We are pleased to announce the release of Dynare 6.1. This maintenance release 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 9.5 (R2018b) to 24.1 (R2024a), and with GNU Octave versions ranging from 7.1.0 to 9.1.0 (NB: the Windows package requires version 9.1.0 specifically).

Here is a list of the problems identified in version 6.0 and that have been fixed in version 6.1:

Identification: simulated moments were triggered instead of theoretical ones
Variance decompositions would crash with measurement errors when zero variance shocks were present
The handling of Lagrange multipliers in the display of problems with the Jacobian was wrong
The option auxname was missing in the documentation of the pac_model command
PAC equation estimation/simulation was crashing in the case of composite target
The PAC equation estimation would crash if the PAC target was a transformed variable
The perfect_foresight_with_expectation_errors_solver command could return incorrect results when used in conjunction with homotopy_linearization_fallback or homotopy_marginal_linearization_fallback options
For scalar values, the description of the horizon option of the var_expectation_model command was incorrect
The steady state computation with the bytecode option in a Ramsey model was broken
OccBin: the piecewise Kalman filter would crash in case of a periodic solution
The heteroskedastic_filter option of the estimation command would cause a crash if there was only one shock
The method_of_moments command would crash during the J-test for just and underidentified models
User-defined warning settings were internally overwritten with the method_of_moments command or the piecewise Kalman filter
The SMC sampler would crash if any of the bayesian_irf, moments_varendo, or smoother options of the estimation command had been specified
The bvar_irf command would ignore the SquareRoot option and instead employ a Cholesky decomposition
The univariate Kalman filter erroneously treated observations with negative prediction variances due to numerical issues as missing values instead of discarding the parameter draw

Moreover, a new homotopy_exclude_varexo option to the perfect_foresight_solver command has been added, to exclude some exogenous variables from the homotopy procedure (i.e. to keep them at their value corresponding to 100% of the shock during all homotopy iterations).

As a reminder, the list of new features introduced in versions 6.x can be found in the release notes for 6.0.