The workshop aims at presenting, discussing, and sharing experiences regarding advanced features and expert use of Dynare. It welcomes and stimulates exchanges and contributions with/from the audience.
The 2026 iteration of the workshop will focus on the following topics:
The workshop is addressed to advanced Dynare users with backgrounds ranging from academia to policy institutions. The workshop is open to a maximum of 20 qualified and selected participants.
No fee is due. Lunches, coffee breaks, a social dinner on 18 September, and workshop material are provided.
European Commission, Joint Research Centre,
Ispra site
via E. Fermi 2749
21027 Ispra (VA), Italy.
Participants will have to fund their own travel and accommodation expenses. The organizers have pre-booked rooms in nearby recommended hotels from 16 September until 19 September 2026. The workshop will start around 9:00 on Wednesday and finish around 17:00 on Friday.
Organizers will provide connections between the JRC and Milan Airports (Malpensa and Linate) and the Milano Centrale Train Station on the days of arrival and departure. We also organize two daily connections (morning and late afternoon) between the JRC and the pre-booked hotels. There will be no connection provided from other hotels to/from JRC.
The course is jointly organized by the Joint Research Centre, CEPREMAP, and DSGE-net.
Organizers and animators:
Local organisers: Eleonora Beghetto, Avramidou Kleoniki, Adrian Ifrim, Marco Membretti, Beatrice Pataracchia, Jan Teresinski.
This is a laptop-only workshop. Each participant is required to come with their laptop with MATLAB R2020a or later or Octave 11 and the latest stable Dynare version pre-installed. We will provide WiFi access, but participants should not rely on it to access a MATLAB license server at their own institution.
Please note that electricity plugs are Schuko type F: attendees should make sure to have the adapter to plug in their laptop.
Please fill out the application form and upload your CV and a recent research paper or work-in-progress (extended abstract, presentation) in PDF format.
In case of any questions, please contact school@dynare.org.
]]>This week-long program offers hands-on training in the simulation and estimation of Dynamic Stochastic General Equilibrium (DSGE) models using Dynare 7. Participants will progressively build, solve, simulate, estimate, and analyze models — from basic New Keynesian setups to policy relevant frameworks, and heterogeneous agent models.
The Dynare Masterclass welcomes a broad audience:
Whether you are new to Dynare or looking to deepen your skills, this program is designed for you.
Participants also get daily open lab time with direct feedback from Dynare developers on their own work.
| Category | Fee |
|---|---|
| Academic | 500 EUR |
| Institutional/Professional | 1,800 EUR |
Fees include meals, refreshments, and the social dinner on Thursday.
Participants must bring a laptop with one of the following installed:
WiFi will be available on site. If you use MATLAB, please do not rely on remote institutional license servers.
École Normale Supérieure 48 boulevard Jourdan, 75014 Paris, France
Please fill out the application form and upload your CV and a recent research paper or work-in-progress (extended abstract, presentation) in PDF format.
In case of any questions, please contact masterclass@dynare.org.
]]>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.
This release is compatible with MATLAB versions ranging from 9.8 (R2020a) to 25.2 (R2025b), and with GNU Octave versions ranging from 8.4.0 to 11.1.0 (NB: the Windows package requires version 11.1.0 specifically).
New framework for solving and simulating models featuring rich heterogeneity (which includes HANK models)
The solution technique for the dynamics combines elements from Bhandari, Bourany, Evans, and Golosov (2023) and Auclert, Bardóczy, Rognlie, and Straub (2021).
The steady state can be computed through a time iteration method within Dynare, or computed outside of Dynare and then loaded therein.
New statements heterogeneity_dimension, var(heterogeneity=NAME),
varexo(heterogeneity=NAME), model(heterogeneity=NAME),
shocks(heterogeneity=NAME), and the SUM() aggregation operator for
declaring heterogeneous features.
New heterogeneity_compute_steady_state command to compute the steady
state numerically.
New heterogeneity_load_steady_state command to load a pre-computed steady
state (policy functions, discretized shocks, stationary distribution) from
a MAT file.
New heterogeneity_solve command to compute a first-order linearized
solution.
New heterogeneity_simulate command to compute impulse response functions
and stochastic simulations, with support for both unanticipated shocks and
anticipated news shock sequences.
