot_irfs.m
View File

@ -1,179 +0,0 @@
function plot_irfs(M_,irfs,options_,var_list)
% 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
%
% 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/>.
if (isfield(options_,'irf_shocks')==0)
shock_names = M_.exo_names;
else
shock_names = options_.irf_shocks;
end
simul_name = options_.occbin.plot_irf.simulname;
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;
steps_irf = 1;
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)
itemp = strmatch(shock_names{var_iter},M_.exo_names,'exact');
if isempty(itemp)
error(['Shock ',shock_names{var_iter},' is not defined!'])
else
iexo=[iexo, itemp];
end
end
ncols = options_.occbin.plot_irf.ncols;
nrows = options_.occbin.plot_irf.nrows;
npan = ncols*nrows;
shocksigns = options_.occbin.plot_irf.shocksigns;
threshold = options_.impulse_responses.plot_threshold;
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)
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, .
screensize = get( groot, 'Screensize' );
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;
if isempty(endo_scaling_factor)
exofactor = 1;
else
exofactor = endo_scaling_factor{var_iter};
end
subplot(nrows,ncols,isub)
irf_field = strcat(vars_irf{var_iter},'_',shock_names{shock_iter},'_',shocksign);
%%linear IRFs
if ~isfield(irfs.linear,irf_field)
warning('occbin.plot_irfs: no linear IRF for %s to %s detected',vars_irf{var_iter,1},shock_names{shock_iter})
else
irfvalues = irfs.linear.(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_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)
warning('occbin.plot_irfs: no piecewise linear IRF for %s to %s detected',vars_irf{var_iter,1},shock_names{shock_iter})
else
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
plot(irfvalues(1:steps_irf:max(max_irf_length_1,max_irf_length_2))*0,'k-','linewidth',1.5);
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
% Annotation Box + save figure
% ----------------------
if mod(j1,npan)==0 || (mod(j1,npan)~=0 && var_iter==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);
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
if options_.TeX && any(strcmp('eps',cellstr(options_.graph_format)))
fprintf(fidTeX,'%% End Of TeX file.');
fclose(fidTeX);
end

86
matlab/+occbin/plot_regimes.m
View File

@ -1,86 +0,0 @@
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;
if nconstr ==1
regime(1) = {'regime'};
regimestart(1) = {'regimestart'};
else
regime(1) = {'regime1'};
regimestart(1) = {'regimestart1'};
regime(2) = {'regime2'};
regimestart(2) = {'regimestart2'};
end
GraphDirectoryName = CheckPath('graphs',M_.dname);
fhandle = dyn_figure(options_.nodisplay,'Name',[M_.fname ': OccBin regimes']);
latexFolder = CheckPath('latex',M_.dname);
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)
for t=1:nperiods
nregimes_in_t = length(regimes(t).(regime{k}));
start_periods = regimes(t).(regimestart{k});
start_periods = [start_periods max(start_periods)];
for r=1:nregimes_in_t
isconstrained = regimes(t).(regime{k})(r);
if isconstrained
plot(t,start_periods(r),'*r')
hold all,
plot([t t],start_periods(r:r+1),'-r')
else
plot(t,start_periods(r),'ob')
hold all,
plot([t t],start_periods(r:r+1),'-b')
end
end
end
xlim([1 nperiods])
title(['Regime ' int2str(k)])
xlabel('Historic period')
ylabel('Regime: expected start')
end
annotation('textbox',[.25,0,.15,.05],'String','Slack','Color','blue');
annotation('textbox',[.65,0,.2,.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

