COPYING

+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+
+                            Preamble
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+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program 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.
+
+    This program 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 this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.

Makefile.am

@@ -37,3 +37,15 @@ dist-hook:
 	rm -f $(distdir)/matlab/dynare_m$(EXEEXT) $(distdir)/matlab/dynare_version.m
 	$(MKDIR_P) $(distdir)/mex/matlab $(distdir)/mex/octave
 	rm -rf `find $(distdir)/contrib -name '.git*'`
+
+install-exec-local:
+	$(MKDIR_P) $(DESTDIR)$(pkglibdir)/contrib/ms-sbvar/TZcode
+	cp -r examples $(DESTDIR)$(pkglibdir)
+	cp -r matlab $(DESTDIR)$(pkglibdir)
+	rm -f $(DESTDIR)$(pkglibdir)/matlab/dynare_m
+	cp preprocessor/dynare_m $(DESTDIR)$(pkglibdir)/matlab
+	cp -r contrib/ms-sbvar/TZcode/MatlabFiles $(DESTDIR)$(pkglibdir)/contrib/ms-sbvar/TZcode
+
+uninstall-local:
+	rm -f  $(DESTDIR)$(bindir)/dynare++
+	rm -rf $(DESTDIR)$(pkglibdir)

NEWS

+Announcement for Dynare 4.4.3 (on 2014-07-31)
+=============================================
+
+We are pleased to announce the release of Dynare 4.4.3.
+
+This is a bugfix release.
+
+The Windows packages are already available for download at:
+
+ http://www.dynare.org/download/dynare-stable
+
+The Mac and GNU/Linux packages (for Debian and Ubuntu LTS) should follow soon.
+
+This release is compatible with MATLAB versions 7.3 (R2006b) to 8.2 (R2013b)
+and with GNU Octave versions 3.6 to 3.8.
+
+Here is a list of the problems identified in version 4.4.2 and that have been
+fixed in version 4.4.3:
+
+ - When loading a dataset in XLS, XLSX or CSV format, the first
+   observation was discarded.
+
+ - Reading data in an Excel-file with only one variable wasz leading
+   to a crash.
+
+ - When using the k_order_perturbation option (which is implicit at
+   3rd order) without the use_dll option, crashes or unexpected
+   behavior could happen if some 2nd or 3rd derivative evaluates to
+   zero (while not being symbolically zero)
+
+ - When using external function, Ramsey policy could crash or return
+   wrong results.
+
+ - For Ramsey policy, the equation numbers associated with the
+   Lagrange multipliers stored in M_.aux_vars were erroneously one too
+   low
+
+ - When updating deep parameters in the steady state file, the changes
+   were not fully taken into account (this was only affecting the
+   Ramsey policy).
+
+ - When using external functions and the bytecode option, wrong
+   results were returned (if second order derivates of the external
+   functions were needed).
+
+ - The confidence level for computations in estimation, conf_sig could
+   not be changed and was fixed at 0.9. The new option mh_conf_sig is
+   now used to set this interval
+
+ - Conditional forecasts with non-diagonal covariance matrix used an
+   incorrect decomposition of the covariance matrix. A Cholesky
+   factorization is used.
+
+ - Option geweke_interval was not effective, Dynare always defaulted
+   to the standard value.
+
+ - The mode_file option lacked backward compatibility with older
+   Dynare versions.
+
+ - Loading an mh_mode file with the mode_file option was broken.
+
+ - Using identification with var_exo_det leaded to crashes (the
+   preprocessor now returns an error if they are used simultaneously)
+
+ - The identification command did not print results if the initial
+   parameter set was invalid and then crashed later on if the MC
+   sample is bigger than 1
+
+ - Inconsistencies between static and dynamic models leaded to crashes
+   instead of error messages (only with block option).
+
+ - The use of external functions crashed the preprocessor when the
+   derivatives of the external function are explicitly called in the
+   model block. The preprocessor now forbids the use of external
+   functions derivates in the model block.
+
+ - Using the block option when a variable does not appear in the
+   current period crashed Dynare instead of providing an error
+   message.
+
+
+Announcement for Dynare 4.4.2 (on 2014-03-04)
+=============================================
+
+We are pleased to announce the release of Dynare 4.4.2.
+
+This is a bugfix release.
+
+The Windows packages are already available for download at:
+
+ http://www.dynare.org/download/dynare-stable
+
+The Mac and GNU/Linux packages (for Debian and Ubuntu LTS) should follow soon.
+
+This release is compatible with MATLAB versions 7.3 (R2006b) to 8.2 (R2013b)
+and with GNU Octave versions 3.6 to 3.8.
+
+Here is a list of the problems identified in version 4.4.1 and that have been
+fixed in version 4.4.2:
+
+ - Geweke convergence diagnostics was computed on the wrong sample if `mh_drop'
+   was not equal to the default of 0.5.
+
+ - The `loglinear' option of `stoch_simul' was displaying the steady state of
+   the original values, not the logged ones, and was producing incorrect
+   simulations and simulated moments. Theoretical moments were unaffected.
+
+ - The `optim' option of `estimation (for setting options to `mode_compute')
+   was only working with at least MATLAB 8.1 (R2013a) or Octave 3.8.
+
+ - For unit root models, theoretical HP filtered moments were sometimes
+   erroneously displayed as NaN.
+
+ - Specifying an endogenous variable twice after the `estimation' command would
+   lead to a crash in the computation of moments.
+
+ - Deterministic simulations were crashing on some models with more than one
+   lead or one lag on exogenous variables.
+
+ - Homotopy in stochastic extended path with order greater than 0 was not
+   working correctly (during the homotopy steps the perfect foresight model
+   solver was called instead of the stochastic perfect foresight model solver).
+
+ - MCMC convergence diagnostics were not computed if `mh_replic' was less than
+   2000; the test now relies on the total number of iterations (this only makes
+   a difference if option `load_mh_file' is used).
+
+
+Announcement for Dynare 4.4.1 (on 2014-01-17)
+=============================================
+
+We are pleased to announce the release of Dynare 4.4.1.
+
+This release contains a few changes to the user interface and fixes various
+bugs. It also adds compatibility with Octave 3.8.
+
+The Windows packages are already available for download at:
+
+ http://www.dynare.org/download/dynare-stable
+
+The Mac and GNU/Linux packages (for Debian and Ubuntu) should follow soon.
+
+All users are encouraged to upgrade.
+
+This release is compatible with MATLAB versions 7.3 (R2006b) to 8.2 (R2013b) and
+with GNU Octave versions 3.6 to 3.8.
+
+* Changes to the user interface:
+
+ - The syntax introduced in 4.4.0 for conditional forecast in a deterministic
+   setup was removed, and replaced by a new one that is better suited to the
+   task. More precisely, such deterministic forecasts are no longer done using
+   the `conditional_forecast' command. The latter is replaced by a group of
+   commands: `init_plan', `basic_plan' and `flip_plan'. See the reference
+   manual for more details.
+
+ - Changes to the reporting module: option `annualAverages' to `addTable' has
+   been removed (use option `tableDataRhs' to `addSeries' instead); option
+   `vlineAfter' to `addTable' now also accepts a cell array.
+
+ - Changes to the date and time series classes: implement broadcasting for
+   operations (+,-,* and /) between `dseries' class and scalar or vectors; add
+   the possibility of selecting an observation within a time series using a
+   formatted string containing a date.
+
+* Bugs and problems identified in version 4.4.0 and that have been fixed in
+  version 4.4.1:
+
+ - In MS-SBVAR, there was a bug preventing the computation of impulse responses
+   on a constant regime.
+
+ - Under Octave, after modifying the MOD file, the changes were not taken into
+   account at the first Dynare run, but only at the second run.
+
+
+Announcement for Dynare 4.4.0 (on 2013-12-16)
+=============================================
+
+We are pleased to announce the release of Dynare 4.4.0.
+
+This major release adds new features and fixes various bugs.
+
+The Windows packages are already available for download at:
+
+ http://www.dynare.org/download/dynare-stable
+
+The Mac and Debian/Ubuntu packages should follow soon.
+
+All users are strongly encouraged to upgrade.
+
+This release is compatible with MATLAB versions ranging from 7.3 (R2006b) to
+8.2 (R2013b) and with GNU Octave version 3.6.
+
+Here is the list of major user-visible changes:
+
+
+* New major algorithms:
+
+ - Extended path at order 1 and above, also known as “stochastic extended
+   path”. This method is triggered by setting the `order' option of the
+   `extended_path' command to a value greater than 0. Dynare will then use a
+   Gaussian quadrature to take into account the effects of future uncertainty.
+   The time series for the endogenous variables are generated by assuming that
+   the agents believe that there will no more shocks after period t+order.
+
+ - Alternative algorithms for computing decision rules of a stochastic model,
+   based on the cycle reduction and logarithmic reduction algorithms. These
+   methods are respectively triggered by giving `dr = cycle_reduction' or 'dr
+   = logarithmic_reduction' as an option to the `stoch_simul' command.
+
+ - Pruning now works with 3rd order approximation, along the lines of
+   Andreasen, Fernández-Villaverde and Rubio-Ramírez (2013).
+
+ - Computation of conditional forecast using an extended path method. This is
+   triggered by the new option `simulation_type = deterministic' in the
+   `conditional_forecast' command. In this case, the `expectation' command in
+   the `conditional_forecast_paths' block has to be used to indicate the nature
+   of expectations (whether shocks are a surprise or are perfectly
+   anticipated).
+
+ - Endogenous priors as in Christiano, Trabandt and Walentin (2011). Those are
+   triggered by the new option `endogenous_prior' of the `estimation' command.
+
+
+* Other algorithmic improvements:
+
+ - New command `model_diagnostics' to perform various sanity checks on the
+   model. Note: in the past, some users may have used a preliminary MATLAB
+   function implementing this; the new command has the same syntax, except that
+   you shouldn't pass any argument to it.
+
+ - Terminal conditions of perfect foresight simulations can now be specified in
+   growth rates. More specifically, the new option `differentiate_forward_vars'
+   of the `model' block will create auxiliary forward looking variables
+   expressed in first differences or growth rates of the actual forward looking
+   variables defined in the model. These new variables have obvious zero
+   terminal conditions whatever the simulation context and this in many cases
+   helps convergence of simulations.
+
+ - Convergence diagnostics for single chain MCMC à la Geweke (1992, 1999).
+
+ - New optimizer for the posterior mode (triggered by `mode_compute=10'): it
+   uses the simpsa algorithm, based on the combination of the non-linear
+   simplex and simulated annealing algorithms and proposed by Cardoso, Salcedo
+   and Feyo de Azevedo (1996).
+
+ - The automatic detrending engine has been extended to work on models written
+   in logs. The corresponding trend variable type is `log_trend_var', and the
+   corresponding deflator type is `log_deflator'.
+
+
+* New features in the user interface:
+
+ - New set of functions for easily creating PDF reports including figures and
+   tables. See the “Reporting” section in the reference manual for more
+   details.
+
+ - New MATLAB/Octave classes for handling time series. See the “Time series”
+   section in the reference manual for more details.
+
+ - Datafiles in CSV format can now be used for estimation.
+
+ - New macro processor `length' operator, returns the length of an array.
+
+ - New option `all_values_required' of `initval' and `endval' blocks: enforces
+   initialization of all endogenous and exogenous variables within the block.
+
+ - Option `ar' can now be given to the `estimation' command.
+
+ - New options `nograph', `nointeractive' and `nowarn' to the `dynare' command,
+   for a better control of what is displayed.
+
+ - New option `nostrict' to the `dynare' command, for allowing Dynare to
+   continue processing when there are more endogenous variables than equations
+   or when an undeclared symbol is assigned in `initval' or `endval'.
+
+ - The information on MCMC acceptance rates, seeds, last log posterior
+   likelihood, and last parameter draw are now saved on the disk and can
+   be displayed with `internals --display-mh-history' or loaded into the
+   workspace with `internals --load-mh-history'.
+
+ - New options `mode_check_neighbourhood_size', `mode_check_symmetric_plots'
+   and `mode_check_number_of_points', for a better control of the diagnostic
+   plots.
+
+ - New option `parallel_local_files' of `model' block, for transferring extra
+   files during parallel computations.
+
+ - New option `clock' of `set_dynare_seed', for setting a different seed at
+   each run.
+
+ - New option `qz_zero_threshold' of the `check', `stoch_simul' and
+   `estimation' commands, for a better control of the situation where a
+   generalized eigenvalue is close to 0/0.
+
+ - New `verbatim' block for inclusion of text that should pass through the
+   preprocessor and be placed as is in the `modfile.m' file.
+
+ - New option `mcmc_jumping_covariance' of the `estimation' command, for a
+   better control of the covariance matrix used for the proposal density of the
+   MCMC sampler.
+
+ - New option `use_calibration' of the `estimated_params_init', for using the
+   calibration of deep parameters and the elements of the covariance matrix
+   specified in the `shocks' block as starting values for the estimation.
+
+ - New option `save_draws' of the `ms_simulation' command.
+
+ - New option `irf_plot_threshold' of the `stoch_simul' and `estimation'
+   commands, for a better control of the display of IRFs which are almost nil.
+
+ - New option `long_name' for endogenous, exogenous and parameter declarations,
+   which can be used to declare a long name for variables. That long name can
+   be programmatically retrieved in `M_.endo_names_long'.
+
+
+* Miscellaneous changes
+
+ - The deciles of some posterior moments were erroneously saved in a field
+   `Distribution' under `oo_'. This field is now called `deciles', for
+   consistency with other posterior moments and with the manual. Similarly, the
+   fields `Mean', `Median', `HPDsup', `HPDinf', and `Variance' are now
+   consistently capitalized.
+
+ - The console mode now implies the `nodisplay' option.
+
+
+* Bugs and problems identified in version 4.3.3 and that have been fixed in
+  version 4.4.0:
+
+ - In an `endval' block, auxiliary variables were not given the right value.
+   This would not result in wrong results, but could prevent convergence of
+   the steady state computation.
+
+ - Deterministic simulations with `stack_solve_algo=0' (the default value) were
+   crashing if some exogenous had a lag strictly greater than 1.
+
+ - When using the `mode_file' option, the initial estimation checks were not
+   performed for the loaded mode, but for the original starting values. Thus,
+   potential prior violations by the mode only appeared during estimation,
+   leading to potentially cryptic crashes and error messages.
+
+ - If a shock/measurement error variance was set to 0 in calibration, the
+   correlation matrix featured a 0 instead of a 1 on the diagonal, leading to
+   wrong estimation results.
+
+ - In the presence of calibrated covariances, estimation did not enforce
+   positive definiteness of the covariance matrix.
+
+ - Estimation using the `diffuse_filter' option together with the univariate
+   Kalman filter and a diagonal measurement error matrix was broken.
+
+ - A purely backward model with `k_order_solver' was leading to crashes of
+   MATLAB/Octave.
+
+ - Non-linear estimation was not skipping the specified presample when
+   computing the likelihood.
+
+ - IRFs and theoretical moments at order > 1 were broken for purely
+   forward-looking models.
+
+ - Simulated moments with constant variables was leading to crashes when
+   displaying autocorrelations.
+
+ - The `osr' command was sometimes crashing with cryptic error messages because
+   of some unaccounted error codes returned from a deeper routine.
+
+ - The check for stochastic singularity during initial estimation checks was
+   broken.
+
+ - Recursive estimation starting with the pathological case of `nobs=1' was
+   crashing.
+
+ - Conditional variance decomposition within or after estimation was crashing
+   when at least one shock had been calibrated to zero variance.
+
+ - The `estimated_params_init' and `estimated_params_bounds' blocks were broken
+   for correlations.
+
+ - The `filter_step_ahead' option was not producing any output in Bayesian
+   estimation.
+
+ - Deterministic simulations were sometimes erroneously indicating convergence
+   although the residuals were actually NaN or Inf.
+
+ - Supplying a user function in the `mode_compute' option was leading to
+   a crash.
+
+ - Deterministic simulation of models without any exogenous variable was
+   crashing.
+
+ - The MS-SBVAR code was not updating files between runs on Windows. This means
+   that if a MOD file was updated between runs in the same folder and a
+   `file_tag' was not changed, then the results would not change.
+
+ - The `ramsey_policy' command was not putting in `oo_.planner_objective_value'
+   the value of the planner objective at the optimum.
+
+
+* References:
+
+ - Andreasen, Martin M., Jesús Fernández-Villaverde, and Juan Rubio-Ramírez
+   (2013): “The Pruned State-Space System for Non-Linear DSGE Models: Theory
+   and Empirical Applications,” NBER Working Paper, 18983
+
+ - Cardoso, Margarida F., R. L. Salcedo and S. Feyo de Azevedo (1996): “The
+   simplex simulated annealing approach to continuous non-linear optimization,”
+   Computers chem. Engng, 20(9), 1065-1080
+
+ - 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
+
+ - Geweke, John (1992): “Evaluating the accuracy of sampling-based approaches
+   to the calculation of posterior moments,” in J.O. Berger, J.M. Bernardo,
+   A.P. Dawid, and A.F.M. Smith (eds.) Proceedings of the Fourth Valencia
+   International Meeting on Bayesian Statistics, pp. 169-194, Oxford University
+   Press
+
+ - Geweke, John (1999): “Using simulation methods for Bayesian econometric
+   models: Inference, development and communication,” Econometric Reviews,
+   18(1), 1-73
+
+
 Announcement for Dynare 4.3.3 (on 2013-04-12)
 =============================================
 

README.md

@@ -20,7 +20,7 @@ Here, we explain how to build from source:
 
 This source can be retrieved in three forms:
 - via git, at <https://github.com/DynareTeam/dynare.git>
-- using the stable source archive of the latest Dynare version (currently 4.3) from <http://www.dynare.org/download/dynare-4.3/source>
+- using the stable source archive of the latest Dynare version (currently 4.4) from <http://www.dynare.org/download/dynare-stable/>
 - using a source snapshot of the unstable version, from <http://www.dynare.org/download/dynare-unstable/source-snapshot>
 
 Note that if you obtain the source code via git, you will need to install more tools (see below).
@@ -49,28 +49,27 @@ A number of tools and libraries are needed in order to recompile everything. You
 