Example .mod files include: Krusell-Smith (krusell_smith.mod),
a one-asset HANK (hank_one_asset.mod), a two-asset HANK
(hank_two_assets.mod), with steady state computation variants. The models
are the same as in the Sequence-Space-Jacobian package
(shade-econ/sequence-jacobian on GitHub).
Skew normal shocks and closed skew normal (CSN) distribution support
New skew keyword in shocks and estimated_params blocks to declare
skewness.
Added support for skew normal shocks in stoch_simul command.
Added support for skew normal shocks in estimation command by means of
the Pruned Skewed Kalman Filter and Smoother of Guljanov, Mutschler, and
Trede (forthcoming).
Posterior samplers
New posterior samplers (available via the posterior_sampling_method
option of the estimation command):
The Dynamic Striated Metropolis Hastings sampler proposed by Waggoner,
Wu, and Zha (2016) is now available under the value 'dsmh';
The Differential-Independence Mixture Ensemble (“DIME”) MCMC sampler of
Boehl (2022) is now available under the value 'dime_mcmc'.
New posterior_sampler_option options for
posterior_sampling_method='slice':
save_iter_info_file to shut off saving of iteration information to a
file;
fast_likelihood_evaluation_for_rejection to reject poor draws in OccBin
more efficiently;
use_prior_draws to use the prior as the proposal within slice (Gibbs)
sampling for up to 10 draws.
Sequential Monte Carlo (SMC) sampler:
Trace plots now allow plotting the particles;
Now supports the bayesian_irf, moments_varendo, and smoother
options.
New functionality for leveraging MATLAB’s Parallel Computing Toolbox (PCT) for accelerating computations
mh_nblocks option);Controlled by a new use_pct option of the estimation command.
use_pct to false); random seeds are precomputed so results are
reproducible between serial and parallel execution (thanks to Eduard Benet
Cerda from MathWorks).New options to the estimation command:
estimate_initial_states_endogenous_prior, that estimates initial states
along with model parameters, for stationary models;
frequentist_smoother that allows shutting off the smoother in cases the
Bayesian smoother is not applicable;
use_univariate_smoother_if_singularity_is_detected to use the univariate
Kalman smoother in case stochastic singularity is detected.
OccBin
New piecewise linear particle filter (PPF).
New posterior importance sampling feature: allows to bootstrap posterior draws obtained from e.g. linear KF estimation, using PKF or PPF and check effective sample size and estimator bias.
Improvements to the the method_of_moments command:
It now supports estimation with discretionary optimal policy;
With mom_method=SMM it now supports the bandpass_filter,
one_sided_hp_filter, and hp_filter options to match filtered moments;
It now allows matching first moments even when the prefilter option is
used to obtain centered higher order moments.
Perfect foresight
Improvements to perfect foresight solver with iterative linear solvers,
i.e., GMRES (stack_solve_algo=2) and BiCGStab (stack_solve_algo=3):
New preconditioner option to the perfect_foresight_solver and
perfect_foresight_with_expectation_errors_solver commands, which
controls the preconditioner used with GMRES or BiCGStab;
The new default, preconditioner=first_iter_lu, does a LU decomposition
with complete pivoting at the first Newton iteration, and uses it as a
preconditioner in further iterations;
The value preconditioner=block_diagonal_lu computes a block diagonal
preconditioner based on a LU decomposition for only a few periods of the
stacked system;
The old default, using an incomplete LU decomposition, remains available
under precondition=incomplete_lu;
The first_iter_lu and block_diagonal_lu preconditioners deliver a
significant performance improvement over the old default
(incomplete_lu) and make this algorithm competitive and in some cases
faster than other perfect foresight solvers;
Other new options that control the behaviour of the iterative solvers:
iter_tol, iter_maxit, gmres_restart, block_diagonal_lu_maxlu,
block_diagonal_lu_nperiods, block_diagonal_lu_nlu,
block_diagonal_lu_relu.
New perfect foresight solvers:
ParU from SuiteSparse, a direct sparse LU solver, available under the
stack_solve_algo=8 option;
Panua PARDISO direct sparse LU solver, available under the
stack_solve_algo=9 option (license file has to be provided by the
user);
Panua PARDISO direct-iterative solver using CGS, available under the
stack_solve_algo=10 option (license file has to be provided by the
user).