33
matlab/+occbin/set_default_options.m
View File

@ -42,35 +42,30 @@ if ismember(flag,{'all'})
end
if ismember(flag,{'filter','all'})
options_occbin_.filter.state_covariance = false;
options_occbin_.filter.guess_regime = false;
options_occbin_.filter.periodic_solution = true;
options_occbin_.filter.use_relaxation = false;
end
if ismember(flag,{'forecast','all'})
options_occbin_.forecast.check_ahead_periods=30;
options_occbin_.forecast.debug_flag=false;
options_occbin_.forecast.frcst_regimes=[];
options_occbin_.forecast.maxit=30;
options_occbin_.forecast.periods=30;
options_occbin_.forecast.qmc=0;
options_occbin_.forecast.replic=0;
options_occbin_.forecast.SHOCKS0=[];
options_occbin_.forecast.treepath=1; % number of branches
end
if ismember(flag,{'irf','all'})
options_occbin_.irf.check_ahead_periods=30;
options_occbin_.irf.exo_names=M_.exo_names;
if ismember(flag,{'irf','all'})
options_occbin_.irf.init_regime=[];
options_occbin_.irf.maxit=30;
options_occbin_.irf.threshold = 10^-6;
% options_occbin_.irf.periods=options_.irf;
options_occbin_.irf.shocksize=[];
options_occbin_.irf.shocksigns = {'pos','neg'};
options_occbin_.irf.t0=0;
options_occbin_.irf.shocksigns = {'1','_1'};
end
if ismember(flag,{'likelihood','all'})
options_occbin_.likelihood.brute_force_regime_guess = true;
options_occbin_.likelihood.curb_retrench = false;
options_occbin_.likelihood.first_period_occbin_update = 1;
options_occbin_.likelihood.full_output = false;
@ -79,12 +74,10 @@ if ismember(flag,{'likelihood','all'})
options_occbin_.likelihood.init_binding_indicator = false(0);
options_occbin_.likelihood.inversion_filter = false;
options_occbin_.likelihood.IVF_shock_observable_mapping = [];
options_occbin_.likelihood.loss_function_regime_guess = false;
options_occbin_.likelihood.maxit = 30; % this is for occbin solver algo
options_occbin_.likelihood.max_number_of_iterations = 10; % this is for occbin_kalman_update loop
options_occbin_.likelihood.max_check_ahead_periods=inf;
options_occbin_.likelihood.number_of_initial_periods_with_extra_regime_guess=0;
options_occbin_.likelihood.periods = 3;
options_occbin_.likelihood.periods = 100;
options_occbin_.likelihood.check_ahead_periods=200;
options_occbin_.likelihood.periodic_solution=false;
options_occbin_.likelihood.piecewise_only = true;
@ -94,16 +87,6 @@ if ismember(flag,{'likelihood','all'})
options_occbin_.likelihood.waitbar=false;
end
if ismember(flag,{'plot_irf','all'})
options_occbin_.plot_irf.add_steadystate = 0;
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='';
end
if ismember(flag,{'plot_shock_decomp','all'})
options_occbin_.plot_shock_decomp.add_steadystate = false;
options_occbin_.plot_shock_decomp.add_zero_line = false;
@ -192,8 +175,6 @@ if ismember(flag,{'simul','all'})
options_occbin_.simul.periods=100;
options_occbin_.simul.check_ahead_periods=200;
options_occbin_.simul.periodic_solution=false;
options_occbin_.simul.periodic_solution_threshold=1;
options_occbin_.simul.periodic_solution_strict=true;
options_occbin_.simul.piecewise_only = false;
options_occbin_.simul.reset_check_ahead_periods_in_new_period = false;
options_occbin_.simul.reset_regime_in_new_period = false;
@ -216,7 +197,7 @@ if ismember(flag,{'smoother','all'})
options_occbin_.smoother.maxit = 30; % this is for occbin solver algo
options_occbin_.smoother.max_check_ahead_periods=inf;
options_occbin_.smoother.max_number_of_iterations = 10; % this is for smoother loop
options_occbin_.smoother.periods = 3;
options_occbin_.smoother.periods = 100;
options_occbin_.smoother.check_ahead_periods=200;
options_occbin_.smoother.periodic_solution=false;
options_occbin_.smoother.piecewise_only = true;