 - A POSIX compliant shell and an implementation of Make (mandatory)
 - The [GNU Compiler Collection](http://gcc.gnu.org/), with gcc, g++ and gfortran (mandatory)
-- [MATLAB](http://www.dynare.org/DynareWiki/BuildingDynareFromSource?action=AttachFile&do=view&target=dynare-mingw64-libs.zip) (if you want to compile MEX for MATLAB)
+- MATLAB (if you want to compile MEX for MATLAB)
 - [GNU Octave](http://www.octave.org), with the development headers (if you want to compile MEX for Octave)
 - [Boost libraries](http://www.boost.org), version 1.36 or later
-- [Bison](http://www.gnu.org/software/bison/), version 2.3 or later (only if you get the source through Git)
+- [Bison](http://www.gnu.org/software/bison/), version 2.5 or later (only if you get the source through Git)
 - [Flex](http://flex.sourceforge.net/), version 2.5.4 or later (only if you get the source through Git)
 - [Autoconf](http://www.gnu.org/software/autoconf/), version 2.62 or later (only if you get the source through Git) (see [Installing an updated version of Autoconf in your own directory, in GNU/Linux](http://www.dynare.org/DynareWiki/AutoMake))
 - [Automake](http://www.gnu.org/software/automake/), version 1.11.2 or later (only if you get the source through Git) (see [Installing an updated version of AutoMake in your own directory, in GNU/Linux](http://www.dynare.org/DynareWiki/AutoMake))
 - [CWEB](http://www-cs-faculty.stanford.edu/%7Eknuth/cweb.html), with its tools `ctangle` and `cweave` (only if you want to build Dynare++ and get the source through Git)
 - An implementation of BLAS and LAPACK: either [ATLAS](http://math-atlas.sourceforge.net/), [OpenBLAS](http://xianyi.github.com/OpenBLAS/), Netlib ([BLAS](http://www.netlib.org/blas/), [LAPACK](http://www.netlib.org/lapack/)) or [MKL](http://software.intel.com/en-us/intel-mkl/) (only if you want to build Dynare++)
 - An implementation of [POSIX Threads](http://en.wikipedia.org/wiki/POSIX_Threads) (optional, for taking advantage of multi-core)
-- [MAT File I/O library](http://sourceforge.net/projects/matio/) (if you want to compile Markov-Switching code, the estimation DLL, k-order DLL and Dynare++ in unstable)
+- [MAT File I/O library](http://sourceforge.net/projects/matio/) (if you want to compile Markov-Switching code, the estimation DLL, k-order DLL and Dynare++)
 - [SLICOT](http://www.slicot.org) (if you want to compile the Kalman steady state DLL)
 - [GSL library](http://www.gnu.org/software/gsl/) (if you want to compile Markov-Switching code)
 - A decent LaTeX distribution (if you want to compile PDF documentation). The following extra components may be needed:
-  - The Econometrica bibliography style: you need [harvard](http://www.ctan.org/tex-archive/macros/latex/contrib/harvard/) and [economic](http://www.ctan.org/tex-archive/biblio/bibtex/contrib/economic/) packages from CTAN (only if you want to build Dynare user guide, no more needed with Dynare unstable)
   - [Eplain](http://www.tug.org/eplain/) TeX macros (only if you want to build Dynare++ source documentation)
   - [Beamer](http://latex-beamer.sourceforge.net/) (for some PDF presentations)
 - For building the reference manual:
   - [GNU Texinfo](http://www.gnu.org/software/texinfo/)
   - [Texi2HTML](http://www.nongnu.org/texi2html) and [Latex2HTML](http://www.latex2html.org), if you want nice mathematical formulas in HTML output
   - [Doxygen](http://www.stack.nl/%7Edimitri/doxygen/) (if you want to build Dynare preprocessor source documentation)
-- For Octave, the development libraries corresponding to the UMFPACK packaged with Octave (only in unstable)
+- For Octave, the development libraries corresponding to the UMFPACK packaged with Octave
 
 ### Preparing the sources
 
@@ -78,9 +77,8 @@ If you have downloaded the sources from an official source archive or the source
 
 If you want to use Git, do the following from a terminal:
 
-    git clone http://github.com/DynareTeam/dynare.git
+    git clone --recursive http://github.com/DynareTeam/dynare.git
     cd dynare
-    git submodule update --init
     autoreconf -si
 
 The last line runs Autoconf and Automake in order to prepare the build environment (this is not necessary if you got the sources from an official source archive or the source snapshot).
@@ -139,12 +137,12 @@ apt-get build-dep dynare
 Alternatively, if you want to build everything, manually install the following packages:
 
 - `build-essential` (for gcc, g++ and make)
-- `octave3.2-headers` or `liboctave-dev` (will install ATLAS)
+- `liboctave-dev` or `octave3.2-headers` (will install ATLAS)
 - `libboost-graph-dev`
 - `libgsl0-dev`
 - `libmatio-dev`
 - `libslicot-dev` and `libslicot-pic`
-- `libsuitesparse-dev` (only for Unstable)
+- `libsuitesparse-dev`
 - `flex`
 - `bison`
 - `autoconf`
@@ -181,24 +179,29 @@ The following instructions are compatible with MATLAB or with Octave/MinGW (as d
 
 ### Setting up the Compilation Environment
 
-- First, you need to setup a Cygwin environment, following the instructions at <http://www.cygwin.com>. You need the following packages:
+- First, you need to setup a Cygwin environment, following the instructions at <http://www.cygwin.com>. You can install either the 32-bit or the 64-bit version. If you opt for the latter, you need to replace `c:\cygwin` by `c:\cygwin64` in the following.
+- Install the following packages:
     - `make`
     - `bison`
     - `flex`
-    - `autoconf` and `autoconf2.5`
-    - `automake` and `automake1.11`
-    - `texlive`, `texlive-collection-latexextra`, `texlive-collection-formatsextra`, `texlive-collection-publishers`
+    - `git`, `gettext`
+    - `autoconf`
+    - `automake`
+    - `texi2html`
+    - `texlive`, `texlive-collection-latexextra`, `texlive-collection-formatsextra`, `texlive-collection-publishers`, `texlive-collection-fontsrecommended`, `texlive-collection-fontsextra`, `texlive-collection-bibtexextra`, `texlive-collection-genericrecommended`, `texlive-collection-mathextra`, `texlive-collection-binextra`
     - `texinfo`
     - `doxygen`
-    - `mingw64-i686-gcc`, `mingw64-i686-gcc-g++`, `mingw64-i686-gcc-fortran` (if you have Octave/MinGW or if you have MATLAB 32-bit)
-    - `mingw64-x86_64-gcc`, `mingw64-x86_64-gcc-g++`, `mingw64-x86_64-gcc-fortran` (if you have MATLAB 64-bit)
+    - `mingw64-i686-gcc-core`, `mingw64-i686-gcc-g++`, `mingw64-i686-gcc-fortran` (if you have Octave/MinGW or if you have MATLAB 32-bit)
+    - `mingw64-x86_64-gcc-core`, `mingw64-x86_64-gcc-g++`, `mingw64-x86_64-gcc-fortran` (if you have MATLAB 64-bit)
 - Second, install precompiled librairies for BLAS, LAPACK, Boost and GSL:
-    - If you have Octave or MATLAB 32-bit, download [dynare-mingw32-libs.zip](http://www.dynare.org/DynareWiki/BuildingDynareFromSource?action=AttachFile&do=view&target=dynare-mingw32-libs.zip), and uncompress it in `c:\cygwin\usr\local\lib\mingw32`
-    - If you have MATLAB 64-bit, download [dynare-mingw64-libs.zip](http://www.dynare.org/DynareWiki/BuildingDynareFromSource?action=AttachFile&do=view&target=dynare-mingw64-libs.zip), and uncompress it in `c:\cygwin\usr\local\lib\mingw64`
+    - If you have Octave or MATLAB 32-bit, download [dynare-mingw32-libs.zip](http://www.dynare.org/build/dynare-mingw32-libs.zip), and uncompress it in `c:\cygwin\usr\local\lib\mingw32`
+    - If you have MATLAB 64-bit, download [dynare-mingw64-libs.zip](http://www.dynare.org/build/dynare-mingw64-libs.zip), and uncompress it in `c:\cygwin\usr\local\lib\mingw64`
+
+*Remark*: You need to make sure that Cygwin’s git is used and not a potentially installed msysgit. The latter typically happens when one installs msysgit and allows it to set a system path. This will result in wrong line endings and cryptic error messages à la "syntax error near unexpected token `fi'". In that case it might be necessary to uninstall msysgit and reinstall it without setting a system path.
 
 ### Compiling the preprocessor, Dynare++, the MEX for MATLAB and the documentation
 
-Download and uncompress the Dynare source tree, let’s say in `c:\cygwin\home\user\dynare`.
+Install the Dynare source tree, let’s say in `c:\cygwin\home\user\dynare` (by either uncompressing a source archive, or by cloning the git repository with `git clone --recursive http://github.com/DynareTeam/dynare.git`).
 