New perfect_foresight_controlled_paths block that gives the possibility
of controlling the value of some endogenous variables for some simulation
periods (in which case some exogenous variables must be left free and are
thus solved for by Dynare, to avoid over-determination of the problem). Can
be used either in a pure perfect foresight context, or in perfect foresight
with expectation errors.
Perfect foresight integration with dates/dseries:
New options first_simulation_period and last_simulation_period to
perfect_foresight_setup and
perfect_foresight_with_expectation_errors_setup commands, to specify
the real dates for the simulation (e.g. 2024Q1);
The periods keyword of the shocks and mshocks blocks now accept
real dates;
The learnt_in option of the shocks, mshocks and endval blocks now
accept real dates;
When real dates are used, the perfect_foresight_solver and
perfect_foresight_with_expectation_errors_solver commands return the
simulation as a dseries object in the workspace variable
Simulated_time_series (which contains both endogenous and exogenous
variables).
The perfect_foresight_with_expectation_errors_setup command can now be
combined with a histval block or a histval_file command.
New endval_steady_nocheck option to the perfect_foresight_solver and
perfect_foresight_with_expectation_errors_solver commands, to skip
checking the terminal steady state values when they are provided explicitly
either by a steady state file or a steady_state_model block (in
combination with the endval_steady option for the
perfect_foresight_solver case). This is useful for models with unit roots
as, in this case, the steady state is not unique or doesn’t exist.
Using several endval blocks in the same file is now supported, in which
case they are concatenated.
Option values stack_solve_algo={2,3} are now available without block
nor bytecode.
New shock_paths block for describing a perfect foresight scenario in a
more compact and flexible way, which supersedes the shocks, mshocks and
endval blocks. Additionally, a new database command has been added to
refer to external databases that can be used from within shock_paths.
Steady state
The steady command now accepts the noprint option.
The steady command now accepts the non_zero option (with the same
meaning as in the resid command).
Option values solve_algo={6,7,8} are now available without block nor
bytecode.
Decision rules (reduced form solution)
New dr=aim option to the stoch_simul, estimation and
method_of_moments commands (equivalent to the former aim_solver option,
which is now deprecated).
New dr_cycle_reduction_maxiter option to the stoch_simul, estimation
and method_of_moments commands, to control the maximum number of
iterations when the cycle reduction algorithm is used.
Extended path
New option use_first_order_solution to the the extended_path command.
It is now possible to have zero-variance shocks.
Forecasting
The forecast command now also works at higher order and supports the
pruning option.
Option forecast_type to shock_decomposition to allow shock decomposing
conditional and unconditional forecasts.
Syntax
More flexible syntax for specifying complementarity conditions:
The complementarity condition is now specified between the associated
equation and its closing semicolon, using the perpendicular symbol as a
separator (the latter can be input either in UTF-8, as ⟂, corresponding
to Unicode codepoint U+27C2; or alternatively as pure ASCII, as _|_,
i.e. a vertical bar enclosed within two underscores);
Both the lower and the upper bounds can be specified at the same time in a given complementarity condition;
Arbitrary functions of parameters can appear in the bounds;
The old syntax using the mcp equation tag is still supported but
deprecated;
Here is an example of an equation with an associated complementarity condition, using the new syntax:
mu = 0 ⟂ 0 < i < 1+2*alpha;
Model-local variables (i.e. defined with a # sign) can now appear with
a lead or a lag in the model block (the expression by which they will be
replaced will be shifted accordingly).
The heteroskedastic_shocks block now supports dates syntax.
The osr command now supports using a planner_objective function to
conduct welfare analysis at higher order.
Significant performance improvements for:
Higher-order perturbation solution under Windows;
Estimation with the analytic_derivation option.
New debugging information
The multivariate Kalman filter smoother now allows using the debug option
to display information on linear combinations causing stochastic
singularity.
dynare_minimize_objective now returns runtime, iterations, function
evaluations, exitflag, and messages from all optimizers.
The model_diagnostics command now:
checks for redundant equations based on the dynamic Jacobian;
displays equation tags of affected equations;
displays its information in a more readable way.
New debugging options to the perfect_foresight_solver command:
check_jacobian_singularity to check the singularity of the dynamic
Jacobian for stack_solve_algo option equal to 0, 2 or 3;
allow_nonfinite_values to prevent the automatic removal of nonfinite
values during Newton iterations, which may be useful for debugging
purposes.
dseries
New routines for converting annual data into quarterly data, and quarterly data into monthly data.