26
matlab/+occbin/solve_one_constraint.m
View File

@ -136,8 +136,6 @@ for shock_period = 1:n_shocks_periods
binding_indicator_history={};
max_err = NaN(max_iter,1);
regime_violates_constraint_in_expectation = false(max_iter,1);
number_of_periods_with_violations = zeros(max_iter,1);
regime_exit_period = ones(max_iter,1);
while (regime_change_this_iteration && iter<max_iter && ~is_periodic && ~is_periodic_loop)
iter = iter +1;
@ -196,11 +194,9 @@ for shock_period = 1:n_shocks_periods
if ~isinf(opts_simul_.max_check_ahead_periods) && opts_simul_.max_check_ahead_periods<length(binding_indicator)
end_periods = opts_simul_.max_check_ahead_periods;
last_indicator = false(length(binding_indicator)-end_periods,1);
regime_violates_constraint_in_last_period(iter) = any(binding.constraint_1(end_periods+1:end));
else
end_periods = length(binding_indicator);
last_indicator = false(0);
regime_violates_constraint_in_last_period(iter) = binding.constraint_1(end_periods);
end
% check if changes to the hypothesis of the duration for each
% regime
@ -219,9 +215,6 @@ for shock_period = 1:n_shocks_periods
% if max_check_ahead_periods<inf, enforce unconstrained regime for period larger than max_check_ahead_periods
binding_constraint_new=[binding.constraint_1(1:end_periods);last_indicator];
relaxed_constraint_new = [relax.constraint_1(1:end_periods);not(last_indicator)];
number_of_periods_with_violations(iter) = sum(binding_indicator -((binding_indicator | binding_constraint_new) & ~(binding_indicator & relaxed_constraint_new)));
regime_exit_period(iter) = max(regime_history(shock_period).regimestart);
if curb_retrench % apply Gauss-Seidel idea of slowing down the change in the guess
% for the constraint -- only relax one
% period at a time starting from the last
@ -259,35 +252,20 @@ for shock_period = 1:n_shocks_periods
if size(binding_indicator,1)== size(binding_indicator_history{kiter},1)
% vvv = [binding_indicator_history{kiter}; false(size(binding_indicator,1)- size(binding_indicator_history{kiter},1), 1)];
% is_periodic(kiter) = isequal(vvv, binding_indicator);
is_periodic(kiter) = isequal(binding_indicator_history{kiter}, binding_indicator) && sum(binding_indicator_history{iter}-binding_indicator)<=opts_simul_.periodic_solution_threshold;
is_periodic(kiter) = isequal(binding_indicator_history{kiter}, binding_indicator) && length(find(binding_indicator_history{iter}-binding_indicator))==1;
else
is_periodic(kiter)=false;
end
end
is_periodic_all =is_periodic;
is_periodic = any(is_periodic);
is_periodic_strict = is_periodic;
if is_periodic_loop && ~is_periodic
if ~opts_simul_.periodic_solution_strict && any(number_of_periods_with_violations(~regime_violates_constraint_in_expectation(1:iter))<=opts_simul_.periodic_solution_threshold)
is_periodic=true;
is_periodic_all=false(size(is_periodic_loop_all));
is_periodic_all(1) = true; % here we consider all guesses and pick the best one according to the criteria below
else
do_nothing=true;
end
end
if is_periodic && periodic_solution
inx = find(is_periodic_all,1):iter;
inx1 = inx(find(~regime_violates_constraint_in_expectation(inx)));
if not(isempty(inx1))
inx=inx1;
end
if is_periodic_strict || isempty(inx1)
[merr,imerr]=min(max_err(inx));
else
[merr,imerr]=min(number_of_periods_with_violations(inx));
end
[merr,imerr]=min(max_err(inx));
inx = inx(imerr);
binding_indicator=binding_indicator_history{inx};
if inx<iter