 Launch a Cygwin shell, and enter the Dynare source tree:
 ```
@@ -259,23 +262,19 @@ Configure and make:
 
 - Install [Homebrew](http://mxcl.github.io/homebrew/) and [Homebrew Science](https://github.com/Homebrew/homebrew-science)
 - Install the following brews:
-```
-    brew install automake
-    brew install gsl
-    brew install boost
-    brew install gfortran
-    brew install matlab2tikz --HEAD
-    brew install libmatio --with-hdf5
-    brew install slicot --with-default-integer-8
-```
+    - ```brew install automake```
+    - ```brew install gsl```
+    - ```brew install boost```
+    - ```brew install gfortran```
+    - ```brew install matlab2tikz --HEAD```
+    - ```brew install libmatio --with-hdf5```
+    - ```brew install slicot --with-default-integer-8```
 - **(Optional)** To compile Dynare mex files for use on Octave, first install Octave following the [Simple Installation Instructions](http://wiki.octave.org/Octave_for_MacOS_X#Simple_Installation_Instructions_3). Then, you will probably also want to install graphicsmagick via Homebrew with `brew install graphicsmagick`.
 - **(Optional)** To compile Dynare's documentation, first install the latest version of [MacTeX](http://www.tug.org/mactex/). Then install `doxygen`, `latex2html` and `texi2html` via Homebrew with the following commands:
-```
-    brew install doxygen
-    brew install latex2html
-    brew install texi2html
-```
-- **(On OS X 10.7 Only)** Copy [FlexLexer.h](http://www.dynare.org/DynareWiki/BuildingDynareFromSource?action=AttachFile&do=view&target=FlexLexer.h) into the `preprocessor` directory (there was an error in the `FlexLexer.h` file distributed with 10.7)
+    - ```brew install doxygen```
+    - ```brew install latex2html```
+    - ```brew install texi2html```
+- **(On OS X 10.7 Only)** Copy [FlexLexer.h](http://www.dynare.org/build/FlexLexer.h) into the `preprocessor` directory (there was an error in the `FlexLexer.h` file distributed with 10.7)
 - Finally, switch to the root dynare directory. Ensure your path contains `/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/bin:/opt/X11/bin:/usr/texbin:/usr/local/sbin`. Run:
     - `autoconf -si`
     - `./configure --with-matlab=/Applications/MATLAB_R2013a.app MATLAB_VERSION=8.1` for builds with Matlab or `./configure` for builds just using Octave

configure.ac

@@ -18,7 +18,7 @@ dnl You should have received a copy of the GNU General Public License
 dnl along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 AC_PREREQ([2.62])
-AC_INIT([dynare], [4.4-unstable])
+AC_INIT([dynare], [4.4.3])
 AC_CONFIG_SRCDIR([preprocessor/DynareMain.cc])
 AM_INIT_AUTOMAKE([1.11 -Wall -Wno-portability foreign no-dist-gzip dist-xz tar-pax])
 
@@ -83,10 +83,6 @@ CPPFLAGS="$CPPFLAGS_SAVED"
 # Don't use deprecated hash structures
 AC_DEFINE([BOOST_NO_HASH], [], [Don't use deprecated STL hash structures])
 
-# Check for dlopen(), needed by tests for estimation DLL
-AC_CHECK_LIB([dl], [dlopen], [LIBADD_DLOPEN="-ldl"], [])
-AC_SUBST([LIBADD_DLOPEN])
-
 # Check for libmatio, needed by Dynare++
 AX_MATIO
 AM_CONDITIONAL([HAVE_MATIO], [test "x$has_matio" = "xyes"])
@@ -160,6 +156,12 @@ fi
 AM_CONDITIONAL([HAVE_BLAS], [test x"$ax_blas_ok" = "xyes"])
 AM_CONDITIONAL([HAVE_LAPACK], [test x"$ax_lapack_ok" = "xyes"])
 
+case ${host_os} in
+  *mingw32*)
+    # Ensure that -lpthread is statically linked under MinGW
+    PTHREAD_LIBS="-Wl,-Bstatic -lpthread -Wl,-Bdynamic"
+    ;;
+esac
 AX_PTHREAD
 
 AC_CONFIG_FILES([Makefile
@@ -192,10 +194,6 @@ AC_CONFIG_FILES([Makefile
                  dynare++/kord/Makefile
                  dynare++/src/Makefile
                  mex/sources/Makefile
-                 mex/sources/estimation/Makefile
-                 mex/sources/estimation/tests/Makefile
-                 mex/sources/estimation/libmat/Makefile
-                 mex/sources/estimation/libmat/tests/Makefile
 ])
 
 AC_ARG_ENABLE([matlab], AS_HELP_STRING([--disable-matlab], [disable compilation of MEX files for MATLAB]), [], [enable_matlab=yes])

switch_dw @ 8b3d1404

-Subproject commit 29c446f7fe8a773860ebee347662c4ae2494c5e9
+Subproject commit 8b3d14040133ea5b622cde9ea79b3b0b41891bce

utilities_dw @ 216abedb

-Subproject commit b388d5eac8c8d092088db5ef805584ae4dd2d952
+Subproject commit 216abedb9ab4df5dd4dca07c721f7c07392801e9

doc/dynare.texi

@@ -77,8 +77,6 @@
 @emph{Algorithms}
 @end macro
 
-
-
 @macro customhead{title}
 @iftex
 @sp 1
@@ -97,7 +95,7 @@
 @c %**end of header
 
 @copying
-Copyright @copyright{} 1996-2013, Dynare Team.
+Copyright @copyright{} 1996-2014, Dynare Team.
 
 @quotation
 Permission is granted to copy, distribute and/or modify this document
@@ -351,15 +349,15 @@ institutions who cannot afford, or do not want to pay for, MATLAB and
 are willing to bear the concomitant performance loss.
 
 The development of Dynare is mainly done at
-@uref{http://www.cepremap.ens.fr, Cepremap} by a core team of
+@uref{http://www.cepremap.fr, Cepremap} by a core team of
 researchers who devote part of their time to software development.
 Currently the development team of Dynare is composed of Stéphane
 Adjemian (Université du Maine, Gains and Cepremap), Houtan Bastani
 (Cepremap), Michel Juillard (Banque de France), Frédéric Karamé
 (Université du Maine, Gains and Cepremap), Junior Maih (Norges Bank),
 Ferhat Mihoubi (Université Paris-Est Créteil, Epee and Cepremap), George
-Perendia, Johannes Pfeifer (University of Mannheim), Marco Ratto (JRC)
-and Sébastien Villemot (Cepremap).
+Perendia, Johannes Pfeifer (University of Mannheim), Marco Ratto (European Commission, Joint Research Centre - JRC)
+and Sébastien Villemot (OFCE – Sciences Po).
 Increasingly, the developer base is expanding, as tools developed by
 researchers outside of Cepremap are integrated into Dynare. Financial
 support is provided by Cepremap, Banque de France and DSGE-net (an
@@ -517,9 +515,8 @@ Please refer to the
 @uref{http://www.dynare.org/DynareWiki/InstallOnDebianOrUbuntu,Dynare
 Wiki} for detailed instructions.
 
-Dynare will be installed under @file{/usr/share/dynare} and
-@file{/usr/lib/dynare}. Documentation will be under
-@file{/usr/share/doc/dynare}.
+Dynare will be installed under @file{/usr/lib/dynare}. Documentation will be
+under @file{/usr/share/doc/dynare-doc}.
 
 @node On Mac OS X
 @subsection On Mac OS X
@@ -582,7 +579,7 @@ addpath c:\dynare\4.@var{x}.@var{y}\matlab
 Under Debian GNU/Linux or Ubuntu, type:
 
 @example
-addpath /usr/share/dynare/matlab
+addpath /usr/lib/dynare/matlab
 @end example
 
 Under Mac OS X, assuming that you have installed Dynare in the standard
@@ -682,7 +679,7 @@ In practice, the handling of the model file is done in two steps: in the
 first one, the model and the processing instructions written by the user
 in a @emph{model file} are interpreted and the proper MATLAB or GNU
 Octave instructions are generated; in the second step, the program
-actually runs the computations. Boths steps are triggered automatically
+actually runs the computations. Both steps are triggered automatically
 by the @code{dynare} command.
 
 @deffn {MATLAB/Octave command} dynare @var{FILENAME}[.mod] [@var{OPTIONS}@dots{}]
@@ -704,7 +701,7 @@ Contains variable declarations, and computing tasks
 
 @item @var{FILENAME}_dynamic.m
 @vindex M_.lead_lag_incidence
-Contains the dynamic model equations. Note that Dynare might introduce auxiliary equations and variables (@pxref{Auxiliary variables}). Outputs are the residuals of the dynamic model equations in the order the equations were declared and the Jacobian of the dynamic model equations. For higher order approximations also the Hessian and the third-order derivates are provided. When computing the Jacobian of the dynamic model, the order of the endogenous variables in the columns is stored in @code{M_.lead_lag_incidence}. The rows of this matrix represent time periods: the first row denotes a lagged (time t-1) variable, the second row a contemporaneous (time t) variable, and the third row a leaded (time t+1) variable. The colums of the matrix represent the endogenous variables in their order of declaration. A zero in the matrix means that this endogenous does not appear in the model in this time period. The value in the @code{M_.lead_lag_incidence} matrix corresponds to the column of that variable in the Jacobian of the dynamic model. Example: Let the second declared variable be @code{c} and the @code{(3,2)} entry of @code{M_.lead_lag_incidence} be @code{15}. Then the @code{15}th column of the Jacobian is the derivative with respect to @code{y(+1)}. 
+Contains the dynamic model equations. Note that Dynare might introduce auxiliary equations and variables (@pxref{Auxiliary variables}). Outputs are the residuals of the dynamic model equations in the order the equations were declared and the Jacobian of the dynamic model equations. For higher order approximations also the Hessian and the third-order derivatives are provided. When computing the Jacobian of the dynamic model, the order of the endogenous variables in the columns is stored in @code{M_.lead_lag_incidence}. The rows of this matrix represent time periods: the first row denotes a lagged (time t-1) variable, the second row a contemporaneous (time t) variable, and the third row a leaded (time t+1) variable. The columns of the matrix represent the endogenous variables in their order of declaration. A zero in the matrix means that this endogenous does not appear in the model in this time period. The value in the @code{M_.lead_lag_incidence} matrix corresponds to the column of that variable in the Jacobian of the dynamic model. Example: Let the second declared variable be @code{c} and the @code{(3,2)} entry of @code{M_.lead_lag_incidence} be @code{15}. Then the @code{15}th column of the Jacobian is the derivative with respect to @code{y(+1)}.
 
 @item @var{FILENAME}_static.m
 Contains the long run static model equations. Note that Dynare might introduce auxiliary equations and variables (@pxref{Auxiliary variables}). Outputs are the residuals of the static model equations in the order the equations were declared and the Jacobian of the static equations. Entry @code{(i,j)} of the Jacobian represents the derivative of the @code{i}th static model equation with respect to the @code{j}th model variable in declaration order.
@@ -715,6 +712,15 @@ These files may be looked at to understand errors reported at the simulation sta
 
 @code{dynare} will then run the computing tasks by executing @file{@var{FILENAME}.m}.
 
+A few words of warning is warranted here: the filename of the
+@file{.mod} file should be chosen in such a way that the generated
+@file{.m} files described above do not conflict with @file{.m} files
+provided by MATLAB/Octave or by Dynare. Not respecting this rule could
+cause crashes or unexpected behaviour. In particular, it means that
+the @file{.mod} file cannot be given the name of a MATLAB/Octave or
+Dynare command. Under Octave, it also means that the @file{.mod} file
+cannot be named @file{test.mod}.
+
 @optionshead
 
 @table @code
@@ -747,7 +753,7 @@ the macro-processor independently of the rest of Dynare toolbox.
 
 @item nolinemacro
 Instructs the macro-preprocessor to omit line numbering information in
-the intermediary @file{.mod} file created after the maco-processing
+the intermediary @file{.mod} file created after the macro-processing
 step. Useful in conjunction with @code{savemacro} when one wants that to
 reuse the intermediary @file{.mod} file, without having it cluttered by
 line numbering directives.
@@ -849,7 +855,7 @@ The output of Dynare is left into three main variables in the
 MATLAB/Octave workspace:
 
 @defvr {MATLAB/Octave variable} M_
-Structure containing various informations about the model.
+Structure containing various information about the model.
 @end defvr
 
 @defvr {MATLAB/Octave variable} options_
@@ -865,11 +871,11 @@ Structure containing the various results of the computations.
 @node Dynare hooks
 @section Dynare hooks
 
-It is possible to call pre and post dynare preprocessor hooks written as matlab scripts.
+It is possible to call pre and post Dynare preprocessor hooks written as MATLAB scripts.
 The script @file{@var{MODFILENAME}/hooks/priorprocessing.m} is executed before the
-call to Dynare's preprocessor, and  can be used to programatically transform the mod file
+call to Dynare's preprocessor, and  can be used to programmatically transform the mod file
 that will be read by the preprocessor. The script @file{@var{MODFILENAME}/hooks/postprocessing.m}
-is executed just after the call to Dynare's preprocessor, and can be used to programatically
+is executed just after the call to Dynare's preprocessor, and can be used to programmatically
 transform the files generated by Dynare's preprocessor before actual computations start. The
 pre and/or post dynare preprocessor hooks are executed if and only if the aforementioned scripts
 are detected in the same folder as the the model file, @file{@var{FILENAME}.mod}.
@@ -1020,9 +1026,9 @@ extension or if the filename contains a non-alphanumeric character;
 
 Declarations of variables and parameters are made with the following commands:
 
-@deffn Command var @var{VARIABLE_NAME} [$@var{LATEX_NAME}$ [(long_name=@var{QUOTED_STRING})]]@dots{};
-@deffnx Command var (deflator = @var{MODEL_EXPRESSION}) @var{VARIABLE_NAME} [$@var{LATEX_NAME}$ [(long_name=@var{QUOTED_STRING})]]@dots{};
-@deffnx Command var (log_deflator = @var{MODEL_EXPRESSION}) @var{VARIABLE_NAME} [$@var{LATEX_NAME}$ [(long_name=@var{QUOTED_STRING})]]@dots{};
+@deffn Command var @var{VARIABLE_NAME} [$@var{LATEX_NAME}$] [(long_name=@var{QUOTED_STRING})]@dots{};
+@deffnx Command var (deflator = @var{MODEL_EXPRESSION}) @var{VARIABLE_NAME} [$@var{LATEX_NAME}$] [(long_name=@var{QUOTED_STRING})]@dots{};
+@deffnx Command var (log_deflator = @var{MODEL_EXPRESSION}) @var{VARIABLE_NAME} [$@var{LATEX_NAME}$] [(long_name=@var{QUOTED_STRING})]@dots{};
 
 @descriptionhead
 
@@ -1075,7 +1081,7 @@ var c $C$ (long_name=`Consumption');
 
 @end deffn
 
-@deffn Command varexo @var{VARIABLE_NAME} [$@var{LATEX_NAME}$ [(long_name=@var{QUOTED_STRING})]]@dots{};
+@deffn Command varexo @var{VARIABLE_NAME} [$@var{LATEX_NAME}$] [(long_name=@var{QUOTED_STRING})]@dots{};
 
 @descriptionhead
 
@@ -1103,7 +1109,7 @@ varexo m gov;
 
 @end deffn
 
-@deffn Command varexo_det @var{VARIABLE_NAME} [$@var{LATEX_NAME}$ [(long_name=@var{QUOTED_STRING})]]@dots{};
+@deffn Command varexo_det @var{VARIABLE_NAME} [$@var{LATEX_NAME}$] [(long_name=@var{QUOTED_STRING})]@dots{};
 
 @descriptionhead
 
@@ -1140,7 +1146,7 @@ varexo_det tau;
 
 @end deffn
 
-@deffn Command parameters @var{PARAMETER_NAME} [$@var{LATEX_NAME}$ [(long_name=@var{QUOTED_STRING})]]@dots{};
+@deffn Command parameters @var{PARAMETER_NAME} [$@var{LATEX_NAME}$] [(long_name=@var{QUOTED_STRING})]@dots{};
 
 @descriptionhead
 
@@ -1961,7 +1967,7 @@ variables will have a name beginning with @code{AUX_EXO_LEAD} or
 @code{AUX_EXO_LAG} respectively).
 
 Another transformation is done for the @code{EXPECTATION}
-operator. For each occurence of this operator, Dynare creates an
+operator. For each occurrence of this operator, Dynare creates an
 auxiliary variable defined by a new equation, and replaces the
 expectation operator by a reference to the new auxiliary variable. For
 example, the expression @code{EXPECTATION(-1)(x(+1))} is replaced by
@@ -2003,20 +2009,20 @@ possibly terminal) conditions. It is also necessary to provide initial
 guess values for non-linear solvers. This section describes the
 statements used for those purposes.
 
-In many contexts (determistic or stochastic), it is necessary to
+In many contexts (deterministic or stochastic), it is necessary to
 compute the steady state of a non-linear model: @code{initval} then
 specifies numerical initial values for the non-linear solver. The
 command @code{resid} can be used to compute the equation residuals for
 the given initial values.
 
-Used in perfect foresight mode, the types of forward-loking models for
+Used in perfect foresight mode, the types of forward-looking models for
 which Dynare was designed require both initial and terminal
 conditions. Most often these initial and terminal conditions are
 static equilibria, but not necessarily.
 
 One typical application is to consider an economy at the equilibrium,
 trigger a shock in first period, and study the trajectory of return at
-the initial equilbrium. To do that, one needs @code{initval} and
+the initial equilibrium. To do that, one needs @code{initval} and
 @code{shocks} (@pxref{Shocks on exogenous variables}.
 
 Another one is to study, how an economy, starting from arbitrary
@@ -2059,7 +2065,7 @@ implemented in @code{simul}.
 For this last reason, it necessary to provide values for all the
 endogenous variables in an @code{initval} block (even though,
 theoretically, initial conditions are only necessary for lagged
-variables). If some variables, endogenous or exogenous, are not mentionned in the
+variables). If some variables, endogenous or exogenous, are not mentioned in the
 @code{initval} block, a zero value is assumed.
 
 Note that if the @code{initval} block is immediately followed by a
@@ -2134,7 +2140,7 @@ form:
 @var{VARIABLE_NAME} = @var{EXPRESSION};
 @end example
 
-The @code{endval} block makes only sense in a determistic model, and
+The @code{endval} block makes only sense in a deterministic model, and
 serves two purposes.
 
 First, it sets the terminal conditions for all the periods succeeding
@@ -2145,8 +2151,8 @@ for the non-linear solver implemented in @code{simul}.
 
 For this last reason, it necessary to provide values for all the
 endogenous variables in an @code{endval} block (even though,
-theoretically, initial conditions are only necessary for forward
-variables). If some variables, endogenous or exogenous, are not mentionned in the
+theoretically, terminal conditions are only necessary for forward
+variables). If some variables, endogenous or exogenous, are not mentioned in the
 @code{endval} block, the value assumed is that of the last
 @code{initval} block or @code{steady} command.
 
@@ -2195,12 +2201,90 @@ steady;
 The initial equilibrium is computed by @code{steady} for @code{x=1},
 and the terminal one, for @code{x=2}.
 
+@examplehead
+
+@example
+var c k;
+varexo x;
+@dots{}
+model;
+c + k - aa*x*k(-1)^alph - (1-delt)*k(-1);
+c^(-gam) - (1+bet)^(-1)*(aa*alph*x(+1)*k^(alph-1) + 1 - delt)*c(+1)^(-gam);
+end;
+
+initval;
+c = 1.2;
+k = 12;
+x = 1;
+end;
+
+endval;
+c = 2;
+k = 20;
+x = 1.1;
+end;
+simul(periods=200);
+
+@end example
+
+In this example, the problem is finding the optimal path for consumption
+and capital for the periods t=1 to T=200, given the path of the exogenous
+technology level @code{x}. Setting @code{x=1.1} in the
+@code{endval}-block without a @code{shocks}-block implies that technology
+jumps to this new level in t=1 and stays there forever. Because the law
+of motion for capital is backward-looking, we also need an initial
+condition for @code{k} at time 0, specified in the @code{initval}-block.
+Similarly, because the Euler equation is forward-looking, we need a
+terminal condition for @code{c} at t=201, which is specified in the
+@code{endval}-block. Specifying @code{c} in the @code{initval}-block and
+@code{k} in the @code{endval}-block has no impact on the results: due to
+the optimization problem in the first period being to choose @code{c,k}
+at t=1 given predetermined capital stock @code{k} inherited from t=0 as
+well as the current and future values for technology, the value for
+@code{c} at time t=0 plays no role. The same applies to the choice of
+@code{c,k} at time t=200, which does not depend on @code{k} at t=201. As
+the Euler equation shows, that choice only depends on current capital as
+well as future consumption @code{c} and technology @code{x}, but not on
+future capital @code{k}. The intuitive reason is that those variables are
+the consequence of optimization problems taking place in at periods t=0
+and t=201, respectively, which are not considered. Thus, when specifying
+those values in the @code{initval} and @code{endval}-blocks, Dynare takes
+them as given and basically assumes that there were realizations
+of exogenous variables and states (basically initial/terminal conditions
+at the unspecified time periods t<0 and t>201) that make those choices
+equilibrium values.
+ 
+This also suggest another way of looking at the use of @code{steady}
+after @code{initval} and @code{endval}. Instead of saying that the
+implicit unspecified conditions before and after the simulation range
+have to fit the initial/terminal conditions of the endogenous variables
+in those blocks, @code{steady} specifies that those conditions at t<0 and
+t>201 are equal to being at the steady state given the exogenous
+variables in the @code{initval} and @code{endval}-blocks and sets the
+endogenous variables at t=0 and t=201 to the corresponding steady state
+equilibrium values.
+ 
+The fact that @code{c} at t=0 and @code{k} at t=201 specified in
+@code{initval} and @code{endval} are taken as given has an important
+implication for plotting the simulated vector for the endogenous