New --print-table option to the dplot command, for displaying the
results as a table instead of a plot.
The dseries constructor based on a table is now available under Octave
(requires the datatypes package).
The dates and dseries classes now accept the string syntax (double
quotes) in their constructor and methods.
The calib_smoother command now plots the smoother results.
New preprocessor option output=first that instructs the preprocessor to
only compute first-order dynamic derivatives if no other command in the
.mod file requires higher-order ones.
New irfs(distribution) option to the prior command from the command-line
interface, for computing the prior distribution of the impulse response
functions of the endogenous variables.
New structure M_.state_var storing information on the state variables in
both declaration and decision-rule order.
References:
Auclert, A., B. Bardóczy, M. Rognlie, and L. Straub (2021): “Using the Sequence‐Space Jacobian to solve and estimate heterogeneous‐agent models,” Econometrica, 89(5), 2375–2408.
Bhandari, A., T. Bourany, D. Evans, and M. Golosov (2023): “A perturbational approach for approximating heterogeneous agent models,” NBER Working Papers, 31744.
Boehl, G. (2022): “DIME MCMC: A Swiss Army Knife for Bayesian Inference”, SSRN Electronic Journal, https://dx.doi.org/10.2139/ssrn.4250395
Guljanov, G., W. Mutschler and M. Trede (forthcoming): “Pruned skewed Kalman filter and smoother with application to DSGE models,” Journal of Economic Dynamics and Control
Waggoner, D. F., H. Wu and T. Zha (2016): “Striated Metropolis–Hastings sampler for high-dimensional models,” Journal of Econometrics, 192(2), 406–420.
With the osr command, the legacy linear-quadratic approach with an
optim_weights block now requires explicitly setting order=1.
The default filename produced by the savemacro option of the dynare
command has changed. It now contains an underscore (instead of a dash), for
better compatibility with MATLAB/Octave syntax.
Prior truncation is now shut off by default via setting prior_trunc=0.
The infile option of the smoother2histval command has been removed. An
equivalent functionality can be obtained with the outfile option of the
smoother2histval command and the histval_file command.
The det_cond_forecast, init_plan, basic_plan and flip_plan commands
have been removed. A similar functionality is provided by the new
perfect_foresight_controlled_paths block (or alternatively by the
shock_paths block with and endogenize stanza).
The unit_root_vars command has been removed. It has been superseded by the
diffuse_filter of the estimation command, and the nocheck option of the
steady command.
Estimated standard errors internally now get a prefix stderr instead of
just the name of the exogenous variable.
The deprecated dynare_sensitivity command has been removed, use the
sensitivity command instead.
The hp_ngrid option was removed, use filtered_theoretical_moments_grid
instead.
Declaring dsge_prior_weight as a parameter has been removed. It will be
automatically added when using the dsge_var option of the estimation
command.
The smoother2histval(outfile = …) command would store incorrect values for
variables appearing with a lag of 2 or more.
Using several endval blocks in the same file could lead to incorrect
results (this setup was not supported, but there was unfortunately no error
message preveting users from doing this). Proper support for multiple
endval blocks has been added (they are now concatenated).
The prior moments and prior optimize commands would crash.
The matched_irfs block would not accept a single entry following the
weights keyword when there were multiple entries following the periods
keyword, contrary to what the manual says.
The mode_compute=5 option of the estimation command would crash in case
of an invalid initial parameter draw.
Uniform priors did not correctly check for invalid hyperparameter specifications.
The method_of_moments command would crash upon trying to randomize the
starting value for steady state finding.
Filtered theoretical autocorrelation matrices at lag>0 in oo_.autocorr and
oo_.gamma_y were erroneously transposed.
The mh_recover and load_mh_file options of the estimation command would
not correctly work if the value of the mh_nblocks option was different from
what was originally run.
extended_path command would:
oo_.exo_simul;The ramsey_model command would crash in the presence of external functions.
model_info command would:
block_static option was
specified;incidence option.estimation command with measurement errors:
analytic_derivation option would crash.Using Weibull priors in estimation could trigger infinite loops.
The generalized Weibull prior distribution did not correctly take the third hyperparameter shifting the lower bound into account.
The display of problematic variables in debug mode for the
perfect_foresight_solver command was broken.
OccBin posterior moments would crash when the smoother encountered an unsuccessful draw.