28
matlab/+occbin/solve_two_constraints.m
View File

@ -228,7 +228,7 @@ for shock_period = 1:n_shocks_periods
err_binding_constraint_new = [err.binding_constraint_1(1:end_periods); err.binding_constraint_2(1:end_periods)];
err_relaxed_constraint_new = [err.relax_constraint_1(1:end_periods); err.relax_constraint_2(1:end_periods)];
% check if changes_
if any(binding_constraint_new & ~my_binding_indicator(:)) || any(relaxed_constraint_new & my_binding_indicator(:))
err_violation = err_binding_constraint_new(binding_constraint_new & ~my_binding_indicator(:));
@ -243,11 +243,6 @@ for shock_period = 1:n_shocks_periods
binding_constraint_new=[binding.constraint_1(1:end_periods);last_indicator; binding.constraint_2(1:end_periods);last_indicator];
relaxed_constraint_new = [relax.constraint_1(1:end_periods);not(last_indicator); relax.constraint_2(1:end_periods);not(last_indicator)];
tmp_nper(1) = sum(binding_indicator(:,1) - (binding_indicator(:,1) | [binding.constraint_1(1:end_periods);last_indicator]) & ~(binding_indicator(:,1) & [relax.constraint_1(1:end_periods);not(last_indicator)]));
tmp_nper(2) = sum(binding_indicator(:,2) - (binding_indicator(:,2) | [binding.constraint_2(1:end_periods);last_indicator]) & ~(binding_indicator(:,2) & [relax.constraint_2(1:end_periods);not(last_indicator)]));
number_of_periods_with_violations(iter) = max(tmp_nper);
regime_exit_period(iter,1) = max(regime_history(shock_period).regimestart1);
regime_exit_period(iter,2) = max(regime_history(shock_period).regimestart2);
if curb_retrench % apply Gauss-Seidel idea of slowing down the change in the guess
% for the constraint -- only relax one
% period at a time starting from the last
@ -281,7 +276,7 @@ for shock_period = 1:n_shocks_periods
is_periodic_loop(kiter) = false;
end
end
is_periodic_loop_all =is_periodic_loop;
% is_periodic_loop_all =is_periodic_loop;
is_periodic_loop = any(is_periodic_loop);
% only accept periodicity where regimes differ by one
% period!
@ -290,35 +285,20 @@ for shock_period = 1:n_shocks_periods
if size(binding_indicator,1)== size(binding_indicator_history{kiter},1)
% vvv = [binding_indicator_history{kiter}; false(size(binding_indicator,1)- size(binding_indicator_history{kiter},1), 1)];
% is_periodic(kiter) = isequal(vvv, binding_indicator);
is_periodic(kiter) = isequal(binding_indicator_history{kiter}, binding_indicator) && sum(binding_indicator_history{iter}(:,1)-binding_indicator(:,1))<=opts_simul_.periodic_solution_threshold && sum(binding_indicator_history{iter}(:,2)-binding_indicator(:,2))<=opts_simul_.periodic_solution_threshold;
is_periodic(kiter) = isequal(binding_indicator_history{kiter}, binding_indicator) && length(find(binding_indicator_history{iter}(:,1)-binding_indicator(:,1)))<=1 && length(find(binding_indicator_history{iter}(:,2)-binding_indicator(:,2)))<=1;
else
is_periodic(kiter)=false;
end
end
is_periodic_all = is_periodic;
is_periodic = any(is_periodic);
is_periodic_strict = is_periodic;
if is_periodic_loop && ~is_periodic
if ~opts_simul_.periodic_solution_strict && any(number_of_periods_with_violations(~regime_violates_constraint_in_expectation(1:iter))<opts_simul_.periodic_solution_threshold)
is_periodic=true;
is_periodic_all=false(size(is_periodic_loop_all));
is_periodic_all(1) = true; % here we consider all guesses and pick the best one according to the criteria below
else
do_nothing=true;
end
end
if is_periodic && periodic_solution
inx = find(is_periodic_all,1):iter;
inx1 = inx(find(~regime_violates_constraint_in_expectation(inx)));
if not(isempty(inx1))
inx=inx1;
end
if is_periodic_strict || isempty(inx1)
[min_err,index_min_err]=min(max_err(inx));
else
[min_err,index_min_err]=min(number_of_periods_with_violations(inx));
end
[min_err,index_min_err]=min(max_err(inx));
inx = inx(index_min_err);
binding_indicator=binding_indicator_history{inx}; %select regime history with same result, but smallest error
if inx<iter %update if needed