+variables: this vector will also contain the initial and terminal
+conditions and thus is 202 periods long in the example. When you specify
+arbitrary values for the initial and terminal conditions for forward- and
+backward-looking variables, respectively, these values can be very far
+away from the endogenously determined values at t=1 and t=200. While the
+values at t=0 and t=201 are unrelated to the dynamics for 0<t<201, they
+may result in strange-looking large jumps. In the example above,
+consumption will display a large jump from t=0 to t=1 and capital will
+jump from t=200 to t=201.
+
 @end deffn
 
 @deffn Block histval ;
 
 @descriptionhead
 
+@customhead{In a deterministic perfect foresight context}
+
 In models with lags on more than one period, the @code{histval} block
 permits to specify different historical initial values for different
 periods.
@@ -2228,6 +2312,14 @@ variables are initialized at their steady state value at period 0 and
 before (except when a @code{steady} command doesn't follow an
 @code{initval} block).
 
+@customhead{In a stochastic simulation context}
+
+In the context of stochastic simulations, @code{histval} allows setting
+the starting point of those simulations in the state space (it does not
+affect the starting point for impulse response functions). As for the case of
+perfect foresight simulations, all not explicitly specified variables are set to 0.
+Moreover, as only states enter the recursive policy functions, all values specified for control variables will be ignored.
+
 @examplehead
 
 @example
@@ -2336,7 +2428,7 @@ in each period. In Dynare, these random values follow a normal
 distribution with zero mean, but it belongs to the user to specify the
 variability of these shocks. The non-zero elements of the matrix of
 variance-covariance of the shocks can be entered with the @code{shocks}
-command. Or, the entire matrix can be direclty entered with
+command. Or, the entire matrix can be directly entered with
 @code{Sigma_e} (this use is however deprecated).
 
 If the variance of an exogenous variable is set to zero, this variable
@@ -2406,10 +2498,6 @@ values (xx);
 end;
 @end example
 
-In case of conditional forecasts using an extended path method, 
-the shock command is extended in order to determine the nature of the expectation 
-on the endogenous variable paths. The command @code{expectation} has, in this case, 
-to be added after the @code{values} command (@pxref{Forecasting}).
 
 @customhead{In stochastic context}
 
@@ -2451,7 +2539,7 @@ var v, w = 2;
 end;
 @end example
 
-@customhead{Mixing determininistic and stochastic shocks}
+@customhead{Mixing deterministic and stochastic shocks}
 
 It is possible to mix deterministic and stochastic shocks to build
 models where agents know from the start of the simulation about future
@@ -2520,7 +2608,7 @@ discouraged.} You should use a @code{shocks} block instead.
 
 This special variable specifies directly the covariance matrix of the
 stochastic shocks, as an upper (or lower) triangular matrix. Dynare
-builds the corresponding symmetrix matrix. Each row of the triangular
+builds the corresponding symmetric matrix. Each row of the triangular
 matrix, except the last one, must be terminated by a semi-colon
 @code{;}. For a given element, an arbitrary @var{EXPRESSION} is
 allowed (instead of a simple constant), but in that case you need to
@@ -2682,7 +2770,7 @@ values:
 
 @item 1
 In this mode, all the parameters are changed simultaneously, and the
-distance between the boudaries for each parameter is divided in as
+distance between the boundaries for each parameter is divided in as
 many intervals as there are steps (as defined by @code{homotopy_steps}
 option); the problem is solves as many times as there are steps.
 
@@ -2724,7 +2812,7 @@ variables are NOT at the value expected by the user
 Default is @code{0}.
 
 @item nocheck
-Don't check the steady state values when they are provided explicitely either by a steady state file or a @code{steady_state_model} block. This is useful for models with unit roots as, in this case, the steady state is not unique or doesn't exist.
+Don't check the steady state values when they are provided explicitly either by a steady state file or a @code{steady_state_model} block. This is useful for models with unit roots as, in this case, the steady state is not unique or doesn't exist.
 
 @item markowitz = @var{DOUBLE}
 Value of the Markowitz criterion, used to select the pivot. Only used
@@ -2837,25 +2925,42 @@ steady(homotopy_mode = 1, homotopy_steps = 50);
 
 If you know how to compute the steady state for your model, you can
 provide a MATLAB/Octave function doing the computation instead of
-using @code{steady}. If your MOD-file is called
-@file{@var{FILENAME}.mod}, the steady state file should be called
-@file{@var{FILENAME}_steadystate.m}.
-
-Again, there are two options for creating this file:
+using @code{steady}. Again, there are two options for doing that:
 
 @itemize
 
 @item
-The easiest way is to write a @code{steady_state_model} block.
+The easiest way is to write a @code{steady_state_model} block, which
+is described below in more details. See also @file{fs2000.mod} in the
+@file{examples} directory for an example.
+
+The steady state file generated by Dynare will be called
+@file{@var{FILENAME}_steadystate2.m}.
 
 @item
-You can write the corresponding Matlab function by hand. See @file{fs2000_steadystate.m}
-in the @file{examples} directory for an example. This option
-gives a bit more flexibility, at the expense of a heavier
-programming burden and a lesser efficiency.
+You can write the corresponding MATLAB function by hand. If your
+MOD-file is called @file{@var{FILENAME}.mod}, the steady state file
+must be called @file{@var{FILENAME}_steadystate.m}. See
+@file{NK_baseline_steadystate.m} in the @file{examples} directory for
+an example. This option gives a bit more flexibility, at the expense
+of a heavier programming burden and a lesser efficiency.
 
 @end itemize
 
+Note that both files allow to update parameters in each call of
+the function. This allows for example to calibrate a model to a labor
+supply of 0.2 in steady state by setting the labor disutility parameter
+to a corresponding value (see @file{NK_baseline_steadystate.m} in the
+@file{examples} directory). They can also be used in estimation
+where some parameter may be a function of an estimated parameter
+and needs to be updated for every parameter draw. For example, one might
+ want to set the capital utilization cost parameter as a function
+of the discount rate to ensure that capacity utilization is 1 in steady
+state. Treating both parameters as independent or not updating one as
+a function of the other would lead to wrong results. But this also means
+that care is required. Do not accidentally overwrite your parameters
+with new values as it will lead to wrong results.
+
 @anchor{steady_state_model}
 @deffn Block steady_state_model ;
 
@@ -3017,6 +3122,7 @@ must have full rank.
 @xref{solve_algo}, for the possible values and their meaning.
 
 @item qz_zero_threshold = @var{DOUBLE}
+@anchor{qz_zero_threshold}
 Value used to test if a generalized eigenvalue is 0/0 in the generalized
 Schur decomposition  (in which case  the model  does not admit  a unique
 solution). Default: @code{1e-6}.
@@ -3155,13 +3261,29 @@ models, this return to equilibrium is only an asymptotic phenomenon,
 which one must approximate by an horizon of simulation far enough in
 the future.  Another exercise for which Dynare is well suited is to
 study the transition path to a new equilibrium following a permanent
-shock.  For deterministic simulations, Dynare uses a Newton-type
-algorithm, first proposed by @cite{Laffargue (1990)} and
-@cite{Boucekkine (1995)}, instead of a first order technique like the
-one proposed by @cite{Fair and Taylor (1983)}, and used in earlier
-generation simulation programs. We believe this approach to be in
-general both faster and more robust. The details of the algorithm can
-be found in @cite{Juillard (1996)}.
+shock.  For deterministic simulations, the numerical problem consists of solving
+ a nonlinar system of simultaneous equations in @code{n} endogenous
+ variables in @code{T} periods. Dynare offers several algorithms for
+ solving this problem, which can be chosen via the
+ @code{stack_solve_algo}-option. By default (@code{stack_solve_algo=0}),
+Dynare uses a Newton-type method to solve the simultaneous equation
+system. Because the resulting Jacobian is in the order of @code{n} by
+@code{T} and hence will be very large for long simulations with many
+variables, Dynare makes use of the sparse matrix capacities of
+MATLAB/Octave. A slower but potentially less memory consuming alternative
+(@code{stack_solve_algo=6}) is based on a Newton-type algorithm first
+proposed by @cite{Laffargue (1990)} and @cite{Boucekkine (1995)}, which
+uses relaxation techniques. Thereby, the algorithm avoids ever storing
+the full Jacobian. The details of the algorithm can be found in
+@cite{Juillard (1996)}. The third type of algorithms makes use of block
+decomposition techniques (divide-and-conquer methods) that exploit the
+structure of the model. The principle is to identify recursive and
+simultaneous blocks in the model structure and use this information to
+aid the solution process.  These solution algorithms can provide a
+significant speed-up on large models.
+
+
+
 
 @deffn Command simul ;
 @deffnx Command simul (@var{OPTIONS}@dots{});
@@ -3239,7 +3361,7 @@ periods. Only used when @code{stack_solve_algo = 5}. Default: @code{1}.
 @item datafile = @var{FILENAME}
 If the variables of the model are not constant over time, their
 initial values, stored in a text file, could be loaded, using that
-option, as initial values before a deteministic simulation.
+option, as initial values before a deterministic simulation.
 @end table
 
 @outputhead
@@ -3376,7 +3498,7 @@ increase it for highly autocorrelated processes. Default: @code{512}.
 @item irf = @var{INTEGER}
 @anchor{irf}
 Number of periods on which to compute the IRFs. Setting @code{irf=0},
-suppresses the plotting of IRF's. Default: @code{40}.
+suppresses the plotting of IRFs. Default: @code{40}.
 
 @item irf_shocks = ( @var{VARIABLE_NAME} [[,] @var{VARIABLE_NAME} @dots{}] )
 @anchor{irf_shocks}
@@ -3462,9 +3584,7 @@ problems. Default: @code{1.000001} (except when estimating with
 @code{0.999999} in that case; @pxref{Estimation}).
 
 @item qz_zero_threshold = @var{DOUBLE}
-Value used to test if a generalized eigenvalue is 0/0 in the generalized
-Schur decomposition  (in which case  the model  does not admit  a unique
-solution). Default: @code{1e-6}.
+@xref{qz_zero_threshold}.
 
 @item replic = @var{INTEGER}
 Number of simulated series used to compute the IRFs. Default: @code{1}
@@ -3503,7 +3623,7 @@ period(s). The periods must be strictly positive. Conditional variances are give
 decomposition provides the decomposition of the effects of shocks upon
 impact. The results are stored in
 @code{oo_.conditional_variance_decomposition}
-(@pxref{oo_.conditional_variance_decomposition}). The variance decomposition is only conducted, if theoretical moments are requested, i.e. using the @code{periods=0}-option. In case of @code{order=2}, Dynare provides a second-order accurate approximation to the true second moments based on the linear terms of the second-order solution (see @cite{Kim, Kim, Schaumburg and Sims (2008)}). 
+(@pxref{oo_.conditional_variance_decomposition}). The variance decomposition is only conducted, if theoretical moments are requested, i.e. using the @code{periods=0}-option. In case of @code{order=2}, Dynare provides a second-order accurate approximation to the true second moments based on the linear terms of the second-order solution (see @cite{Kim, Kim, Schaumburg and Sims (2008)}). Note that the unconditional variance decomposition (i.e. at horizon infinity) is automatically conducted if theoretical moments are requested (@pxref{oo_.variance_decomposition})
 
 @item pruning
 Discard higher order terms when iteratively computing simulations of
@@ -3529,7 +3649,7 @@ Determines the algorithm used to solve the Sylvester equation for block decompos
 
 @item default
 Uses the default solver for Sylvester equations (@code{gensylv}) based
-on Ondra Kamenik algorithm (see
+on Ondra Kamenik's algorithm (see
 @uref{http://www.dynare.org/documentation-and-support/dynarepp/sylvester.pdf/at_download/file,the
 Dynare Website} for more information).
 
@@ -3543,7 +3663,7 @@ Default value is @code{default}
 
 @item sylvester_fixed_point_tol = @var{DOUBLE}
 @anchor{sylvester_fixed_point_tol}
-It is the convergence criterion used in the fixed point sylvester solver. Its default value is 1e-12.
+It is the convergence criterion used in the fixed point Sylvester solver. Its default value is 1e-12.
 
 @item dr = @var{OPTION}
 @anchor{dr}
@@ -3581,7 +3701,11 @@ The convergence criterion used in the logarithmic reduction algorithm. Its defau
 The maximum number of iterations used in the logarithmic reduction algorithm. Its default value is 100.
 
 @item loglinear
-@xref{loglinear}.
+@xref{loglinear}. Note that ALL variables are log-transformed by using the Jacobian transformation,
+not only selected ones. Thus, you have to make sure that your variables have strictly positive
+steady states. @code{stoch_simul} will display the moments, decision rules,
+and impulse responses for the log-linearized variables. The decision rules saved
+in @code{oo_.dr} and the simulated variables will also be the ones for the log-linear variables.
 
 @end table
 
@@ -3648,7 +3772,7 @@ number of the matrix in the cell array corresponds to the order of
 autocorrelation. The option @code{ar} specifies the number of
 autocorrelation matrices available. Contains theoretical
 autocorrelations if the @code{periods} option is not present (or an approximation thereof for @code{order=2}), and
-empirical autocorrelations otherwise.
+empirical autocorrelations otherwise. The field is only created if stationary variables are present.
 
 The element @code{oo_.autocorr@{i@}(k,l)} is equal to the correlation
 between @math{y^k_t} and @math{y^l_{t-i}}, where @math{y^k}
@@ -3675,7 +3799,7 @@ details. Beware, this is the @i{autocorrelation} function, not the
 @i{autocovariance} function.
 
 @item oo_.gamma@{nar+2@}
-Variance decomposition.
+Unconditional variance decomposition @pxref{oo_.variance_decomposition}
 
 @item oo_.gamma@{nar+3@}
 If a second order approximation has been requested, contains the
@@ -3686,6 +3810,22 @@ In case of @code{order=2}, the theoretical second moments are a second order acc
 
 @end defvr
 
+@anchor{oo_.variance_decomposition}
+@defvr {MATLAB/Octave variable} oo_.variance_decomposition
+After a run of @code{stoch_simul} when requesting theoretical moments (@code{periods=0}), contains a matrix with the result of the unconditional variance decomposition (i.e. at horizon infinity). The first dimension corresponds to the endogenous variables (in the order of declaration) and the second dimension corresponds to exogenous variables (in the order of declaration). Numbers are in percent and sum up to 100 across columns.
+@end defvr
+
+@anchor{oo_.conditional_variance_decomposition}
+@defvr {MATLAB/Octave variable} oo_.conditional_variance_decomposition
+After a run of @code{stoch_simul} with the
+@code{conditional_variance_decomposition} option, contains a
+three-dimensional array with the result of the decomposition. The
+first dimension corresponds to forecast horizons (as declared with the
+option), the second dimension corresponds to endogenous variables (in
+the order of declaration), the third dimension corresponds to
+exogenous variables (in the order of declaration).
+@end defvr
+
 @defvr {MATLAB/Octave variable} oo_.irfs
 After a run of @code{stoch_simul} with option @code{irf} different
 from zero, contains the impulse responses, with the following naming
@@ -3695,11 +3835,10 @@ For example, @code{oo_.irfs.gnp_ea} contains the effect on @code{gnp}
 of a one standard deviation shock on @code{ea}.
 @end defvr
 
-@vindex oo_.dr
 The approximated solution of a model takes the form of a set of decision
 rules or transition equations expressing the current value of the endogenous
 variables of the model as function of the previous state of the model and
-shocks oberved at the beginning of the period. The decision rules are stored
+shocks observed at the beginning of the period. The decision rules are stored
 in the structure @code{oo_.dr} which is described below.
 
 @deffn Command extended_path ;
@@ -3980,16 +4119,6 @@ three times in the unfolded @math{G_3} matrix, they must be multiplied
 by 3 when computing the decision rules.
 @end itemize
 
-@anchor{oo_.conditional_variance_decomposition}
-@defvr {MATLAB/Octave variable} oo_.conditional_variance_decomposition
-After a run of @code{stoch_simul} with the
-@code{conditional_variance_decomposition} option, contains a
-three-dimensional array with the result of the decomposition. The
-first dimension corresponds to forecast horizons (as declared with the
-option), the second dimension corresponds to endogenous variables (in
-the order of declaration), the third dimension corresponds to
-exogenous variables (in the order of declaration).
-@end defvr
 
 @node Estimation
 @section Estimation
@@ -4185,7 +4314,7 @@ apply.
 
 The following line:
 @example
-corr eps_1, eps_2, 0.5,  ,  , beta_pdf, 0, 0.3, -1, 1;
+corr eps_1, eps_2, 0.5,  ,  , beta_pdf, 0, 0.3, -1, 1;
 @end example
 sets a generalized beta prior for the correlation between @code{eps_1} and
 @code{eps_2} with mean 0 and variance 0.3. By setting
@@ -4197,7 +4326,7 @@ respectively. The initial value is set to 0.5.
 
 Similarly, the following line:
 @example
-corr eps_1, eps_2, 0.5,  -0.5,  1, beta_pdf, 0, 0.3, -1, 1;
+corr eps_1, eps_2, 0.5,  -0.5,  1, beta_pdf, 0, 0.3, -1, 1;
 @end example
 sets the same generalized beta distribution as before, but now truncates
 this distribution to [-0.5,1] through the use of @var{LOWER_BOUND} and
@@ -4250,7 +4379,7 @@ stderr VARIABLE_NAME | corr VARIABLE_NAME_1, VARIABLE_NAME_2 | PARAMETER_NAME
 @table @code
 
 @item use_calibration
-For not specifically intialized parameters, use the deep parameters and the elements of the covariance matrix specified in the @code{shocks}-block from calibration as starting values for estimation. For components of the @code{shocks}-block that were not explicitly specified during calibration or which violate the prior, the prior mean is used.
+For not specifically initialized parameters, use the deep parameters and the elements of the covariance matrix specified in the @code{shocks} block from calibration as starting values for estimation. For components of the @code{shocks} block that were not explicitly specified during calibration or which violate the prior, the prior mean is used.
 @end table
 
 @xref{estimated_params}, for the meaning and syntax of the various components.
@@ -4313,23 +4442,45 @@ acceptance ratio  should be close to  one third or one  quarter. If this
 not the case, you can stop the MCMC (@code{Ctrl-C}) and change the value
 of option @code{mh_jscale} (see below).
 
+Note that by default Dynare generates random numbers using the algorithm
+@code{mt199937ar} (@i{ie} Mersenne Twister method) with a seed set equal
+to @code{0}.   Consequently the MCMCs  in Dynare are  deterministic: one
+will  get  exactly  the  same   results  across  different  Dynare  runs
+(@i{ceteris paribus}).  For instance, the posterior moments or posterior
+densities  will be  exactly the  same. This  behaviour allows  to easily
+identify the  consequences of a change  on the model, the  priors or the
+estimation options. But one may also  want to check that across multiple
+runs, with  different sequences of  proposals, the returned  results are
+almost  identical. This  should  be  true if  the  number of  iterations
+(@i{ie} the value of @code{mh_replic}) is important enough to ensure the
+convergence of  the MCMC to its  ergodic distribution. In this  case the
+default behaviour of the random number generators in not wanted, and the
+user  should set  the  seed according  to the  system  clock before  the
+estimation command using the following command:
+
+@example
+set_dynare_seed('clock');
+@end example
+
+@noindent so that the sequence of proposals will be different across different runs.
+
 @algorithmshead
 
-The Monte Carlo Markov Chain (MCMC) diagnostics are generated
-by the estimation command if @ref{mh_replic} is larger than 2000 and if 
-option @ref{nodiagnostic} is not used. If @ref{mh_nblocks} is equal to one,
-the convergence diagnostics of @cite{Geweke (1992,1999)} is computed. It uses a
-chi square test to compare the means of the first and last draws specified by
-@ref{geweke_interval} after discarding the burnin of @ref{mh_drop}. The test is
-computed using variance estimates under the assumption of no serial correlation
-as well as using tapering windows specified in @ref{taper_steps}.
-If @ref{mh_nblocks}  is larger  than 1, the convergence diagnostics of 
-@cite{Brooks and Gelman (1998)} are used instead.