OccBin: when multiple solutions were found, the relaxation algorithm would crash.
The diffuse Kalman smoother would return incorrect smoothed state uncertainty.
The block decomposition in a perfect foresight context would occasionally crash Octave.
Using the forecast command with a model solved at order=2 with
varexo_det, Dynare would return uncertainty bands based on a first-order
approximation.
OccBin would not correctly sum the likelihood of detected multiple solution paths.
identification command would ignore the kalman_algo option.We invite submissions of theoretical, empirical, and quantitative research on macroeconomic topics. We highly encourage all contributions, not limited to the use of Dynare. There will also be a Workshop on HANK models in Dynare on 1 June 2026, held by the Dynare team. Conference participants can attend the workshop free of charge.
All papers should be submitted via the submission form. The deadline for submissions is 15 March 2026, and notifications of acceptance will be sent by 10 April 2026. There is no fee to attend, lunch on both days and a conference dinner will be provided. Authors of accepted papers will be responsible for their airfare and accommodation.
Scientific committee: Stéphane Adjemian (Université du Mans and Dynare Team), Anastasiia Antonova (Bank of Canada), Willi Mutschler (University of Tübingen and Dynare Team), Chris Naubert (Bank of Canada), Johannes Pfeifer (University of the Bundeswehr Munich), Sébastien Villemot (CEPREMAP and Dynare Team), and Yang Zhang (Bank of Canada).
For inquiries, please contact conference@dynare.org
]]>This maintenance release fixes various bugs.
The Windows, macOS, MATLAB Online and source packages are available for download at the Dynare website.
This release is compatible with MATLAB versions ranging from 9.5 (R2018b) to 25.2 (R2025b), and with GNU Octave versions ranging from 7.1.0 to 10.3.0 (NB: the Windows package requires version 10.3.0 specifically).
Here is a list of the problems identified in version 6.4 and that have been fixed in version 6.5:
NaN or Inf; otherwise, the numerical solver was
uselessly startedInfkalman_algo=3) did not correctly exit the diffuse stageestimation command were fixed:
forecast option did not take shock
uncertainty into accountkeep_kalman_algo_if_singularity_is_detected option did not work as
advertised (option will be removed in Dynare 7)EndTemperature suboption of the mode_compute=10 option (“simpsa”) was
brokenmh_posterior_mode_estimation option was broken in conjunction with
the method_of_moments commandnodecomposition option did not suppress all variance decompositionsposterior_sampling_method='slice' option did not save all files in
the correct foldermh_recover option did not work correctly in conjunction with the
posterior_sampling_method='slice' and
posterior_sampler_options=('save_tmp_file', 1) optionsoo_.posterior_mode for shock and measurement error correlations1,000 instead of 5,000 particles was
the defaultestimation command did not allow the pruning option to be set for
higher-order estimationfilter_algorithm=cpf and
proposal_approximation=montecarlo of the estimation command returned
incorrect results'unscented_beta' and 'unscented_kappa' of the
particle_filter_options option of the estimation command did not accept
values of 0filter_algorithm=gf and filter_algorithm=gmf options of the
estimation command did not correctly handle correlated measurement errorforecast command used with exogenous deterministic
variables (declared with the varexo_det command) were fixed:
HPDinf field and the lower bound in the HPDsup fielddiscretionary_policy command did not accept the
planner_discount_latex_name option, contrary to what the documentation saysoutfile option of the smoother2histval command would produce an
incorrect file on models with two lags or more, leading to incorrect results
when loading that file with the histval_file commandexample1_reporting.mod example file was brokenAs a reminder, the list of new features introduced in versions 6.x can be found in the release notes for 6.0.
]]>This maintenance release fixes various bugs.
The Windows, macOS, MATLAB Online and source packages are available for download at the Dynare website.
This release is compatible with MATLAB versions ranging from 9.5 (R2018b) to 25.1 (R2025a), and with GNU Octave versions ranging from 7.1.0 to 10.2.0 (NB: the Windows package requires version 10.2.0 specifically).