48
matlab/+occbin/solver.m
View File

@ -1,28 +1,25 @@
function [dr, out, ss] = solver(M_, options_, dr ,steady_state, exo_steady_state, exo_det_steady_state)
% [dr, out, ss] = solver(M_,oo_,options_, dr ,steady_state, exo_steady_state, exo_det_steady_state
% function [oo_, out, ss] = solver(M_,oo_,options_, dr ,steady_state, exo_steady_state, exo_det_steady_state
% Solves the model with an OBC and produces simulations/IRFs
%
% INPUT:
% - M_ [structure] Matlab's structure describing the model
% - options_ [structure] Matlab's structure containing the options
% - dr [structure] model information structure
% - endo_steady_state [vector] steady state value for endogenous variables
% - exo_steady_state [vector] steady state value for exogenous variables
% - exo_det_steady_state [vector] steady state value for exogenous deterministic variables
% - M_ [structure] Matlab's structure describing the model
% - oo_ [structure] Matlab's structure containing the results
% - options_ [structure] Matlab's structure containing the options
%
% OUTPUT:
% - dr [structure] decision rules
% - out [structure] simulation result containing fields:
% - linear: paths for endogenous variables ignoring OBC (linear solution)
% - piecewise: paths for endogenous variables satisfying the OBC (occbin/piecewise solution)
% - ys: vector of steady state values
% - regime_history: information on number and time of regime transitions
% - ss [structure] State space solution
% - T: [n_vars by n_vars by n_shock_period] array of transition matrices
% - R: [n_vars by n_exo by n_shock_period] array of shock response matrices
% - C: [n_vars by n_shock_period] array of constants
% - oo_ [structure] Matlab's structure containing the results
% - out [structure] simulation result containing fields:
% - linear: paths for endogenous variables ignoring OBC (linear solution)
% - piecewise: paths for endogenous variables satisfying the OBC (occbin/piecewise solution)
% - ys: vector of steady state values
% - regime_history: information on number and time of regime transitions
% - ss [structure] State space solution
% - T: [n_vars by n_vars by n_shock_period] array of transition matrices
% - R: [n_vars by n_exo by n_shock_period] array of shock response matrices
% - C: [n_vars by n_shock_period] array of constants
% Copyright © 2021-2023 Dynare Team
% Copyright © 2021 Dynare Team
%
% This file is part of Dynare.
%
@ -53,7 +50,6 @@ else
end
end
ss=struct();
if solve_dr
if isempty(options_.qz_criterium)
options_.qz_criterium = 1+1e-6;
@ -94,7 +90,15 @@ end
% add back steady state
if ~options_.occbin.simul.piecewise_only
out.linear = out.linear + out.ys';
if ~isoctave && matlab_ver_less_than('9.1') % Automatic broadcasting was introduced in MATLAB R2016b
out.linear = bsxfun(@plus, out.linear, out.ys');
else
out.linear = out.linear + out.ys';
end
end
out.piecewise = out.piecewise + out.ys';
out.exo_pos = options_.occbin.simul.exo_pos;
if ~isoctave && matlab_ver_less_than('9.1') % Automatic broadcasting was introduced in MATLAB R2016b
out.piecewise = bsxfun(@plus, out.piecewise, out.ys');
else
out.piecewise = out.piecewise + out.ys';
end
out.exo_pos = options_.occbin.simul.exo_pos;

44
matlab/+occbin/squeeze_shock_decomposition.m
View File

@ -1,44 +0,0 @@
function [oo_,options_] = squeeze_shock_decomposition(M_,oo_,options_, sd_vlist)
if isstruct(options_.plot_shock_decomp.q2a)
avname=char({options_.plot_shock_decomp.q2a.qname});
sda = options_.plot_shock_decomp.q2a(ismember(avname,sd_vlist,'rows'));
for k=1:length(sda)
if isstruct(sda(k).aux)
sd_vlist = [sd_vlist; cellstr(sda(k).aux.y)];
end
end
end
i_var = varlist_indices(sd_vlist,M_.endo_names);
sd_vlist = M_.endo_names(i_var);
% first we squeeze usual fields
oo_ = squeeze_shock_decomposition(M_,oo_,options_,sd_vlist);
i_var = oo_.shock_decomposition_info.i_var;
sd_vlist = M_.endo_names(i_var);
% now we check for occbin SDs
options_.occbin.shock_decomp.i_var = i_var;
if isfield (oo_.occbin.smoother,'decomp')
oo_.occbin.smoother.decomp = oo_.occbin.smoother.decomp(i_var,:,:);
oo_.occbin.smoother.wdecomp = oo_.occbin.smoother.wdecomp(i_var,:,:);
end
if isfield(oo_.occbin,'shock_decomp')
fnames = fieldnames(oo_.occbin.shock_decomp);
for k=1:length(fnames)
nendo = numel(oo_.occbin.shock_decomp.(fnames{k}).vname);
tmp_i_var = varlist_indices(sd_vlist,char(oo_.occbin.shock_decomp.(fnames{k}).vname));
oo_.occbin.shock_decomp.(fnames{k}).vname = cellstr(sd_vlist);
tmpnames = fieldnames(oo_.occbin.shock_decomp.(fnames{k}));
for t=1:length(tmpnames)
if size(oo_.occbin.shock_decomp.(fnames{k}).(tmpnames{t}),3)==nendo
oo_.occbin.shock_decomp.(fnames{k}).(tmpnames{t})= oo_.occbin.shock_decomp.(fnames{k}).(tmpnames{t})(:,:,tmp_i_var);
end
end
end
end
end