-As described in section  3 of @cite{Brooks and Gelman (1998)} the univariate
-convergence diagnostics are  based on comparing pooled  and within MCMC moments 
-(Dynare displays the  second and third order  moments, and
-the length of  the Highest Probability Density interval  covering 80% of
-the  posterior   distribution).   Due  to  computational   reasons,  the
+The Monte  Carlo Markov  Chain (MCMC) diagnostics  are generated  by the
+estimation command if @ref{mh_replic} is  larger than 2000 and if option
+@ref{nodiagnostic} is not used. If @ref{mh_nblocks} is equal to one, the
+convergence diagnostics  of @cite{Geweke  (1992,1999)} is  computed.  It
+uses a chi square test to compare  the means of the first and last draws
+specified  by  @ref{geweke_interval}  after  discarding  the  burnin  of
+@ref{mh_drop}. The test  is computed using variance  estimates under the
+assumption of  no serial correlation  as well as using  tapering windows
+specified in  @ref{taper_steps}.  If @ref{mh_nblocks} is  larger than 1,
+the convergence diagnostics of @cite{Brooks  and Gelman (1998)} are used
+instead.  As described  in section 3 of @cite{Brooks  and Gelman (1998)}
+the univariate convergence diagnostics are based on comparing pooled and
+within MCMC moments (Dynare displays the second and third order moments,
+and the length of the  Highest Probability Density interval covering 80%
+of  the  posterior distribution).   Due  to  computational reasons,  the
 multivariate  convergence diagnostic  does not  follow @cite{Brooks  and
 Gelman (1998)}  strictly, but rather  applies their idea  for univariate
 convergence  diagnostics  to  the  range  of  the  posterior  likelihood
@@ -4359,7 +4510,7 @@ The name of the sheet with the data in an Excel file
 
 @item xls_range = @var{RANGE}
 @anchor{xls_range}
-The range with the data in an Excel file
+The range with the data in an Excel file. For example, @code{xls_range=B2:D200}
 
 @item nobs = @var{INTEGER}
 @anchor{nobs}
@@ -4380,7 +4531,7 @@ The number of the first observation to be used. Default: @code{1}
 @item prefilter = @var{INTEGER}
 @anchor{prefilter}
 A value of @code{1} means that the estimation procedure will demean
-the data. Default: @code{0}, @i{i.e.} no prefiltering
+each data series by its empirical mean. Default: @code{0}, @i{i.e.} no prefiltering
 
 @item presample = @var{INTEGER}
 @anchor{presample}
@@ -4390,8 +4541,8 @@ likelihood. These first observations are used as a training sample. Default: @co
 @item loglinear
 @anchor{loglinear}
 Computes a log-linear approximation of the model instead of a linear
-approximation. The data must correspond to the definition of the
-variables used in the model. Default: computes a linear approximation
+approximation. As always in the context of estimation, the data must correspond to the definition of the
+variables used in the model (see @cite{Pfeifer 2013} for more details on how to correctly specify observation equations linking model variables and the data). If you specify the loglinear option, Dynare will take the logarithm of both your model variables and of your data as it assumes the data to correspond to the original non-logged model variables. The displayed posterior results like impulse responses, smoothed variables, and moments will be for the logged variables, not the original un-logged ones. Default: computes a linear approximation
 
 @item plot_priors = @var{INTEGER}
 Control the plotting of priors:
@@ -4404,7 +4555,7 @@ No prior plot
 @item 1
 Prior density for each estimated parameter is plotted. It is important
 to check that the actual shape of prior densities matches what you
-have in mind. Ill choosen values for the prior standard density can
+have in mind. Ill-chosen values for the prior standard density can
 result in absurd prior densities.
 @end table
 
@@ -4450,7 +4601,11 @@ Default value is @code{1}. For advanced use only.
 For internal use and testing only.
 
 @item conf_sig = @var{DOUBLE}
-@xref{conf_sig}.
+Confidence interval used for classical forecasting after estimation. See @xref{conf_sig}.
+
+@item mh_conf_sig = @var{DOUBLE}
+@anchor{mh_conf_sig} 
+Confidence/HPD interval used for the computation of prior and  posterior statistics like: parameter distributions, prior/posterior moments, conditional variance decomposition, impulse response functions, Bayesian forecasting. Default: @code{0.9} 
 
 @item mh_replic = @var{INTEGER}
 @anchor{mh_replic} Number of replications for Metropolis-Hastings
@@ -4646,7 +4801,7 @@ Available options are:
 Maximum number of iterations. Default: @code{1000}
 
 @item 'NumgradAlgorithm'
-Possible values are @code{2}, @code{3} and @code{5} respectively corresponding to the two, three and five points formula used to compute the gradient of the objective function (see @cite{Abramowitz and Stegun (1964)}). Values @code{13} and @code{15} are more experimental. If perturbations on the right and the left increase the value of the objective function (we minimize this function) then we force the corresponding element of the gradient to be zero. The idea is to temporarly reduce the size of the optimization problem. Default: @code{2}.
+Possible values are @code{2}, @code{3} and @code{5} respectively corresponding to the two, three and five points formula used to compute the gradient of the objective function (see @cite{Abramowitz and Stegun (1964)}). Values @code{13} and @code{15} are more experimental. If perturbations on the right and the left increase the value of the objective function (we minimize this function) then we force the corresponding element of the gradient to be zero. The idea is to temporarily reduce the size of the optimization problem. Default: @code{2}.
 
 @item 'NumgradEpsilon'
 Size of the perturbation used to compute numerically the gradient of the objective function. Default: @code{1e-6}
@@ -4764,7 +4919,6 @@ To change the defaults of csminwel (@code{mode_compute=4}):
 Metropolis-Hastings. Default: diagnostics are computed and displayed
 
 @item bayesian_irf
-@vindex oo_.PosteriorIRF.dsge
 @anchor{bayesian_irf} Triggers the computation of the posterior
 distribution of IRFs. The length of the IRFs are controlled by the
 @code{irf} option. Results are stored in @code{oo_.PosteriorIRF.dsge}
@@ -4773,7 +4927,7 @@ distribution of IRFs. The length of the IRFs are controlled by the
 @item dsge_var = @var{DOUBLE}
 @anchor{dsge_var} Triggers the estimation of a DSGE-VAR model, where the
 weight of  the DSGE prior  of the VAR model  is calibrated to  the value
-passed (see @cite{Del  Negro and Schorfheide (2004)}). NB:  The previous method
+passed (see @cite{Del  Negro and Schorfheide (2004)}). It represents ratio of dummy over actual observations. To assure that the prior is proper, the value must be bigger than @math{(k+n)/T}, where @math{k} is the number of estimated parameters, @math{n} is the number of observables, and @math{T} is the number of observations. NB:  The previous method
 of   declaring  @code{dsge_prior_weight}   as  a   parameter  and   then
 calibrating it is now deprecated and will be removed in a future release
 of Dynare.
@@ -4792,12 +4946,10 @@ deprecated and will be removed in a future release of Dynare.
 model. Default: @code{4}.
 
 @item moments_varendo
-@vindex oo_.PosteriorTheoreticalMoments
 @anchor{moments_varendo} Triggers the computation of the posterior
 distribution of the theoretical moments of the endogenous
 variables. Results are stored in
-@code{oo_.PosteriorTheoreticalMoments} (see below for a description of
-this variable). The number of lags in the autocorrelation function is
+@code{oo_.PosteriorTheoreticalMoments} (@pxref{oo_.PosteriorTheoreticalMoments}). The number of lags in the autocorrelation function is
 controlled by the @code{ar} option.
 
 @item conditional_variance_decomposition = @var{INTEGER}
@@ -4814,30 +4966,24 @@ period 1, the conditional variance decomposition provides the
 decomposition of the effects of shocks upon impact. The results are
 stored in
 @code{oo_.PosteriorTheoreticalMoments.dsge.ConditionalVarianceDecomposition},
-but currently there is no output. Note that this option requires the
+but currently there is no displayed output. Note that this option requires the
 option @code{moments_varendo} to be specified.
 
 @item filtered_vars
-@vindex oo_.FilteredVariables
 @anchor{filtered_vars} Triggers the computation of the posterior
 distribution of filtered endogenous variables/one-step ahead forecasts, i.e. @math{E_{t}{y_{t+1}}}. Results are
 stored in @code{oo_.FilteredVariables} (see below for a description of
 this variable)
 
 @item smoother
-@vindex oo_.SmoothedVariables
-@vindex oo_.SmoothedShocks
-@vindex oo_.SmoothedMeasurementErrors
-@vindex oo_.UpdatedVariables
 @anchor{smoother} Triggers the computation of the posterior distribution
-of smoothered endogenous variables and shocks, i.e. the expected value of variables and shocks given the information available in all observations up to the @emph{final} date (@math{E_{T}{y_t}}). Results are stored in
+of smoothed endogenous variables and shocks, i.e. the expected value of variables and shocks given the information available in all observations up to the @emph{final} date (@math{E_{T}{y_t}}). Results are stored in
 @code{oo_.SmoothedVariables}, @code{oo_.SmoothedShocks} and
 @code{oo_.SmoothedMeasurementErrors}.  Also triggers the computation of
 @code{oo_.UpdatedVariables}, which contains the estimation of the expected value of variables given the information available at the @emph{current} date (@math{E_{t}{y_t}}).  See below for a description of all these
 variables.
 
 @item forecast = @var{INTEGER}
-@vindex oo_.forecast
 @anchor{forecast} Computes the posterior distribution of a forecast on
 @var{INTEGER} periods after the end of the sample used in
 estimation. If no Metropolis-Hastings is computed, the result is
@@ -4875,7 +5021,7 @@ Use the Univariate Diffuse Kalman Filter
 
 @end table
 @noindent
-Default value is @code{0}.
+Default value is @code{0}. In case of missing observations of single or all series, Dynare treats those missing values as unobserved states and uses the Kalman filter to infer their value (see e.g. @cite{Durbin and Koopman (2012), Ch. 4.10})
 
 
 @item kalman_tol = @var{DOUBLE}
@@ -4887,9 +5033,10 @@ Default value is @code{0}.
 forecast covariance matrices.
 
 @item filter_step_ahead = [@var{INTEGER1}:@var{INTEGER2}]
+See below.
+
+@item filter_step_ahead = [@var{INTEGER1} @var{INTEGER2}  @dots{}]
 @anchor{filter_step_ahead}
-@vindex oo_.FilteredVariablesKStepAhead
-@vindex oo_.FilteredVariablesKStepAheadVariances
 Triggers the computation k-step ahead filtered values. Stores results in
 @code{oo_.FilteredVariablesKStepAhead} and
 @code{oo_.FilteredVariablesKStepAheadVariances}.
@@ -4898,15 +5045,9 @@ Triggers the computation k-step ahead filtered values. Stores results in
 @anchor{filter_decomposition} Triggers the computation of the shock
 decomposition of the above k-step ahead filtered values.
 
-@item constant
-@dots{}
-
-@item noconstant
-@dots{}
-
 @item diffuse_filter
 Uses the diffuse Kalman filter (as described in
-@cite{Durbin and Koopman (2001)} and @cite{Koopman and Durbin
+@cite{Durbin and Koopman (2012)} and @cite{Koopman and Durbin
 (2003)}) to estimate models with non-stationary observed variables.
 
 When @code{diffuse_filter} is used the @code{lik_init} option of
@@ -4921,8 +5062,9 @@ any value of  @math{\bar a} of @math{a} is a  deterministic steady state for pro
 Note that the nonstationary variables in the model must be integrated processes (their first difference or k-difference must be stationary).
 
 @item selected_variables_only
+@anchor{selected_variables_only}
 Only run the smoother on the variables listed just after the
-@code{estimation} command. Default: run the smoother on all the
+@code{estimation} command. This options is incompatible with requesting classical frequentist forecasts and will be overridden in this case. Default: run the smoother on all the
 declared endogenous variables.
 
 @item cova_compute = @var{INTEGER}
@@ -4943,7 +5085,7 @@ Order of approximation, either @code{1} or @code{2}. When equal to
 @code{2}, the likelihood is evaluated with a particle filter based on
 a second order approximation of the model (see
 @cite{Fernandez-Villaverde and Rubio-Ramirez (2005)}).  Default is
-@code{1}, ie the lilkelihood of the linearized model is evaluated
+@code{1}, ie the likelihood of the linearized model is evaluated
 using a standard Kalman filter.
 
 @item irf = @var{INTEGER}
@@ -4967,7 +5109,7 @@ with @ref{dsge_var}.
 
 @item lyapunov = @var{OPTION}
 @anchor{lyapunov}
-Determines the algorithm used to solve the Laypunov equation to initialized the variance-covariance matrix of the Kalman filter using the steady-state value of state variables. Possible values for @code{@var{OPTION}} are:
+Determines the algorithm used to solve the Lyapunov equation to initialized the variance-covariance matrix of the Kalman filter using the steady-state value of state variables. Possible values for @code{@var{OPTION}} are:
 
 @table @code
 
@@ -4996,11 +5138,11 @@ Default value is @code{default}
 
 @item lyapunov_fixed_point_tol = @var{DOUBLE}
 @anchor{lyapunov_fixed_point_tol}
-This is the convergence criterion used in the fixed point lyapunov solver. Its default value is 1e-10.
+This is the convergence criterion used in the fixed point Lyapunov solver. Its default value is 1e-10.
 
 @item lyapunov_doubling_tol = @var{DOUBLE}
 @anchor{lyapunov_doubling_tol}
-This is the convergence criterion used in the doubling algorithm to solve the lyapunov equation. Its default value is 1e-16.
+This is the convergence criterion used in the doubling algorithm to solve the Lyapunov equation. Its default value is 1e-16.
 
 @item analytic_derivation
 Triggers estimation with analytic gradient. The final hessian is also
@@ -5008,7 +5150,7 @@ computed analytically. Only works for stationary models without
 missing observations.
 
 @item ar = @var{INTEGER}
-@xref{ar}. Only useful in conjuction with option @code{moments_varendo}.
+@xref{ar}. Only useful in conjunction with option @code{moments_varendo}.
 
 @item endogenous_prior
 Use endogenous priors as in @cite{Christiano, Trabandt and Walentin
@@ -5023,9 +5165,7 @@ the filter, but rather use a penalty value for the likelihood when
 such a singularity is encountered. Default: @code{1}.
 
 @item qz_zero_threshold = @var{DOUBLE}
-Value used to test if a generalized eigenvalue is 0/0 in the generalized
-Schur decomposition  (in which case  the model  does not admit  a unique
-solution). Default: @code{1e-6}.
+@xref{qz_zero_threshold}.
 
 @item taper_steps = [@var{INTEGER1} @var{INTEGER2} @dots{}]
 @anchor{taper_steps}
@@ -5141,15 +5281,22 @@ Variable set by the @code{estimation} command, if it is used with
 
 @defvr {MATLAB/Octave variable} oo_.FilteredVariables
 Variable set by the @code{estimation} command, if it is used with the
-@code{filtered_vars} option. Fields are of the form:
+@code{filtered_vars} option.
+
+After an estimation without Metropolis, fields are of the form:
 @example
 @code{oo_.FilteredVariables.@var{VARIABLE_NAME}}
 @end example
+
+After an estimation with Metropolis, fields are of the form:
+@example
+@code{oo_.FilteredVariables.@var{MOMENT_NAME}.@var{VARIABLE_NAME}}
+@end example
 @end defvr
 
 @defvr {MATLAB/Octave variable} oo_.FilteredVariablesKStepAhead
 Variable set by the @code{estimation} command, if it is used with the
-@code{filter_step_ahead} option.
+@code{filter_step_ahead} option. The k-steps are stored along the rows while the columns indicate the respective variables. The third dimension of the array provides the observation for which the forecast has been made. For example, if @code{filter_step_ahead=[1 2 4]} and @code{nobs=200}, the element (3,5,204) stores the four period ahead filtered value of variable 5 computed at time t=200 for time t=204. The periods at the beginning and end of the sample for which no forecasts can be made, e.g. entries (1,5,1) and (1,5,204) in the example, are set to zero.
 @end defvr
 
 @defvr {MATLAB/Octave variable} oo_.FilteredVariablesKStepAheadVariances
@@ -5157,6 +5304,16 @@ Variable set by the @code{estimation} command, if it is used with the
 @code{filter_step_ahead} option.
 @end defvr
 
+@defvr {MATLAB/Octave variable} oo_.Filtered_Variables_X_step_ahead
+Variable set by the @code{estimation} command, if it is used with the @code{filter_step_ahead} option in the context of Bayesian estimation. Fields are of the form:
+@example
+@code{oo_.Filtered_Variables_X_step_ahead.@var{VARIABLE_NAME}}
+@end example
+The nth entry stores the k-step ahead filtered variable computed at time n for time n+k.
+@end defvr
+
+
+
 @defvr {MATLAB/Octave variable} oo_.PosteriorIRF.dsge
 Variable set by the @code{estimation} command, if it is used with the
 @code{bayesian_irf} option. Fields are of the form:
@@ -5174,32 +5331,58 @@ Variable set by the @code{estimation} command, if it is used with the
 @end defvr
 
 @defvr {MATLAB/Octave variable} oo_.SmoothedShocks
-Variable set by the @code{estimation} command, if it is used with the
-@code{smoother} option. Fields are of the form:
+Variable set by the @code{estimation} command (if used with the
+@code{smoother} option), or by the @code{calib_smoother} command.
+
+After an estimation without Metropolis, or if computed by
+@code{calib_smoother}, fields are of the form:
 @example
 @code{oo_.SmoothedShocks.@var{VARIABLE_NAME}}
 @end example
+
+After an estimation with Metropolis, fields are of the form:
+@example
+@code{oo_.SmoothedShocks.@var{MOMENT_NAME}.@var{VARIABLE_NAME}}
+@end example
+
 @end defvr
 
 @defvr {MATLAB/Octave variable} oo_.SmoothedVariables
-Variable set by the @code{estimation} command, if it is used with the
-@code{smoother} option. Fields are of the form:
+Variable set by the @code{estimation} command (if used with the
+@code{smoother} option), or by the @code{calib_smoother} command.
+
+After an estimation without Metropolis, or if computed by
+@code{calib_smoother}, fields are of the form:
 @example
 @code{oo_.SmoothedVariables.@var{VARIABLE_NAME}}
 @end example
+
+After an estimation with Metropolis, fields are of the form:
+@example
+@code{oo_.SmoothedVariables.@var{MOMENT_NAME}.@var{VARIABLE_NAME}}
+@end example
 @end defvr
 
 @defvr {MATLAB/Octave variable} oo_.UpdatedVariables
-Variable set by the @code{estimation} command, if it is used with the
-@code{smoother} option. Contains the estimation of the expected value of
-variables given the information available at the @emph{current}
-date. Fields are of the form:
+Variable set by the @code{estimation} command (if used with the
+@code{smoother} option), or by the @code{calib_smoother} command.
+Contains the estimation of the expected value of variables given the
+information available at the @emph{current} date.
+
+After an estimation without Metropolis, or if computed by
+@code{calib_smoother}, fields are of the form:
 @example
 @code{oo_.UpdatedVariables.@var{VARIABLE_NAME}}
 @end example
+
+After an estimation with Metropolis, fields are of the form:
+@example
+@code{oo_.UpdatedVariables.@var{MOMENT_NAME}.@var{VARIABLE_NAME}}
+@end example
 @end defvr
 
 @defvr {MATLAB/Octave variable} oo_.PosteriorTheoreticalMoments
+@anchor{oo_.PosteriorTheoreticalMoments}
 Variable set by the @code{estimation} command, if it is used with the
 @code{moments_varendo} option. Fields are of the form:
 @example
@@ -5217,9 +5400,9 @@ Auto- and cross-correlation of endogenous variables. Fields are vectors with cor
 
 
 @item VarianceDecomposition
-Decomposition of variance@footnote{When the shocks are correlated, it
+Decomposition of variance (unconditional variance, i.e. at horizon infinity)@footnote{When the shocks are correlated, it
 is the decomposition of orthogonalized shocks via Cholesky
-decompostion according to the order of declaration of shocks
+decomposition according to the order of declaration of shocks
 (@pxref{Variable declarations})}
 
 @item ConditionalVarianceDecomposition
@@ -5369,8 +5552,12 @@ estimates using a higher tapering are usually more reliable.
 @descriptionhead
 
 This command computes odds ratios and estimate a posterior density
-over a collection of models. The priors over models can be specified
-as the @var{DOUBLE} values, otherwise a uniform prior is assumed.
+over a collection of models 
+(see e.g. @cite{Koop (2003), Ch. 1}). The priors over models can be specified
+as the @var{DOUBLE} values, otherwise a uniform prior over all models is assumed. 
+In contrast to frequentist econometrics, the models to be compared do not need to be nested. 
+However, as the computation of posterior odds ratios is a Bayesian technique, 
+the comparison of models estimated with maximum likelihood is not supported.
 
 @examplehead
 
@@ -5412,6 +5599,15 @@ calibrated model.
 
 @end table
 
+@vindex oo_.shock_decomposition
+The results are stored in the field @code{oo_.shock_decomposition}, which is a three
+dimensional array. The first dimension contains the endogenous variables for
+which the shock decomposition has been requested. The second dimension stores
+in the first @code{M_.exo_nbr} columns the contribution of the respective shocks.