Here is a list of the problems identified in version 6.3 and that have been fixed in version 6.4:
perfect_foresight_with_expectation_errors command:
endval/endval(learnt_in=1) block but an
endval(learnt_in=t) block with t>1, convergence could fail if homotopy
was neededhomotopy_marginal_linearization_fallback, it would return
incorrect resultshomotopy_marginal_linearization_fallback option if
there were several distinct shocks or endval blocks with the
learnt_in optionmodel_diagnostics command with the bytecode option of the
model block or the model_options command would crash if the static
Jacobian was singulardiscretionary_policy command with the noprint option would crash when
encountering a problem instead of continuingramsey_model command with models declared as linear would crash
during preprocessing when there is a lead/lag greater than 1 on endogenous
variables or any lead/lag on exogenous variablesidentification command with the slice sampler would crash if the
slice_initialize_with_mode option was usedresampling_method of the estimation command
was brokenoutput=third preprocessor option would not work if no computational
commands were present in the .mod fileAs a reminder, the list of new features introduced in versions 6.x can be found in the release notes for 6.0.
]]>Jean-Pierre Laffargue leaves an indelible mark on CEPREMAP’s macroeconomic activities. His commitment to developing numerical methods for solving large-scale, nonlinear, perfect foresight economic models was instrumental for the international community of macroeconomists. It was during the 1980s that Jean-Pierre Laffargue decided to develop an algorithm capable of solving any type of nonlinear dynamic model. His key contribution lay in harnessing the superior speed and stability of the Newton-Raphson algorithm, while devising ways to minimize memory requirements for its practical implementation, thereby reducing computational time. Laffargue’s algorithm was first published in 1988 in CEPREMAP’s Couvertures Oranges series, and later in 1990 in the Annales d’Économie et de Statistique. Around this major breakthrough in numerical methods for economists, the Dynare software was built. Laffargue’s algorithm is now used daily by macroeconomists around the world—whether academic researchers or economists at major institutions such as the IMF, the Federal Reserve System, the ECB, and others. His foundational research continues to contribute to public policy by enabling the use of state-of-the-art macroeconomic analysis to assess and compare alternative policy scenarios.
Building on these methodological achievements, Jean-Pierre Laffargue also contributed to the debate on the effectiveness of public policy in France. Initiating what would become the CEPREMAP tradition, he developed original models of the French economy, making it possible to analyze and evaluate a wide range of reforms. His work significantly shaped the analytical frameworks used in policy decision-making. In particular, during the 1990s (1993, 1995, 1996a, 1996b), Laffargue demonstrated how to integrate the latest advances in macroeconomic theory—such as monopolistic competition and wage bargaining—into dynamic models, even as these theoretical developments were still fresh in the literature. The models he developed were subsequently used to evaluate a broad array of public policies in France, including payroll tax cuts on low wages in response to skill-biased technological change, social VAT, and the taxation of capital income.
Jean-Pierre Laffargue was also a teacher to many of us. In this role, we are grateful to him for instilling in us a sense of rigor and critical thinking. His humor, love of debate, and attentive listening made it easy to engage with him and to learn from his teachings—both inside and outside the classroom.
Laffargue, Jean-Pierre. 1988. Résolution d’un modèle macroéconomique non linéaire avec anticipations rationnelles. Document de travail du CEPREMAP, no. 88/24. Paris: CEPREMAP.
Laffargue, Jean-Pierre. 1990. Résolution d’un modèle macroéconomique avec anticipations rationnelles. Annales d’économie et de statistique, 17: 97-119.
Laffargue, Jean-Pierre. 1993. Maquette dynamique de l’économie française avec anticipations rationnelles, concurrence monopolistique et négociations sur le marché du travail. Document de travail du CEPREMAP, no. 88/24. Paris: CEPREMAP.
Laffargue, Jean-Pierre. 1995. A Dynamic Model of the French Economy, with Rational Expectations, Monopolistic Competition and Labour Market Bargaining. Annales d’économie et de statistique, 37/38: 465-530.
Laffargue, Jean-Pierre. 1996a. Chômage des non qualifiés et progrès technique. le modèle charlotte. Document de travail du CEPREMAP, no. 96/12. Paris: CEPREMAP
Laffargue, Jean-Pierre. 1996b. Fiscalité, Charges sociales, qualifications et emploi. Économie et Prévision, 125: 87-105.
The workshop aims at presenting, discussing, and sharing experiences regarding advanced features and expert use of Dynare. It welcomes and stimulates exchanges and contributions with/from the audience.
The 2025 iteration of the workshop will focus on the following topics:
An update on Dynare – new & prospective features:
Contributions from the audience and open discussion: “Using models for policy analysis and research: lessons learned, issues, needs.” (selected short and in-depth presentations).