15
matlab/+occbin/write_regimes_to_xls.m
View File

@ -7,7 +7,7 @@ function write_regimes_to_xls(occbin_struct,M_,options_)
% - M_ [struct] Matlab's structure describing the model
% - options_ [struct] Matlab's structure describing the current options
% Copyright © 2021-2023 Dynare Team
% Copyright © 2021 Dynare Team
%
% This file is part of Dynare.
%
@ -62,7 +62,14 @@ else
end
end
filename=[OutputDirectoryName filesep xls_filename '.xls'];
if ~ispc && ~isoctave && matlab_ver_less_than('9.0')
% On GNU/Linux and macOS, with MATLAB < R2016a, “writeable” can’t write Excel files
% (and “xlswrite” can’t either)
warning('This version of MATLAB is too old and cannot create Excel files. The Occbin regimes will rather be written to a CSV file.')
filename=[OutputDirectoryName filesep xls_filename '.csv'];
else
filename=[OutputDirectoryName filesep xls_filename '.xls'];
end
if isfile(filename)
delete(filename)
@ -74,6 +81,10 @@ if isoctave
error('The io package is required to write XLS files from Octave')
end
xlswrite(filename, vertcat(Header, xlsmat));
elseif ~ispc && matlab_ver_less_than('9.0')
% Use a CSV file. See the comment above about filename.
% We don’t use xlswrite because its CSV fallback does not support cell-arrays.
writetable(array2table(xlsmat,'VariableNames',Header), filename);
else
writetable(array2table(xlsmat,'VariableNames',Header), filename, 'Sheet', 'Regimes');
end

4
matlab/+osr/objective.m
View File

@ -64,9 +64,9 @@ if ~options_.analytic_derivation
loss = full(weights(:)'*vx(:));
else
totparam_nbr=length(i_params);
oo_.dr.derivs = identification.get_perturbation_params_derivs(M_, options_, [], oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, i_params, [], [], 0); %analytic derivatives of perturbation matrices
oo_.dr.derivs = get_perturbation_params_derivs(M_, options_, [], oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, i_params, [], [], 0); %analytic derivatives of perturbation matrices
pruned_state_space = pruned_SS.pruned_state_space_system(M_, options_, oo_.dr, i_var, 0, 0, 1);
pruned_state_space = pruned_state_space_system(M_, options_, oo_.dr, i_var, 0, 0, 1);
vx = pruned_state_space.Var_y + pruned_state_space.E_y*pruned_state_space.E_y';
dE_yy = pruned_state_space.dVar_y;
for jp=1:length(i_params)

2
matlab/+pac/+estimate/init.m
View File

@ -18,7 +18,7 @@ function [pacmodl, lhs, rhs, pnames, enames, xnames, rname, pid, eid, xid, pname
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% Get the original equation to be estimated
[~, RHS] = get_lhs_and_rhs(eqname, M_, true);
[LHS, RHS] = get_lhs_and_rhs(eqname, M_, true);
% Check that the equation has a PAC expectation term.
if ~contains(RHS, 'pac_expectation', 'IgnoreCase', true)

2
matlab/+pac/+estimate/iterative_ols.m
View File

@ -41,7 +41,7 @@ function iterative_ols(eqname, params, data, range)
global M_ oo_ options_
[pacmodl, ~, rhs, ~, ~, ~, rname, ~, ~, ~, ~, ipnames_, params, data] = ...
[pacmodl, ~, rhs, ~, ~, ~, rname, ~, ~, ~, ~, ipnames_, params, data, ~] = ...
pac.estimate.init(M_, oo_, eqname, params, data, range);
% Set initial condition.

28
matlab/+pac/+estimate/nls.m
View File

@ -42,7 +42,7 @@ function nls(eqname, params, data, range, optimizer, varargin)
% is available only if the matylab optimization toolbox is installed), the
% remaining inputs are the options (key/value) passed to the optimizers.
% Copyright © 2018-2023 Dynare Team
% Copyright © 2018-2022 Dynare Team
%
% This file is part of Dynare.
%
@ -96,6 +96,12 @@ write_residuals_routine(lhs, rhs, eqname, ipnames_, M_, pacmodl);
% Create a routine for evaluating the sum of squared residuals of the nonlinear model
write_ssr_routine(lhs, rhs, eqname, ipnames_, M_, pacmodl);
% Workaround for Octave bug https://savannah.gnu.org/bugs/?46282
% Octave will randomly fail to read the ssr_* file generated in the +folder
if isoctave && octave_ver_less_than('7')
path(path)
end
% Copy (sub)sample data in a matrix.
DATA = data([range(1)-1, range]).data;
@ -200,23 +206,23 @@ if isnan(ssr0) || isinf(ssr0) || ~isreal(ssr0)
end
if is_gauss_newton
[params1, SSR] = gauss_newton(resfun, params0);
[params1, SSR, exitflag] = gauss_newton(resfun, params0);
elseif is_lsqnonlin
if ismember('levenberg-marquardt', varargin)
% Levenberg Marquardt does not handle boundary constraints.
[params1, SSR] = lsqnonlin(resfun, params0, [], [], optimset(varargin{:}));
[params1, SSR, ~, exitflag] = lsqnonlin(resfun, params0, [], [], optimset(varargin{:}));
else
[params1, SSR] = lsqnonlin(resfun, params0, bounds(:,1), bounds(:,2), optimset(varargin{:}));
[params1, SSR, ~, exitflag] = lsqnonlin(resfun, params0, bounds(:,1), bounds(:,2), optimset(varargin{:}));
end
else
% Estimate the parameters by minimizing the sum of squared residuals.
[params1, SSR] = dynare_minimize_objective(ssrfun, params0, ...
minalgo, ...
options_, ...
bounds, ...
parameter_names, ...
[], ...
[]);
[params1, SSR, exitflag] = dynare_minimize_objective(ssrfun, params0, ...
minalgo, ...
options_, ...
bounds, ...
parameter_names, ...
[], ...
[]);
end
% Revert local modifications to the options.