+Column @code{M_.exo_nbr+1} stores the contribution of the initial conditions,
+while column @code{M_.exo_nbr+2} stores the smoothed value of the respective
+endogenous variable. The third dimension stores the time periods.
+
 @end deffn
 
 
@@ -5444,9 +5640,6 @@ A datafile must be provided, and the observable variables declared with
 @code{varobs}. The smoother is based on a first-order approximation of
 the model.
 
-@vindex oo_.SmoothedVariables
-@vindex oo_.SmoothedShocks
-@vindex oo_.UpdatedVariables 
 By default, the command computes the smoothed variables and shocks and stores the
 results in @code{oo_.SmoothedVariables} and
 @code{oo_.SmoothedShocks}. It also fills @code{oo_.UpdatedVariables}.
@@ -5484,13 +5677,6 @@ DSGE model, by finding the structural shocks that are needed to match
 the restricted paths. Use @code{conditional_forecast},
 @code{conditional_forecast_paths} and @code{plot_conditional_forecast}
 for that purpose.
-If the model contains strong non-linearities, the conditional forecasts 
-can be computed using an extended path method with the @code{simulation_type} 
-option in @code{conditional_forecast} command set to @code{deterministic}. 
-Because in this case deterministic simulations are carried out, 
-the nature of the shocks (surprise or perfect foresight) has to be indicated 
-in the @code{conditional_forecast_paths} block, using the command @code{expectation} 
-for each endogenous path. The forecasts are plotted using the rplot command.
 
 Finally, it is possible to do forecasting with a Bayesian VAR using
 the @code{bvar_forecast} command.
@@ -5504,7 +5690,7 @@ This command computes a simulation of a stochastic model from an
 arbitrary initial point.
 
 When the model also contains deterministic exogenous shocks, the
-simulation is computed conditionaly to the agents knowing the future
+simulation is computed conditionally to the agents knowing the future
 values of the deterministic exogenous variables.
 
 @code{forecast} must be called after @code{stoch_simul}.
@@ -5520,7 +5706,7 @@ confidence interval.
 @table @code
 
 @item periods = @var{INTEGER}
-Number of periods of the forecast. Default: @code{40}
+Number of periods of the forecast. Default: @code{5}.
 
 @item conf_sig = @var{DOUBLE}
 @anchor{conf_sig} Level of significance for confidence
@@ -5643,18 +5829,55 @@ Fields are of the form:
 
 @descriptionhead
 
-This command computes forecasts on an estimated model for a given
-constrained path of some future endogenous variables. This is done,
-from the reduced form representation of the DSGE model, by finding the
-structural shocks that are needed to match the restricted paths. This
-command has to be called after estimation.
+This command computes forecasts on an estimated or calibrated model for a
+given constrained path of some future endogenous variables. This is done
+using the reduced form first order state-space representation of the DSGE
+model by finding the structural shocks that are needed to match the
+restricted paths. Consider the an augmented state space representation
+that stacks both predetermined and non-predetermined variables into a
+vector @math{y_{t}}:
+
+@math{y_t=Ty_{t-1}+R\varepsilon_t}
+
+Both
+@math{y_t} and @math{\varepsilon_t} are split up into controlled and
+uncontrolled ones to get:
+
+@math{y_t(contr\_vars)=Ty_{t-1}(contr\_vars)+R(contr\_vars,uncontr\_shocks)\varepsilon_t(uncontr\_shocks)
++R(contr\_vars,contr\_shocks)\varepsilon_t(contr\_shocks)}
+
+which can be solved algebraically for @math{\varepsilon_t(contr\_shocks)}.
+
+Using these controlled shocks, the state-space representation can be used
+for forecasting. A few things need to be noted. First, it is assumed that
+controlled exogenous variables are fully under control of the policy
+maker for all forecast periods and not just for the periods where the
+endogenous variables are controlled. For all uncontrolled periods, the
+controlled exogenous variables are assumed to be 0. This implies that
+there is no forecast uncertainty arising from these exogenous variables
+in uncontrolled periods. Second, by making use of the first order state
+space solution, even if a higher-order approximation was performed, the
+conditional forecasts will be based on a first order approximation.
+Third, although controlled exogenous variables are taken as instruments
+perfectly under the control of the policy-maker, they are nevertheless
+random and unforeseen shocks from the perspective of the households. That is,
+households are in each period surprised by the realization of a shock
+that keeps the controlled endogenous variables at their respective level.
+Fourth, keep in mind that if the structural innovations are correlated,
+because the calibrated or estimated covariance matrix has non zero off
+diagonal elements, the results of the conditional forecasts will depend on
+the ordering of the innovations (as declared after @code{varexo}). As in VAR
+models, a Cholesky decomposition is used to factorize the covariance matrix
+and identify orthogonal impulses. It is preferable to declare the correlations
+in the @code{model} block (explicitly imposing the identification restrictions),
+unless you are satisfied with the implicit identification restrictions implied
+by the Cholesky decomposition.
+
+This command has to be called after @code{estimation} or @code{stoch_simul}.
 
 Use @code{conditional_forecast_paths} block to give the list of
 constrained endogenous, and their constrained future path.
-If an extended path method is applied on the original dsge model, 
-the nature of the expectation on the constrained endogenous has to be 
-specified using expectation command. Option
-@code{controlled_varexo} is used to specify the structural shocks
+Option @code{controlled_varexo} is used to specify the structural shocks
 which will be matched to generate the constrained path.
 
 Use @code{plot_conditional_forecast} to graph the results.
@@ -5681,14 +5904,6 @@ Number of simulations. Default: @code{5000}.
 @item conf_sig = @var{DOUBLE}
 Level of significance for confidence interval. Default: @code{0.80}
 
-@item  simulation_type = @code{stochastic} | @code{deterministic}
-Indicates the nature of simulations used to compute the conditional forecast.  
-The default value @code{stochastic} is used, when simulations are computed 
-using the reduced form representation of the DSGE model. 
-If the model has to be simulated using extended path method on the original 
-DSGE model, @code{simulation_type} has to be set equal to @code{deterministic}.
-
-
 @end table
 
 @outputhead
@@ -5758,31 +5973,6 @@ plot_conditional_forecast(periods = 10) a y;
 @end example
 
 
-@examplehead
-@example
-/* conditional forecast using extended path method
-with perfect foresight on r path*/
-var y r
-varexo e u;
-
-@dots{}
-
-conditional_forecast_paths;
-var y;
-periods 1:3, 4:5;
-values 2, 5;
-var r
-periods 1:5;
-values 3;
-expectation perfect_foresight;
-end;
-
-conditional_forecast(parameter_set = calibration, controlled_varexo = (e, u), simulation_type=deterministic);
-
-rplot a;
-rplot y;
-@end example
-
 @end deffn
 
 @deffn Block conditional_forecast_paths ;
@@ -5795,10 +5985,6 @@ example.
 The syntax of the block is the same than the deterministic shocks in
 the @code{shocks} blocks (@pxref{Shocks on exogenous variables}).
 
-If the conditional forecast is carried out using the extended path method 
-on the original DSGE model, the nature of the expectation have to be specified
-for each endogenous path, using the @code{expectation} = @code{surprise} | @code{perfect_foresight}.
-By default, @code{expectation} is equal to @code{surprise}.
 
 @end deffn
 
@@ -5832,6 +6018,67 @@ See @file{bvar-a-la-sims.pdf}, which comes with Dynare distribution,
 for more information on this command.
 @end deffn
 
+If the model contains strong non-linearities or if some perfectly expected shocks are considered, the forecasts and the conditional forecasts
+can be computed using an extended path method. The forecast scenario describing the shocks and/or the constrained paths on some endogenous variables should be build.
+The first step is the forecast scenario initialization using the function @code{init_plan}:
+
+@anchor{init_plan}
+@deftypefn {MATLAB/Octave command} {HANDLE =} init_plan (DATES) ;
+
+Creates a new forecast scenario for a forecast period (indicated as a dates class, see @ref{dates class members}). This function return a handle on the new forecast scenario.
+
+@end deftypefn
+
+The forecast scenario can contain some simple shocks on the exogenous variables. This shocks are described using the function @code{basic_plan}:
+
+@anchor{basic_plan}
+@deftypefn {MATLAB/Octave command} {HANDLE =} basic_plan (HANDLE, 'VARIABLE_NAME', 'SHOCK_TYPE', DATES, MATLAB VECTOR OF DOUBLE |  [DOUBLE | EXPRESSION [DOUBLE | | EXPRESSION] ] ) ;
+
+Adds to the forecast scenario a shock on the exogenous variable indicated between quotes in the second argument. The shock type has to be specified in the third argument between quotes: 'surprise' in case of an unexpected shock  or 'perfect_foresight' for a perfectly anticipated shock. The fourth argument indicates the period of the shock using a dates class (see @ref{dates class members}). The last argument is the shock path indicated as a Matlab vector of double. This function return the handle of the updated forecast scenario.
+
+@end deftypefn
+
+The forecast scenario can also contain a constrained path on an endogenous variable. The values of the related exogenous variable compatible with the constrained path are in this case computed. In other words, a conditional forecast is performed. This kind of shock is described with the function  @code{flip_plan}:
+
+@anchor{flip_plan}
+@deftypefn {MATLAB/Octave command} {HANDLE =} flip_plan (HANDLE, 'VARIABLE_NAME, 'VARIABLE_NAME', 'SHOCK_TYPE', DATES, MATLAB VECTOR OF DOUBLE |  [DOUBLE | EXPRESSION [DOUBLE | | EXPRESSION] ] ) ;
+
+Adds to the forecast scenario a constrained path on the endogenous variable specified between quotes in the second argument. The associated exogenous variable provided in the third argument between quotes, is considered as an endogenous variable and its values compatible with the constrained path on the endogenous variable will be computed. The nature of the expectation on the constrained  path has to be specified in the fourth argument between quotes: 'surprise' in case of an unexpected path or 'perfect_foresight' for a perfectly anticipated path. The fifth argument indicates the period where the path of the endogenous variable is constrained using a dates class (see @ref{dates class members}). The last argument contains the constrained path as a Matlab vector of double. This function return the handle of the updated forecast scenario.
+
+@end deftypefn
+
+Once the forecast scenario if fully described, the forecast is computed with the command @code{det_cond_forecast}:
+@anchor{det_cond_forecast}
+@deftypefn {MATLAB/Octave command} {DSERIES =} det_cond_forecast (HANDLE[, DSERIES [, DATES]]) ;
+
+Computes the forecast or the conditional forecast using an extended path method for the given forecast scenario (first argument). The past values of the endogenous and exogenous variables provided with a dseries class (see @ref{dseries class members}) can be indicated in the second argument. By default, the past values of the variables are equal to their steady-state values. The initial date of the forecast can be provided in the third argument. By default, the forecast will start at the first date indicated in the @code{init_plan} command. This function returns a dset containing the historical and forecast values for the endogenous and exogenous variables.
+
+@end deftypefn
+
+
+
+@examplehead
+@example
+/* conditional forecast using extended path method
+with perfect foresight on r path*/
+var y r
+varexo e u;
+
+@dots{}
+
+smoothed = dseries('smoothed_variables.csv');
+
+fplan = init_plan(2013Q4:2029Q4);
+
+fplan = flip_plan(fplan, 'y', 'u', 'surprise', 2013Q4:2014Q4,  [1 1.1 1.2 1.1 ]);
+
+fplan = flip_plan(fplan, 'r', 'e', 'perfect_foresight', 2013Q4:2014Q4,  [2 1.9 1.9 1.9 ]);
+
+dset_forecast = det_cond_forecast(fplan, smoothed);
+
+plot(dset_forecast.@{'y','u'@});
+plot(dset_forecast.@{'r','e'@});
+@end example
 
 @node Optimal policy
 @section Optimal policy
@@ -5854,7 +6101,7 @@ This command computes optimal simple policy rules for
 linear-quadratic problems of the form:
 
 @quotation
-@math{\max_\gamma E(y'_tWy_t)}
+@math{\min_\gamma E(y'_tWy_t)}
 @end quotation
 
 such that:
@@ -5865,42 +6112,82 @@ such that:
 where:
 
 @itemize 
+@item
+@math{E} denotes the unconditional expectations operator;
 
 @item
-@math{\gamma} are parameters to be optimized. They must be elements of matrices
-@math{A_1}, @math{A_2}, @math{A_3};
+@math{\gamma} are parameters to be optimized. They must be elements
+of the matrices @math{A_1}, @math{A_2}, @math{A_3}, i.e. be specified as
+parameters in the @code{params}-command and be entered in the
+@code{model}-block;
 
 @item
-@math{y} are the endogenous variables;
+@math{y} are the endogenous variables, specified in the
+@code{var}-command, whose (co)-variance enters the loss function;
 
 @item
-@math{e} are the exogenous stochastic shocks;
-@end itemize
+@math{e} are the exogenous stochastic shocks, specified in the
+@code{varexo}-command;
+
+@item
+@math{W} is the weighting matrix;
 
-The parameters to be optimized must be listed with @code{osr_params}.
+@end itemize
 
-The quadratic objectives must be listed with @code{optim_weights}.
+The linear quadratic problem consists of choosing a subset of model
+parameters to minimize the weighted (co)-variance of a specified subset
+of endogenous variables, subject to a linear law of motion implied by the
+first order conditions of the model. A few things are worth mentioning.
+First, @math{y} denotes the selected endogenous variables' deviations
+from their steady state, i.e. in case they are not already mean 0 the
+variables entering the loss function are automatically demeaned so that
+the centered second moments are minimized. Second, @code{osr} only solves
+linear quadratic problems of the type resulting from combining the
+specified quadratic loss function with a first order approximation to the
+model's equilibrium conditions. The reason is that the first order
+state-space representation is used to compute the unconditional
+(co)-variances. Hence, @code{osr} will automatically select
+@code{order=1}. Third, because the objective involves minimizing a
+weighted sum of unconditional second moments, those second moments must
+be finite. In particular, unit roots in @math{y} are not allowed.
+
+The subset of the model parameters over which the optimal simple rule is
+to be optimized, @math{\gamma}, must be listed with @code{osr_params}.
+
+The weighting matrix @math{W} used for the quadratic objective function
+is specified in the @code{optim_weights}-block. By attaching weights to
+endogenous variables, the subset of endogenous variables entering the
+objective function, @math{y}, is implicitly specified.
+
+The linear quadratic problem is solved using the numerical optimizer
+@code{csminwel} of Chris Sims.
 
-This problem is solved using the numerical optimizer @code{csminwel} of Chris Sims.
 
 @optionshead
 
-This command accepts the same options as @code{stoch_simul}
+The @code{osr} command will subsequently run @code{stoch_simul} and
+accepts the same options, including restricting the endogenous variables
+by listing them after the command, as @code{stoch_simul}
 (@pxref{Computing the stochastic solution}) plus
 
 @table @code
 
-@item maxit = @var{INTEGER}
-Determines the maximum number of iterations used in the non-linear solver. Default: @code{1000}
+@item maxit = @var{INTEGER} Determines the maximum number of iterations
+used in the non-linear solver. Default: @code{1000}
+
+@item tolf = @var{DOUBLE} Convergence criterion for termination based on
+the function value. Iteration will cease when it proves impossible to
+improve the function value by more than tolf. Default: @code{1e-7}
 
-@item tolf = @var{DOUBLE}
-Convergence criterion for termination based on the function value. Iteration will 
-cease when it proves impossible to improve the function value by more than tolf. Default: @code{1e-7}
 @end table
 
 The value of the objective is stored in the variable
 @code{oo_.osr.objective_function}, which is described below.
 
+After running @code{osr} the parameters entering the simple rule will be
+set to their optimal value so that subsequent runs of @code{stoch_simul}
+will be conducted at these values.
+
 @end deffn
 
 @anchor{osr_params}
@@ -5913,11 +6200,12 @@ This command declares parameters to be optimized by @code{osr}.
 
 This block specifies quadratic objectives for optimal policy problems
 
-More precisely, this block specifies the nonzero elements of the
-quadratic weight matrices for the objectives in @code{osr}.
+More precisely, this block specifies the nonzero elements of the weight
+matrix @math{W} used in the quadratic form of the objective function in
+@code{osr}.
 
-An element of the diagonal of the weight matrix is given by a line of
-the form:
+An element of the diagonal of the weight matrix is given by a line of the
+form:
 @example
 @var{VARIABLE_NAME} @var{EXPRESSION};
 @end example
@@ -5930,11 +6218,110 @@ the form:
 
 @end deffn
 
+@examplehead
+
+@example
+var y inflation r; 
+varexo y_ inf_;
+
+parameters delta sigma alpha kappa gammarr gammax0 gammac0 gamma_y_ gamma_inf_;
+
+delta =  0.44;
+kappa =  0.18;
+alpha =  0.48;
+sigma = -0.06;
+
+gammarr = 0;
+gammax0 = 0.2;
+gammac0 = 1.5;
+gamma_y_ = 8;
+gamma_inf_ = 3;
+
+model(linear); 
+y  = delta * y(-1)  + (1-delta)*y(+1)+sigma *(r - inflation(+1)) + y_;
+inflation  =   alpha * inflation(-1) + (1-alpha) * inflation(+1) + kappa*y + inf_;
+r = gammax0*y(-1)+gammac0*inflation(-1)+gamma_y_*y_+gamma_inf_*inf_; 
+end;
+
+shocks; 
+var y_; stderr 0.63; 
+var inf_; stderr 0.4; 
+end;
+
+optim_weights; 
+inflation 1; 
+y 1; 
+y, inflation 0.5; 
+end;
+
+osr_params gammax0 gammac0 gamma_y_ gamma_inf_; 
+osr y; 
+@end example
+
 @defvr {MATLAB/Octave variable} oo_.osr.objective_function 
-After an execution of the @code{osr} command, this variable contains
-the value of the objective under optimal policy.
+After an execution of the @code{osr} command, this variable contains the value of
+the objective under optimal policy.
 @end defvr
 
+@anchor{Ramsey}
+
+@deffn Command ramsey_model (@var{OPTIONS}@dots{});
+
+@descriptionhead
+
+This command computes the First Order Conditions for maximizing the policy maker objective function subject to the
+constraints provided by the equilibrium path of the economy.
+
+The planner objective must be declared with the @code{planner_objective} command.
+
+This command only creates the expanded model, it doesn't perform any
+computations. It needs to be followed by other instructions to actually
+perfrom desired computations. Note that it is the only way to perform
+perfect foresight simulation of the Ramsey policy problem.
+
+@xref{Auxiliary
+variables}, for an explanation of how Lagrange multipliers are
+automatically created.
+
+@optionshead
+
+This command accepts the following options:
+
+@table @code
+
+@item planner_discount = @var{EXPRESSION}
+Declares the discount factor of the central planner. Default: @code{1.0}
+
+@item instruments = (@var{VARIABLE_NAME},@dots{})
+Declares instrument variables for the computation of the steady state
+under optimal policy. Requires a @code{steady_state_model} block or a
+@code{@dots{}_steadystate.m} file. See below.
+
+@end table
+
+@customhead{Steady state}
+
+Dynare takes advantage of the fact that the Lagrange multipliers appear
+linearly in the equations of the steady state of the model under optimal
+policy. Nevertheless, it is in general very difficult to compute the
+steady state with simply a numerical guess in @code{initval} for the
+endogenous variables.
+
+It greatly facilitates the computation, if the user provides an
+analytical solution for the steady state (in @code{steady_state_model}
+block or in a @code{@dots{}_steadystate.m} file). In this case, it is
+necessary to provide a steady state solution CONDITIONAL on the value
+of the instruments in the optimal policy problem and declared with
+option @code{instruments}. Note that choosing the instruments is
+partly a matter of interpretation and you can choose instruments that
+are handy from a mathematical point of view but different from the
+instruments you would refer to in the analysis of the paper. A typical
+example is choosing inflation or nominal interest rate as an
+instrument.
+
+
+@end deffn
+
 @deffn Command ramsey_policy [@var{VARIABLE_NAME}@dots{}];
 @deffnx Command ramsey_policy (@var{OPTIONS}@dots{}) [@var{VARIABLE_NAME}@dots{}];
 
@@ -6040,7 +6427,10 @@ This command declares the policy maker objective, for use with
 
 You need to give the one-period objective, not the discounted lifetime
 objective. The discount factor is given by the @code{planner_discount}
-option of @code{ramsey_policy} and @code{discretionary_policy}.