The workshop is addressed to advanced Dynare users with backgrounds ranging from academia to policy institutions.
The course will be open to a maximum of 20 qualified and selected participants. Interested parties can apply by sending the following information to school@dynare.org:
CV and a recent research paper (not necessarily using Dynare) (PDFs only)
The filled-in attached application form (saved as a PDF)
Applications should be submitted no later than 30 April 2025. We will notify successful applicants by 31 May 2025 the latest.
No fee is due. Lunches, coffee breaks, a social dinner on September 18th, and workshop material are provided.
European Commission, Joint Research Centre, Ispra site via E. Fermi 2749 21027 Ispra (VA), Italy.
Participants will have to fund their own travel and accommodation expenses. The organizers have pre-booked rooms in nearby recommended hotels from 17 September until 19 September 2025. The workshop will start around 9:00 on Wednesday and finish around 17:00 on Friday.
Organizers will provide connections between the JRC and Milan Airports (Malpensa and Linate) and the Milano Centrale Train Station on the days of arrival and departure. We also organize two daily connections (morning and late afternoon) between the JRC and the pre-booked hotels. There will be no connection provided from other hotels to/from JRC.
The course is jointly organized by the Joint Research Centre and CEPREMAP. Organizers and animators:
Local organisers: Eleonora Beghetto, Avramidou Kleoniki, Adrian Ifrim, Marco Membretti, Beatrice Pataracchia, Josselin Roman, Jan Teresinski.
This is a laptop-only workshop. Each participant is required to come with his/her laptop with MATLAB R2018b or later and the latest stable Dynare version installed. We will provide WiFi access, but participants shouldn’t rely on it to access a MATLAB license server at their own institution. As an alternative to MATLAB, it is possible to use GNU Octave (free software, compatible with MATLAB syntax).
Please note that electricity plugs are Schuko type F: attendees should make sure to have the adapter to plug in in their laptop.
]]>This maintenance release fixes various bugs.
The Windows, macOS, MATLAB Online and source packages are available for download at the Dynare website.
This release is compatible with MATLAB versions ranging from 9.5 (R2018b) to 24.2 (R2024b), and with GNU Octave versions ranging from 7.1.0 to 9.4.0 (NB: the Windows package requires version 9.4.0 specifically).
Here is a list of the problems identified in version 6.2 and that have been fixed in version 6.3:
smoother_inversion_filter would crash the MCMC
estimation if the filtered_variables or filter_step_ahead options were
usedsmoother_inversion_filter would use the PKF if
mh_replic>0smoothed_state_uncertainty would crash Dynare if the
MCMC smoother was run after the classical onemodel_info with differentiate_forward_vars would crashidentification command would compute the asymptotic Hessian via
simulation instead of via moments as intendedidentification command would crash during prior sampling if the initial
draw did not solve the modelgsa_sample_file was brokenmode_compute=5 (newrat) would crash with
analytic_derivationdiscretionary_policy command would crash if the model was purely
forward-lookingdsample command would crashconditional_forecast_paths block did not accept vector inputssub_draws
used to compute posterior moments was incorrect2024S1 or 2024H1) were not accepted within Dynare
statementsdseries did not correctly show dates on the x-axisAs a reminder, the list of new features introduced in versions 6.x can be found in the release notes for 6.0.
]]>We invite submissions of theoretical, empirical, and quantitative research on macroeconomic topics. Following this year’s special theme, we are particularly interested in contributions that explore heterogeneity and non-linearities in quantitative macroeconomic models. We encourage the use of diverse software tools beyond Dynare.
All papers should be submitted via the submission form. The deadline for submissions is 16 March 2025, and notifications of acceptance will be sent by 11 April 2025. There is no fee to attend, lunch on both days and a conference dinner will be provided. Authors of accepted papers will be responsible for their airfare and accommodation.
Scientific committee: Stéphane Adjemian (Université du Mans and Dynare Team), Johannes Pfeifer (Universität der Bundeswehr München and Dynare Team), Annukka Ristiniemi (European Central Bank), Juha Kilponen (Bank of Finland), Esa Jokivuolle (Bank of Finland) and Markus Haavio (Bank of Finland) For inquiries, please contact conference(at)dynare.org and on local organisation, please contact Riikka Virtanen, riikka.virtanen(at)bof.fi
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