15
matlab/+pac/+update/parameters.m
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@ -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

2
matlab/+pac/check.m
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@ -40,7 +40,7 @@ end
errorcode = 0;
% Get the original equation to be estimated
[~, RHS] = get_lhs_and_rhs(eqname, M_, true);
[LHS, RHS] = get_lhs_and_rhs(eqname, M_, true);
% Check that the equation has a PAC expectation term.
if ~contains(RHS, 'pac_expectation', 'IgnoreCase', true)

2
matlab/+pruned_SS/Q6_plication.m
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@ -49,7 +49,7 @@ for i1=1:p
for i4=i3:p
for i5=i4:p
for i6=i5:p
idx = pruned_SS.uperm([i6 i5 i4 i3 i2 i1]);
idx = uperm([i6 i5 i4 i3 i2 i1]);
for r = 1:size(idx,1)
ii1 = idx(r,1); ii2= idx(r,2); ii3=idx(r,3); ii4=idx(r,4); ii5=idx(r,5); ii6=idx(r,6);
n = ii1 + (ii2-1)*p + (ii3-1)*p^2 + (ii4-1)*p^3 + (ii5-1)*p^4 + (ii6-1)*p^5;

6
matlab/+pruned_SS/allVL1.m
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@ -78,14 +78,14 @@ end
Counter(0);
switch L1ops
case {'==' '='}
case {'==' '='},
if isnan(MaxCounter)
% return the number of solutions
v = nchoosek(n+L1-1,L1); % nchoosek(n+L1-1,n-1)
else
v = allVL1eq(n, L1);
end
case '<=' % call allVL1eq for various sum targets
case '<=', % call allVL1eq for various sum targets
if isnan(MaxCounter)
% return the number of solutions
%v = nchoosek(n+L1,L1)*factorial(n-L1); BUG <- 16/Sep/2009:
@ -99,7 +99,7 @@ switch L1ops
v = cell2mat(arrayfun(@(j) allVL1eq(n, j), (0:L1)', ...
'UniformOutput', false));
end
case '<'
case '<',
v = allVL1(n, L1-1, '<=', MaxCounter);
otherwise
error('allVL1: unknown L1ops')

4
matlab/+pruned_SS/bivmom.m
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@ -76,8 +76,8 @@ if odd
end
y = y*rho;
end
y = prod(1:2:s1)*prod(1:2:s2)*y;
y = prod([1:2:s1])*prod([1:2:s2])*y;
if nargout > 1
dy = prod(1:2:s1)*prod(1:2:s2)*dy;
dy = prod([1:2:s1])*prod([1:2:s2])*dy;
end

8
matlab/+pruned_SS/prodmom.m
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@ -21,7 +21,7 @@
% Multivariate Analysis, 2008, vol. 99, issue 3, pages 542-554.
% =========================================================================
% Copyright © 2008-2015 Raymond Kan <kan@chass.utoronto.ca>
% Copyright © 2019-2023 Dynare Team
% Copyright © 2019-2020 Dynare Team
%
% This file is part of Dynare.
%
@ -38,7 +38,7 @@
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
% =========================================================================
function y = prodmom(V,ii,nu)
function y = prodmom(V,ii,nu);
if nargin<3
nu = ones(size(ii));
end
@ -61,7 +61,7 @@ s2 = s/2;
% Use univariate normal results
%
if m==1
y = V^s2*prod(1:2:s-1);
y = V^s2*prod([1:2:s-1]);
return
end
%
@ -104,4 +104,4 @@ for i=1:fix(prod(nu+1)/2)
end
end
end
y = y/prod(1:s2);
y = y/prod([1:s2]);