+option of @code{ramsey_policy} and @code{discretionary_policy}. The
+objective function can only contain current endogenous variables and no
+exogenous ones. This limitation is easily circumvented by defining an
+appropriate auxiliary variable in the model.
 
 With @code{ramsey_policy}, you are not limited to quadratic
 objectives: you can give any arbitrary nonlinear expression.
@@ -6765,7 +7155,7 @@ the given chain. One, but not both, of @code{coefficients} or
 @code{variances} must appear. Default: @code{none}
 
 @item equations
-Defines the equation controlled by the given chain. If not specificed,
+Defines the equation controlled by the given chain. If not specified,
 then all equations are controlled by @code{chain}. Default: @code{none}
 
 @item chain = @var{INTEGER}
@@ -6838,6 +7228,25 @@ To be documented. For now, see the wiki: @uref{http://www.dynare.org/DynareWiki/
 @end deffn
 
 
+@deffn Block svar_identification ;
+
+@descriptionhead
+
+This block is terminated by @code{end;}, and contains lines of the
+form:
+@example
+UPPER_CHOLESKY;
+LOWER_CHOLESKY;
+EXCLUSION CONSTANTS;
+EXCLUSION LAG @var{INTEGER}; @var{VARIABLE_NAME} [,@var{VARIABLE_NAME}@dots{}]
+EXCLUSION LAG @var{INTEGER}; EQUATION @var{INTEGER}, @var{VARIABLE_NAME} [,@var{VARIABLE_NAME}@dots{}]
+RESTRICTION EQUATION @var{INTEGER}, @var{EXPRESSION} = @var{EXPRESSION};
+@end example
+
+To be documented. For now, see the wiki: @uref{http://www.dynare.org/DynareWiki/MarkovSwitchingInterface}
+
+@end deffn
+
 @anchor{ms_estimation}
 @deffn Command ms_estimation (@var{OPTIONS}@dots{});
 @descriptionhead
@@ -6924,11 +7333,14 @@ The first period of data (@i{i.e.} for quarterly data, an integer in
 [@code{1,4}]). Default: @code{1}
 
 @item final_year = @var{INTEGER}
-The last year of data. Default: Set to encompass entire dataset
+The last year of data. Default: Set to encompass entire dataset.
 
 @item final_subperiod = @var{INTEGER}
 The final period of data (@i{i.e.} for monthly data, an integer in
-[@code{1,12}]. Default: Set to encompass entire dataset
+[@code{1,12}]. Default: When final_year is also missing, set to
+encompass entire dataset; when final_year is indicated, set to the
+maximum number of subperiods given the frequency (@i{i.e}. 4 for
+quarterly data, 12 for monthly,...).
 
 @item datafile = @var{FILENAME}
 @xref{datafile}.
@@ -6949,7 +7361,7 @@ Use cross @math{A^0} and @math{A^+} restrictions. Default: @code{off}
 Use contemporaneous recursive reduced form. Default: @code{off}
 
 @item no_bayesian_prior
-Do not use bayesian prior. Default: @code{off} (@i{i.e.} use bayesian
+Do not use Bayesian prior. Default: @code{off} (@i{i.e.} use Bayesian
 prior)
 
 @item alpha = @var{INTEGER}
@@ -6975,7 +7387,7 @@ parameters. Default: @code{Random Walk}
 @item convergence_starting_value = @var{DOUBLE}
 This is the tolerance criterion for convergence and refers to changes in
 the objective function value. It should be rather loose since it will
-gradually be tighened during estimation. Default: @code{1e-3}
+gradually be tightened during estimation. Default: @code{1e-3}
 
 @item convergence_ending_value = @var{DOUBLE}
 The convergence criterion ending value. Values much smaller than square
@@ -7024,7 +7436,7 @@ The entire process described by @ref{max_block_iterations} is repeated
 with random starting values drawn from the posterior. This specifies the
 number of random starting values used. Set this to @code{0} to not use
 random starting values. A larger number should be specified to ensure
-that the entire parameter space has been covererd. Default: @code{5}
+that the entire parameter space has been covered. Default: @code{5}
 
 @item number_of_small_perturbations = @var{INTEGER}
 The number of small perturbations to make after the large perturbations
@@ -7038,7 +7450,7 @@ small number will result in a small perturbation. Default: @code{1}
 
 @item max_number_of_stages = @var{INTEGER}
 The small and large perturbation are repeated until improvement has
-stopped. This specifices the maximum number of stages allowed. Default:
+stopped. This specifics the maximum number of stages allowed. Default:
 @code{20}
 
 @item random_function_convergence_criterion = @var{DOUBLE}
@@ -7099,7 +7511,7 @@ The total number of draws is equal to
 @code{thinning_factor*mh_replic+drop}.  Default: @code{1}
 
 @item adaptive_mh_draws = @var{INTEGER}
-Tuning period for Metropolis-Hasting draws. Default: @code{30,000}
+Tuning period for Metropolis-Hastings draws. Default: @code{30,000}
 
 @item save_draws
 Save all elements of @math{A^0}, @math{A^+}, @math{Q}, and
@@ -7144,7 +7556,7 @@ log marginal densities are contained in the @code{oo_.ms} structure.
 
 @item simulation_file_tag = @var{FILENAME}
 @anchor{simulation_file_tag} The portion of the filename associated with
-the simulation run.  Defualt: @code{<file_tag>}
+the simulation run.  Default: @code{<file_tag>}
 
 @item proposal_type = @var{INTEGER}
 The proposal type:
@@ -7778,7 +8190,7 @@ Includes @file{modeldesc.mod}, calibrates parameters and runs
 stochastic simulations
 @item estim.mod
 Includes @file{modeldesc.mod}, declares priors on parameters and runs
-bayesian estimation
+Bayesian estimation
 @end table
 
 Dynare can be called on @file{simul.mod} and @file{estim.mod}, but it
@@ -7871,7 +8283,7 @@ The labor share in GDP is defined as:
 In the model, @math{\alpha} is a (share) parameter, and
 @code{lab_rat} is an endogenous variable.
 
-It is clear that calibrating @math{\alpha} is not straigthforward; but
+It is clear that calibrating @math{\alpha} is not straightforward; but
 on the contrary, we have real world data for @code{lab_rat}, and
 it is clear that these two variables are economically linked.
 
@@ -8096,6 +8508,12 @@ the one that is highest on the MATLAB/Octave path).
 
 @end deffn
 
+@deffn {MATLAB/Octave command} write_latex_definitions ;
+
+Writes the names, @LaTeX{} names and long names of model variables to
+tables in a file named @code{<<M_.fname>>_latex_definitions.tex}.
+
+@end deffn
 
 
 @node The Configuration File
@@ -8169,7 +8587,7 @@ processing. Currently, there is only one option available.
 @descriptionhead
 
 The @code{[hooks]} block can be used to specify configuration options
-that will be used when running dynare.
+that will be used when running Dynare.
 
 @optionshead
 
@@ -8438,14 +8856,16 @@ using time series.
 @subsection dates in a mod file
 
 Dynare  understands dates  in  a  mod file.  Users  can declare  annual,
-quaterly, monthly or weekly dates using the following syntax:
+quarterly, monthly or weekly dates using the following syntax:
+
 @example
 1990Y
 1990Q3
 1990M11
 1990W49
 @end example
-@noindent Behind the scene, the dynare's preprocessor translates these expressions
+
+@noindent Behind the scene, Dynare's preprocessor translates these expressions
 into instantiations of the  Matlab/Octave's class @dates described
 below. Basic  operations can be performed  on dates:
 @table @strong
@@ -8461,11 +8881,15 @@ Increments a date by one period. @code{+1950Q1} is identical to @code{1950Q2}, @
 
 @item minus binary operator (@code{-})
 
-The difference between two dates is a number of periods. For instance if @code{1951Q2-1950Q1} is equal to  @code{5} (quarters). 
+Has two functions: difference and subtraction. If the second argument
+is a date, calculates the difference between the first date and the
+second date (@i{e.g.} @code{1951Q2-1950Q1} is equal to @code{5}). If
+the second argument is an integer @code{X}, subtracts @code{X} periods
+from the date (@i{e.g.} @code{1951Q2-2} is equal to @code{1950Q4}).
 
 @item minus unary operator (@code{-})
 
-Substracts one period to a date. @code{-1950Q1} is identical to @code{1949Q4}. The unary minus operator is the reciprocal of the unary plus operator, @code{+-1950Q1} is identical to @code{1950Q1}.
+Subtracts one period to a date. @code{-1950Q1} is identical to @code{1949Q4}. The unary minus operator is the reciprocal of the unary plus operator, @code{+-1950Q1} is identical to @code{1950Q1}.
 
 @item colon operator (@code{:})
 
@@ -8485,7 +8909,10 @@ Tests if two @dates objects are equal. @code{+1950Q1==1950Q2} returns @code{1},
 
 @item ne operator (not equal, @code{~=})
 
-Tests if two @dates objects are not equal. @code{+1950Q1==1950Q2} returns @code{0}, @code{1950Q1==1950Q2} returns @code{1}. If the compared objects have both @code{n>1} elements, the @code{ne} operator returns a column vector, @code{n} by @code{1}, of zeros and ones.
+Tests if two @dates objects are not equal. @code{+1950Q1~=1950Q2}
+returns @code{0} while @code{1950Q1~=1950Q2} returns @code{1}. If the
+compared objects both have @code{n>1} elements, the @code{ne} operator
+returns an @code{n} by @code{1} column vector of zeros and ones.
 
 @item lt operator (less than, @code{<})
 
@@ -8507,6 +8934,33 @@ Tests if a @dates object follows another @dates object or is equal to this objec
 
 @noindent One can select an element, or some elements, in a @dates object as he would extract some elements from a vector in Matlab/Octave. Let @code{a = 1950Q1:1951Q1} be a @dates object, then @code{a(1)==1950Q1} returns @code{1}, @code{a(end)==1951Q1} returns @code{1} and @code{a(end-1:end)} selects the two last elements of @code{a} (by instantiating the @dates object @code{[1950Q4, 1951Q1]}).
 
+@remarkhead
+@noindent Dynare substitutes any occurrence of dates in the mod file into an instantiation of the @dates class regardless of the context. For instance, @code{d = 1950Q1;} will be translated as @code{d = dates('1950Q1');}. This automatic substitution can lead to a crash if a date is defined in a string. Typically, if the user wants to display a date:
+
+@example
+disp('Initial period is 1950Q1');
+@end example
+
+@noindent Dynare will translate this as:
+
+@example
+disp('Initial period is dates('1950Q1')');
+@end example
+
+@noindent which will lead to a crash because this expression is illegal in Matlab. For this situation, Dynare provides the @code{$} escape parameter. The following expression:
+
+@example
+disp('Initial period is $1950Q1');
+@end example
+
+@noindent will be translated as:
+
+@example
+disp('Initial period is 1950Q1');
+@end example
+
+@noindent in the generated MATLAB script.
+
 @node dates class
 @subsection dates class
 
@@ -8515,7 +8969,7 @@ The @dates class has three members:
 @anchor{dates class members}
 
 @item freq
-an integer equal to 1, 4, 12  or 52 (resp. for annual, quaterly, monthly
+an integer equal to 1, 4, 12  or 52 (resp. for annual, quarterly, monthly
 or weekly dates).
 
 @item ndat
@@ -8523,12 +8977,14 @@ an integer scalar, the number of declared dates in the object.
 
 @item time
 a @code{ndat}*2  array of integers,  the years  are stored in  the first
-column, the subperiods (1 for annual dates, 1-4 for quaterly dates, 1-12
+column, the subperiods (1 for annual dates, 1-4 for quarterly dates, 1-12
 for monthly  dates and 1-52 for  weekly dates) are stored  in the second
 column.
 
 @end table
+
 @noindent Each member is private, one can display the content of a member but cannot change its value:
+
 @example
 >> d = dates('2009Q2');
 >> d.time
@@ -8547,15 +9003,19 @@ ans =
 @deftypefn  {dates} dates ()
 @deftypefnx {dates} dates (@code{FREQ})
 
-Returns an empty @dates object with  a given frequency (if the constructor is called with one input argument).  @code{FREQ} is a character equal to 'Y' or 'A'  for annual dates, 'Q' for quaterly dates, 'M' for monthly dates or 'W'  for weekly dates. Note that @code{FREQ} is not  case sensitive,  so that,  for instance,  'q' is  also allowed  for quaterly dates. The frequency can also be set with an integer scalar equal to 1 (annual), 4 (quaterly), 12 (monthly) or 52 (weekly). The instantiation of empty objects can be used to rename  the @dates class. For  instance, if one  only works  with quaterly dates, he can create @code{qq} as:
+Returns an empty @dates object with  a given frequency (if the constructor is called with one input argument).  @code{FREQ} is a character equal to 'Y' or 'A'  for annual dates, 'Q' for quarterly dates, 'M' for monthly dates or 'W'  for weekly dates. Note that @code{FREQ} is not  case sensitive,  so that,  for instance,  'q' is  also allowed  for quarterly dates. The frequency can also be set with an integer scalar equal to 1 (annual), 4 (quarterly), 12 (monthly) or 52 (weekly). The instantiation of empty objects can be used to rename  the @dates class. For  instance, if one  only works  with quarterly dates, he can create @code{qq} as:
+
 @example
 qq = dates('Q')
 @end example
-and a @dates object holding the date @code{2009Q2}:
+
+@noindent and a @dates object holding the date @code{2009Q2}:
+
 @example
-d0 = dates(2009,2);
+d0 = qq(2009,2);
 @end example
-@noindent which is much simpler if @dates objects have to be defined programatically.
+
+@noindent which is much simpler if @dates objects have to be defined programmatically.
 
 @end deftypefn
 
@@ -8568,7 +9028,6 @@ Returns a @dates object that represents a date as given by the string @code{STRI
 
 @end deftypefn
 
-
 @sp 1
 
 @deftypefn {dates} dates (@code{DATES})
@@ -8582,7 +9041,7 @@ Returns a copy of the  @dates object @code{DATES} passed as input arguments. If
 
 @deftypefn {dates} dates (@code{FREQ}, @code{YEAR}, @code{SUBPERIOD})
 
-where @code{FREQ} is a single character ('Y', 'A', 'Q', 'M', 'W') or integer (1, 4, 12 or 52) specifying the frequency, @code{YEAR} and @code{SUBPERIOD} are @code{n*1} vectors of integers. Returns a @dates object with @code{n} elements. If @code{FREQ} is equal to @code{'Y', 'A'} or @code{1}, the third argument is not needed (because @code{SUBPERIOD} is necessarly a vector of ones in this case).
+where @code{FREQ} is a single character ('Y', 'A', 'Q', 'M', 'W') or integer (1, 4, 12 or 52) specifying the frequency, @code{YEAR} and @code{SUBPERIOD} are @code{n*1} vectors of integers. Returns a @dates object with @code{n} elements. If @code{FREQ} is equal to @code{'Y', 'A'} or @code{1}, the third argument is not needed (because @code{SUBPERIOD} is necessarily a vector of ones in this case).
 
 @end deftypefn
 
@@ -8598,21 +9057,25 @@ do4 = dates('Q',1950, 1);
 
 @sp 1
 
-A list of the available methods, by alphabetical order, is given below. Note that the Matlab/Octave classes do not allow in place modifications: when a method is applied to an object a new object is instantiated. For instance, to apply the method @code{multiplybytwo} to an object @code{X} we write:
+@noindent A list of the available methods, by alphabetical order, is given below. Note that the Matlab/Octave classes do not allow in place modifications: when a method is applied to an object a new object is instantiated. For instance, to apply the method @code{multiplybytwo} to an object @code{X} we write:
+
 @example
 Y = X.multiplybytwo()
 @end example
-or equivalently:
+
+@noindent or equivalently:
+
 @example
 Y = multiplybytwo(X)
 @end example
-the object @code{X} is left unchanged, and the object @code{Y} is a modified copy of @code{X}.
+
+@noindent the object @code{X} is left unchanged, and the object @code{Y} is a modified copy of @code{X}.
 
 @sp 1
 
-@deftypefn {dates} {@var{B} = } append (@var{A}, @var{a})
+@deftypefn {dates} {@var{C} = } append (@var{A}, @var{B})
 
-Appends @dates object @var{a}, or a string that can be interpreted as a date, to the @dates object @var{A}. If @code{a} is a @dates object it is assumed that it has no more than one element.
+Appends @dates object @var{B}, or a string that can be interpreted as a date, to the @dates object @var{A}. If @var{B} is a @dates object it is assumed that it has no more than one element.
 
 @examplehead
 @example
@@ -8770,6 +9233,27 @@ C = <dates: 1951Q1, 1951Q2, 1951Q3, 1951Q4>
 
 @sp 1
 
+@deftypefn{dates} {@var{C} =} setdiff (@var{A}, @var{B})
+
+Overloads the Matlab/Octave @code{setdiff} function. All the input arguments must be @dates objects. The returned argument is a @dates object all dates present in @var{A} but not in @var{B}. If @var{A} and @var{B} are disjoint @dates objects, the function returns @var{A}. Returned dates in @dates object @var{C} are sorted by increasing order.
+
+@examplehead
+@example
+>> A = dates('1950Q1'):dates('1969Q4') ;
+>> B = dates('1960Q1'):dates('1969Q4') ;
+>> C = dates('1970Q1'):dates('1979Q4') ;
+>> d1 = setdiff(d1,d2);
+>> d2 = setdiff(d1,d3);
+d1 = <dates: 1950Q1, 1950Q2,  ..., 1959Q3, 1959Q4>
+d2 = <dates: 1950Q1, 1950Q2,  ..., 1969Q3, 1969Q4>
+@end example
+
+@end deftypefn
+
+
+@sp 1
+
+
 @deftypefn{dates} {@var{B} =} isempty (@var{A})
 
 Overloads the Matlab/Octave isempty function for @dates object.
@@ -8918,6 +9402,26 @@ ans = <dates: 1950Q3, 1950Q4, 1960Q1>
 
 @sp 1
 
+@deftypefn{dates} {@var{C} =} ne (@var{A}, @var{B})
+
+Overloads the Matlab/Octave @code{ne} (not equal, @code{~=}) operator. @dates objects @var{A} and @var{B} must have the  same number of elements (say, @code{n}) or one of the inputs must be a single element @dates object. The returned argument is a @code{n} by @code{1} vector of zeros and ones. The i-th element of @var{C} is equal to @code{1} if and only if the dates @code{A(i)} and @code{B(i)} are different.
+
+@examplehead
+@example
+>> A = dates('1950Q1','1951Q2');
+>> B = dates('1950Q1','1950Q2');
+>> A~=B
+
+ans =
+
+     0
+     1
+@end example
+
+@end deftypefn
+
+@sp 1
+
 @deftypefn{dates} {@var{C} =} plus (@var{A}, @var{B})
 
 Overloads the Matlab/Octave @code{plus} operator (@code{+}). If both input arguments are @dates objects, then the method combines A and B without removing repetitions. If @var{B} is a vector of integers, the @code{plus} operator shifts the @dates object by @var{B} periods forward.
@@ -9070,7 +9574,7 @@ A @code{nobs} by @code{vobs} array of doubles, the data.
 
 @end table
 
-@noindent The following constructors are available:
+@noindent @code{freq}, @code{nobs}, @code{vobs}, @code{data}, @code{name}, @code{tex} are private members. The following constructors are available:
 
 @deftypefn  {dseries} dseries ()
 @deftypefnx {dseries} dseries (@var{INITIAL_DATE})
@@ -9100,9 +9604,9 @@ If a @file{.mat} file is used instead, it should provide the same informations.
 
 @sp 1
 
-@deftypefn {dseries} dseries (@var{DATA_MATRIX}, @var{INITIAL_DATE}, @var{LIST_OF_NAMES}, @var{LIST_OF_TEX_NAMES})
+@deftypefn {dseries} dseries (@var{DATA_MATRIX}[, @var{INITIAL_DATE}[, @var{LIST_OF_NAMES}[, @var{LIST_OF_TEX_NAMES}]]])
 
-If the data is not read from a file, it can be provided via a @math{T}x@math{N} matrix as the first argument to @code{dseries}' constructor, with @math{T} representing the number of observations on @math{N} variables. The second argument, @var{INITIAL_DATE}, can be either a @dates object representing the period of the first observation or a string which would be used to instantiate a @dates object. The third argument, @var{LIST_OF_NAMES}, is a @math{N} by @math{1} cell of strings  with one entry for each variable name. The final argument, @var{LIST_OF_TEX_NAMES}, is a @math{N} by @math{1} cell of strings composed of the @LaTeX{} names associated with the variables. Input arguments two, three and four are optional. The default value for @var{INITIAL_DATE} is @code{1Y}.
+If the data is not read from a file, it can be provided via a @math{T}x@math{N} matrix as the first argument to @code{dseries}' constructor, with @math{T} representing the number of observations on @math{N} variables. The optional second argument, @var{INITIAL_DATE}, can be either a @dates object representing the period of the first observation or a string which would be used to instantiate a @dates object. Its default value is @code{dates('1Y')}. The optional third argument, @var{LIST_OF_NAMES}, is a @math{N} by @math{1} cell of strings  with one entry for each variable name. The default name associated with column @code{i} of @var{DATA_MATRIX} is @code{Variable_i}. The final argument, @var{LIST_OF_TEX_NAMES}, is a @math{N} by @math{1} cell of strings composed of the @LaTeX{} names associated with the variables. The default @LaTeX{} name associated with column @code{i} of @var{DATA_MATRIX} is @code{Variable\_i}.
 
 @end deftypefn
 
@@ -9116,21 +9620,23 @@ Various ways to create a @code{dseries} object:
 
 @example In a mod file:
 do1 = dseries(1999Q3);
-do2 = dseries(`filename.csv');
-do3 = dseries([1; 2; 3], 1999Q3, @{`var123'@}, @{`var_@{123@}'@});
+do2 = dseries('filename.csv');
+do3 = dseries([1; 2; 3], 1999Q3, @{'var123'@}, @{'var_@{123@}'@});
 @end example
 
 @sp 1
 
 @example In a Matlab/Octave script:
 >> do1 = dseries(dates('1999Q3'));
->> do2 = dseries(`filename.csv');
->> do3 = dseries([1; 2; 3], dates('1999Q3'), @{`var123'@}, @{`var_@{123@}'@});
+>> do2 = dseries('filename.csv');
+>> do3 = dseries([1; 2; 3], dates('1999Q3'), @{'var123'@}, @{'var_@{123@}'@});
 @end example
 
 @sp 1
 
-A list of the available methods, by alphabetical order, is given below.
+@noindent One can easily create subsamples from a @dseries object using the overloaded parenthesis operator. If @var{ds} is a @dseries object with @math{T} observations and @var{d} is a @dates object with @math{S<T} elements, such that @math{min(d)} is not smaller than the date associated to the first observation in @var{ds} and @math{max(d)} is not greater than the date associated to the last observation, then @code{ds(d)} instantiates a new @dseries object containing the subsample defined by @var{d}.
