NEWS

Announcement for Dynare 4.3.0 (on 2012-06-15)
=============================================

We are pleased to announce the release of Dynare 4.3.0. This major release adds
new features and fixes various bugs.

The Windows and Mac packages are already available for download at:

 http://www.dynare.org/download/dynare-4.3

The GNU/Linux packages should follow soon.

All users are strongly encouraged to upgrade.

The new release is compatible with MATLAB versions ranging from 7.0 (R14) to
7.14 (R2012a) and with GNU Octave versions ranging from 3.2 to 3.6.

Here is the list of the main user-visible changes:


* New major algorithms:

 - Nonlinear estimation with a particle filter based on a second order
   approximation of the model, as in Fernández-Villaverde and Rubio-Ramírez
   (2005); this is triggered by setting `order=2' in the `estimation' command

 - Extended path solution method as in Fair and Taylor (1983); see the
   `extended_path' command

 - Support for Markov-Switching Structural Bayesian VARs (MS-SBVAR) along the
   lines of Sims, Waggoner and Zha (2008) (see the dedicated section in the
   reference manual)

 - Optimal policy under discretion along the lines of Dennis (2007); see the
   `discretionary_policy' command

 - Identification analysis along the lines of Iskrev (2010); see the
   `identification' command

 - The Global Sensitivity Analysis toolbox (Ratto, 2008) is now part of the
   official Dynare distribution


* Other algorithmic improvements:

 - Stochastic simulation and estimation can benefit from block decomposition
   (with the `block' option of `model'; only at 1st order)

 - Possibility of running smoother and filter on a calibrated model; see the
   `calib_smoother' command

 - Possibility of doing conditional forecast on a calibrated model; see the
   `parameter_set=calibration' option of the `conditional_forecast' command

 - The default algorithm for deterministic simulations has changed and is now
   based on sparse matrices; the historical algorithm (Laffargue, Boucekkine
   and Juillard) is still available under the `stack_solve_algo=6'option of the
   `simul' command

 - Possibility of using an analytic gradient for the estimation; see the
   `analytic_derivation' option of the `estimation' command

 - Implementation of the Nelder-Mead simplex based optimization routine for
   computing the posterior mode; available under the `mode_compute=8' option of
   the `estimation' command

 - Implementation of the CMA Evolution Strategy algorithm for computing the
   posterior mode; available under the `mode_compute=9' option of the
   `estimation' command