8
matlab/+pruned_SS/prodmom_deriv.m
View File

@ -82,9 +82,9 @@ s2 = s/2;
% Use univariate normal results
%
if m==1
y = V^s2*prod(1:2:s-1);
y = V^s2*prod([1:2:s-1]);
if nargout > 1
dy = s2*V^(s2-1)*dV*prod(1:2:s-1);
dy = s2*V^(s2-1)*dV*prod([1:2:s-1]);
dy = reshape(dy,1,size(dV,3));
end
return
@ -169,8 +169,8 @@ for i=1:fix(prod(nu+1)/2)
end
end
end
y = y/prod(1:s2);
y = y/prod([1:s2]);
if nargout > 1
dy = dy/prod(1:s2);
dy = dy/prod([1:s2]);
dy = reshape(dy,1,size(dV,3));
end

2
matlab/+pruned_SS/quadruplication.m
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@ -49,7 +49,7 @@ for l=1:p
for k=l:p
for j=k:p
for i=j:p
idx = pruned_SS.uperm([i j k l]);
idx = uperm([i j k l]);
for r = 1:size(idx,1)
ii = idx(r,1); jj= idx(r,2); kk=idx(r,3); ll=idx(r,4);
n = ii + (jj-1)*p + (kk-1)*p^2 + (ll-1)*p^3;

146
matlab/@dprior/admissible.m
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@ -1,146 +0,0 @@
function b = admissible(o, d)
% Return true iff d is an admissible draw in a distribution characterized by o.
%
% INPUTS
% - o [dprior] Distribution specification for a n×1 vector of independent continuous random variables
% - d [double] n×1 vector.
%
% OUTPUTS
% - b [logical] scalar.
%
% REMARKS
% None.
%
% EXAMPLE
%
% >> Prior = dprior(bayestopt_, options_.prior_trunc);
% >> d = Prior.draw()
% >> Prior.admissible(d)
% ans =
%
% logical
%
% 1
% Copyright © 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/>.
b = false;
if ~isequal(length(d), length(o.lb))
return
end
if all(d>=o.lb & d<=o.ub)
b = true;
end
return % --*-- Unit tests --*--
%@test:1
% Fill global structures with required fields...
prior_trunc = 1e-10;
p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1); % Prior shape
p1 = .4*ones(14,1); % Prior mean
p2 = .2*ones(14,1); % Prior std.
p3 = NaN(14,1);
p4 = NaN(14,1);
p5 = NaN(14,1);
p6 = NaN(14,1);
p7 = NaN(14,1);
for i=1:14
switch p0(i)
case 1
% Beta distribution
p3(i) = 0;
p4(i) = 1;
[p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
case 2
% Gamma distribution
p3(i) = 0;
p4(i) = Inf;
[p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
case 3
% Normal distribution
p3(i) = -Inf;
p4(i) = Inf;
p6(i) = p1(i);
p7(i) = p2(i);
p5(i) = p1(i);
case 4
% Inverse Gamma (type I) distribution
p3(i) = 0;
p4(i) = Inf;
[p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
case 5
% Uniform distribution
[p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
p3(i) = p6(i);
p4(i) = p7(i);
p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
case 6
% Inverse Gamma (type II) distribution
p3(i) = 0;
p4(i) = Inf;
[p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
case 8
% Weibull distribution
p3(i) = 0;
p4(i) = Inf;
[p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
otherwise
error('This density is not implemented!')
end
end
BayesInfo.pshape = p0;
BayesInfo.p1 = p1;
BayesInfo.p2 = p2;
BayesInfo.p3 = p3;
BayesInfo.p4 = p4;
BayesInfo.p5 = p5;
BayesInfo.p6 = p6;
BayesInfo.p7 = p7;
ndraws = 10;
% Call the tested routine
try
% Instantiate dprior object
o = dprior(BayesInfo, prior_trunc, false);
% Do simulations in a loop and estimate recursively the mean and the variance.
for i = 1:ndraws
draw = o.draw();
if ~o.admissible(draw)
error()
end
end
t(1) = true;
catch
t(1) = false;
end
T = all(t);
%@eof:1

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