+
+@noindent A list of the available methods, by alphabetical order, is given below.
 
 @deftypefn {dseries} {[@var{A}, @var{B}] = } align (@var{A}, @var{B})
 
@@ -9172,7 +9678,7 @@ ts1 is a dseries object:
 
 @deftypefn {dseries} {@var{B} = } baxter_king_filter (@var{A}, @var{hf}, @var{lf}, @var{K})
 
-Implementation of Baxter and King (1999) band pass filter for @dseries objects. This filter isolates business cycle fluctuations with a period of length ranging between @var{hf} (high frequency) to @var{lf} (low frequency) using a symetric moving average smoother with @math{2K+1} points, so that K observations at the beginning and at the end of the  sample are lost in the computation of the filter.
+Implementation of the Baxter and King (1999) band pass filter for @dseries objects. This filter isolates business cycle fluctuations with a period of length ranging between @var{hf} (high frequency) to @var{lf} (low frequency) using a symmetric moving average smoother with @math{2K+1} points, so that K observations at the beginning and at the end of the  sample are lost in the computation of the filter. The default value for @var{hf} is @math{6}, for @var{lf} is @math{32}, and for @var{K} is 12.
 
 @examplehead
 @example
@@ -9226,7 +9732,7 @@ Sanity check of @dseries object @var{A}. Returns @math{1} if there is an error,
 
 @deftypefn {dseries} {@var{B} = } cumsum (@var{A}[, @var{d}[, @var{v}]])
 
-Overloads the Matlab/Octave @code{cumsum} function for @dseries objects. The cumulated sum cannot be computed if the variables in @dseries object @var{A} have @code{NaN}s. If a @dates object @var{d} is provided as a second argument, then the method computes the cumulated sum with the additional constraint that the variables in the @dseries object @var{B} are zero in period @var{d}. If a single observation @dseries object @var{v} is provided as a third argument, the cumulated sum in @var{B} is such that @code{B(d)} matches @var{v}.
+Overloads the Matlab/Octave @code{cumsum} function for @dseries objects. The cumulated sum cannot be computed if the variables in @dseries object @var{A} has @code{NaN}s. If a @dates object @var{d} is provided as a second argument, then the method computes the cumulated sum with the additional constraint that the variables in the @dseries object @var{B} are zero in period @var{d}. If a single observation @dseries object @var{v} is provided as a third argument, the cumulated sum in @var{B} is such that @code{B(@var{d})} matches @var{v} (@dseries objects @var{A} and @var{v} must have the same number of variables).
 
 @examplehead
 @example
@@ -9321,7 +9827,7 @@ Overloads the Matlab/Octave @code{exp} function for @dseries objects.
 
 @sp 1
 
-@deftypefn{dseries} {@var{C} =} extract (@var{A}, @var{B}[, ]...)
+@deftypefn{dseries} {@var{C} =} extract (@var{A}, @var{B}[, ...])
 
 Extracts some variables from a @dseries object @var{A} and returns a @dseries object @var{C}. The input arguments following @var{A} are strings representing the variables to be selected in the new @dseries object @var{C}. To simplify the creation of sub-objects, the @dseries class overloads the curly braces (@code{D = extract (A, B, C)} is equivalent to @code{D = A@{B,C@}}) and allows implicit loops (defined between a pair of @@ symbol, see examples below) or Matlab/Octave's regular expressions (introduced by square brackets).
 
@@ -9363,9 +9869,16 @@ ans is a dseries object:
 
 @sp 1
 
-@deftypefn{dseries} {@var{D} =} horzcat (@var{A}, @var{B}[, ]...)
+@deftypefn{dseries} {@var{D} =} horzcat (@var{A}, @var{B}[, ...])
 
-Overloads the @code{horzcat} Matlab/Octave's method for @dseries objects. Returns a @dseries object @var{D} containing the variables in @dseries objects passed as inputs: @var{A}, @var{B}, ... If the inputs are not defined on the same time ranges, the method add @code{NaN}s to the variables so that the variables are redefined on the smallest common time range. Note that the names in the @dseries objects passed as inputs must be different and these objects must have common frequency.
+Overloads the @code{horzcat} Matlab/Octave's method for @dseries
+objects. Returns a @dseries object @var{D} containing the variables
+in @dseries objects passed as inputs: @var{A}, @var{B}, ... If the
+inputs are not defined on the same time ranges, the method adds
+@code{NaN}s to the variables so that the variables are redefined on
+the smallest common time range. Note that the names in the @dseries
+objects passed as inputs must be different and these objects must have
+common frequency.
 
 @examplehead
 @example
@@ -9394,7 +9907,10 @@ ts2 is a dseries object:
 
 @deftypefn {dseries} {@var{B} = } hpcycle (@var{A}[, @var{lambda}])
 
-Extracts the cycle component from a @dseries @var{A} object using Hodrick Prescott filter and returns a @dseries object, @var{B}. Default value for @var{lambda}, the smoothing parameter, is  @math{1600}.
+Extracts the cycle component from a @dseries @var{A} object using
+Hodrick Prescott (1997) filter and returns a @dseries object, @var{B}. The
+default value for @var{lambda}, the smoothing parameter, is
+@math{1600}.
 
 @examplehead
 @example
@@ -9440,7 +9956,7 @@ The previous code should produce something like:
 
 @deftypefn {dseries} {@var{B} = } hptrend (@var{A}[, @var{lambda}])
 
-Extracts the trend component from a @dseries @var{A} object using Hodrick Prescott filter and returns a @dseries object, @var{B}. Default value for @var{lambda}, the smoothing parameter, is  @math{1600}.
+Extracts the trend component from a @dseries @var{A} object using Hodrick Prescott (1997) filter and returns a @dseries object, @var{B}. Default value for @var{lambda}, the smoothing parameter, is  @math{1600}.
 
 @examplehead
 Using the same generating data process as in the previous example:
@@ -9472,7 +9988,7 @@ The previous code should produce something like:
 
 @deftypefn {dseries} {@var{C} = } insert (@var{A}, @var{B}, @var{I})
 
-Inserts variables contained in @dseries object @var{B} in @dseries object @var{A} at positions specified by integer scalars in vector @var{I}, returns augmented @dseries object @var{C}. The integer scalars in @var{I} must take values between @code{1} and @code{A.length()+1} and refers to @var{A}'s column numbers. The @dseries objects @var{A} and @var{B} need not to be defined over the same time ranges, but it is assumled that they have common frequency.
+Inserts variables contained in @dseries object @var{B} in @dseries object @var{A} at positions specified by integer scalars in vector @var{I}, returns augmented @dseries object @var{C}. The integer scalars in @var{I} must take values between @code{1} and @code{A.length()+1} and refers to @var{A}'s column numbers. The @dseries objects @var{A} and @var{B} need not to be defined over the same time ranges, but it is assumed that they have common frequency.
 
 @examplehead
 @example
@@ -9620,6 +10136,7 @@ ts2 is a dseries object:
 @noindent @remarkhead
 
 @noindent The overloading of the parenthesis for @dseries objects, allows to easily create new @dseries objects by copying/pasting equations declared in the @code{model} block. For instance, if an Euler equation is defined in the @code{model} block:
+
 @example
 model;
     ...
@@ -9627,11 +10144,14 @@ model;
     ...
 end;
 @end example
+
 @noindent and if variables @var{C}, @var{A} and @var{K} are defined as @dseries objects, then by writting:
+
 @example
 Residuals = 1/C - beta/C(1)*(exp(A(1))*K^(alpha-1)+1-delta) ;
 @end example
-@noindent outside of the @code{model} block, we create a new @dseries object, called @var{Residuals}, for the residuals of the Euler equation (the conditional expectation of the equation defined in the @code{model} block is zero, but the residuals are non zero).
+
+@noindent outside of the @code{model} block, we create a new @dseries object, called @code{Residuals}, for the residuals of the Euler equation (the conditional expectation of the equation defined in the @code{model} block is zero, but the residuals are non zero).
 
 @sp 1
 
@@ -9699,7 +10219,28 @@ ans is a dseries object:
 
 @deftypefn{dseries} {@var{C} =} minus (@var{A}, @var{B})
 
-Overloads the @code{minus} (@code{-}) operator for @dseries objects, element by element substraction. If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then  @math{N_A} must be equal to @math{N_B} or @math{1} and  @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{minus} operator will compute for each couple  @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)-B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, the @code{minus} operator will substract the variable defined in @var{B} to each variable in @var{A}. If @var{B} is a double scalar, then the method @code{minus} will substract @var{B} to all the observations/variables in @var{A}.
+Overloads the @code{minus} (@code{-}) operator for @dseries objects,
+element by element subtraction. If both @var{A} and @var{B}
+are @dseries objects, they do not need to be defined over the same
+time ranges. If @var{A} and @var{B} are @dseries objects with
+@math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B}
+variables, then @math{N_A} must be equal to @math{N_B} or @math{1} and
+@math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B},
+@code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the
+@code{minus} operator will compute for each couple @math{(t,n)}, with
+@math{1\le t\le T_A} and @math{1\le n\le N_A},
+@code{C.data(t,n)=A.data(t,n)-B.data(t,n)}. If @math{N_B} is equal to
+@math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is
+``broadcast'' across the larger @dseries (@var{A}) so that they have
+compatible shapes, the @code{minus} operator will subtract the
+variable defined in @var{B} from each variable in @var{A}. If @var{B}
+is a double scalar, then the method @code{minus} will subtract
+@var{B} from all the observations/variables in @var{A}. If @var{B} is
+a row vector of length @math{N_A}, then the @code{minus} method will
+subtract @code{B(i)} from all the observations of variable @code{i},
+for @math{i=1,...,N_A}. If @var{B} is a column vector of length
+@math{T_A}, then the @code{minus} method will subtract @code{B} from
+all the variables.
 
 @examplehead
 @example
@@ -9746,9 +10287,61 @@ ans is a dseries object:
 
 @sp 1
 
+@deftypefn{dseries} {@var{C} =} mpower (@var{A}, @var{B})
+
+Overloads the @code{mpower} (@code{^}) operator for @dseries objects and computes element-by-element power. @var{A} is a @dseries object with @code{N} variables and @code{T} observations. If @var{B} is a real scalar, then @code{mpower(@var{A},@var{B})} returns a @dseries object @var{C} with @code{C.data(t,n)=A.data(t,n)^C}. If @var{B} is a @dseries object with @code{N} variables and @code{T} observations then @code{mpower(@var{A},@var{B})} returns a @dseries object @var{C} with @code{C.data(t,n)=A.data(t,n)^C.data(t,n)}.
+
+@examplehead
+@example
+>> ts0 = dseries(transpose(1:3));
+>> ts1 = ts0^2
+
+ts1 is a dseries object:
+
+   | power(Variable_1,2)
+1Y | 1
+2Y | 4
+3Y | 9
+
+>> ts2 = ts0^ts0
+
+ts2 is a dseries object:
+
+   | power(Variable_1,Variable_1)
+1Y | 1
+2Y | 4
+3Y | 27
+@end example
+
+@end deftypefn
+
+@sp 1
+
 @deftypefn{dseries} {@var{C} =} mrdivide (@var{A}, @var{B})
 
-Overloads the @code{mrdivide} (@code{/}) operator for @dseries objects, element by element division (like the @code{./} Matlab/Octave operator). If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then  @math{N_A} must be equal to @math{N_B} or @math{1} and  @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{mrdivide} operator will compute for each couple  @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)/B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, @code{mrdivides} operator will divide each variable defined in @var{A} by the variable in @var{B}, observation per observation. If @var{B} is a double scalar, then the method @code{mrdivide} will divide all the observations/variables in @var{A} by @var{B}.
+Overloads the @code{mrdivide} (@code{/}) operator for @dseries
+objects, element by element division (like the @code{./} Matlab/Octave
+operator). If both @var{A} and @var{B} are @dseries objects, they do
+not need to be defined over the same time ranges. If @var{A} and
+@var{B} are @dseries objects with @math{T_A} and @math{T_B}
+observations and @math{N_A} and @math{N_B} variables, then @math{N_A}
+must be equal to @math{N_B} or @math{1} and @math{N_B} must be equal
+to @math{N_A} or @math{1}. If @math{T_A=T_B},
+@code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the
+@code{mrdivide} operator will compute for each couple @math{(t,n)},
+with @math{1\le t\le T_A} and @math{1\le n\le N_A},
+@code{C.data(t,n)=A.data(t,n)/B.data(t,n)}. If @math{N_B} is equal to
+@math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is
+``broadcast'' across the larger @dseries (@var{A}) so that they have
+compatible shapes. In this case the @code{mrdivides} operator will
+divide each variable defined in @var{A} by the variable in @var{B},
+observation per observation. If @var{B} is a double scalar, then
+@code{mrdivide} will divide all the observations/variables in @var{A}
+by @var{B}. If @var{B} is a row vector of length @math{N_A}, then
+@code{mrdivide} will divide all the observations of variable @code{i}
+by @code{B(i)}, for @math{i=1,...,N_A}. If @var{B} is a column vector
+of length @math{T_A}, then @code{mrdivide} will perform a division of
+all the variables by @code{B}, element by element.
 
 @examplehead
 @example
@@ -9778,7 +10371,29 @@ ans is a dseries object:
 
 @deftypefn{dseries} {@var{C} =} mtimes (@var{A}, @var{B})
 
-Overloads the @code{mtimes} (@code{*}) operator for @dseries objects, Hadammard product (the @code{.*} Matlab/Octave operator). If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then  @math{N_A} must be equal to @math{N_B} or @math{1} and  @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{mtimes} operator will compute for each couple  @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)*B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, @code{mtimes} operator will multiply each variable defined in @var{A} by the variable in @var{B}, observation per observation. If @var{B} is a double scalar, then the method @code{mtimes} will multiply all the observations/variables in @var{A} by @var{B}.
+Overloads the @code{mtimes} (@code{*}) operator for @dseries objects
+and the Hadammard product (the @code{.*} Matlab/Octave operator). If
+both @var{A} and @var{B} are @dseries objects, they do not need to be
+defined over the same time ranges. If @var{A} and @var{B} are @dseries
+objects with @math{T_A} and @math{T_B} observations and @math{N_A} and
+@math{N_B} variables, then @math{N_A} must be equal to @math{N_B} or
+@math{1} and @math{N_B} must be equal to @math{N_A} or @math{1}. If
+@math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and
+@math{N_A=N_B}, then the @code{mtimes} operator will compute for each
+couple @math{(t,n)}, with @math{1\le t\le T_A} and @math{1\le n\le N_A},
+@code{C.data(t,n)=A.data(t,n)*B.data(t,n)}. If @math{N_B} is equal to
+@math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is
+``broadcast'' across the larger @dseries (@var{A}) so that they have
+compatible shapes, @code{mtimes} operator will multiply each variable
+defined in @var{A} by the variable in @var{B}, observation per
+observation. If @var{B} is a double scalar, then the method
+@code{mtimes} will multiply all the observations/variables in @var{A}
+by @var{B}. If @var{B} is a row vector of length @math{N_A}, then the
+@code{mtimes} method will multiply all the observations of variable
+@code{i} by @code{B(i)}, for @math{i=1,...,N_A}. If @var{B} is a
+column vector of length @math{T_A}, then the @code{mtimes} method will
+perform a multiplication of all the variables by @code{B}, element by
+element.
 
 @end deftypefn
 
@@ -9859,15 +10474,35 @@ Overloads Matlab/Octave's @code{plot} function for @dseries objects. Returns a M
 
 @deftypefn{dseries} {@var{C} =} plus (@var{A}, @var{B})
 
-Overloads the @code{plus} (@code{+}) operator for @dseries objects, element by element addition. If both @var{A} and @var{B} are @dseries objects, they do not need to be defined over the same time ranges. If @var{A} and @var{B} are @dseries object with @math{T_A} and @math{T_B} observations and @math{N_A} and @math{N_B} variables, then  @math{N_A} must be equal to @math{N_B} or @math{1} and  @math{N_B} must be equal to @math{N_A} or @math{1}. If @math{T_A=T_B}, @code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the @code{minus} operator will compute for each couple  @math{(t,n)}, with @math{1<=t<=T_A} and @math{1<=n<=N_A}, @code{C.data(t,n)=A.data(t,n)+B.data(t,n)}. If @math{N_B} is equal to @math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is ``broadcast'' across the larger @dseries (@var{A}) so that they have compatible shapes, the @code{plus} operator will add the variable defined in @var{B} to each variable in @var{A}. If @var{B} is a double scalar, then the method @code{add} will add @var{B} to all the observations/variables in @var{A}.
+Overloads the @code{plus} (@code{+}) operator for @dseries objects,
+element by element addition. If both @var{A} and @var{B} are @dseries
+objects, they do not need to be defined over the same time ranges. If
+@var{A} and @var{B} are @dseries objects with @math{T_A} and @math{T_B}
+observations and @math{N_A} and @math{N_B} variables, then @math{N_A}
+must be equal to @math{N_B} or @math{1} and @math{N_B} must be equal
+to @math{N_A} or @math{1}. If @math{T_A=T_B},
+@code{isequal(A.init,B.init)} returns 1 and @math{N_A=N_B}, then the
+@code{plus} operator will compute for each couple @math{(t,n)}, with
+@math{1\le t\le T_A} and @math{1\le n\le N_A},
+@code{C.data(t,n)=A.data(t,n)+B.data(t,n)}. If @math{N_B} is equal to
+@math{1} and @math{N_A>1}, the smaller @dseries object (@var{B}) is
+``broadcast'' across the larger @dseries (@var{A}) so that they have
+compatible shapes, the @code{plus} operator will add the variable
+defined in @var{B} to each variable in @var{A}. If @var{B} is a double
+scalar, then the method @code{plus} will add @var{B} to all the
+observations/variables in @var{A}. If @var{B} is a row vector of
+length @math{N_A}, then the @code{plus} method will add @code{B(i)} to
+all the observations of variable @code{i}, for @math{i=1,...,N_A}. If
+@var{B} is a column vector of length @math{T_A}, then the @code{plus}
+method will add @code{B} to all the variables.
 
 @end deftypefn
 
 @sp 1
 
-@deftypefn{dseries} {@var{B} =} pop (@var{A}[, @var{a}])
+@deftypefn{dseries} {@var{C} =} pop (@var{A}[, @var{B}])
 
-Removes variable @var{a} from @dseries object @var{A}. By default, if the second argument is not provided, the last variable is removed.
+Removes variable @var{B} from @dseries object @var{A}. By default, if the second argument is not provided, the last variable is removed.
 
 @examplehead
 @example
@@ -9889,7 +10524,7 @@ ts1 is a dseries object:
 @deftypefn{dseries} {@var{B} =} qdiff (@var{A})
 @deftypefnx{dseries} {@var{B} =} qgrowth (@var{A})
 
-Computes quaterly differences or growth rates.
+Computes quarterly differences or growth rates.
 
 @examplehead
 @example
@@ -9924,7 +10559,8 @@ ts1 is a dseries object:
 
 @deftypefn{dseries} {@var{B} =} rename (@var{A},@var{oldname},@var{newname})
 
-Rename variable @var{oldname} to @var{newname} in @dseries object @var{A}, returns a @dseries object.
+Rename variable @var{oldname} to @var{newname} in @dseries object
+@var{A}. Returns a @dseries object.
 
 @examplehead
 @example
@@ -9942,26 +10578,36 @@ ts1 is a dseries object:
 
 @sp 1
 
-@deftypefn{dseries} save (@var{A}[, @var{basename}[, @var{format}]])
+@deftypefn{dseries} save (@var{A}, @var{basename}[, @var{format}])
 
-Overloads the Matlab/Octave @code{save} function, saves @dseries object @var{A} to disk. Possible formats are @code{csv} (this is the default), @code{m} (Matlab/Octave script), and @code{mat} (Matlab binary data file). The name of the file without extension is specified by @var{basename}, by default @var{basename} is the name of the first input (namely, the @dseries object @var{A}).
+Overloads the Matlab/Octave @code{save} function and saves @dseries
+object @var{A} to disk. Possible formats are @code{csv} (this is the
+default), @code{m} (Matlab/Octave script), and @code{mat} (Matlab
+binary data file). The name of the file without extension is specified
+by @var{basename}.
 
 @examplehead
 @example
 >> ts0 = dseries(ones(2,2));
->> ts0.save();
+>> ts0.save('ts0');
 @end example
+
 @noindent The last command will create a file @code{ts0.csv} with the following content:
+
 @example
 ,Variable_1,Variable_2
 1Y,               1,               1
 2Y,               1,               1
 @end example
-To create a Matlab/octave script, the following command:
+
+@noindent To create a Matlab/octave script, the following command:
+
 @example
->> ts0.save([],'m');
+>> ts0.save('ts0','m');
 @end example
-will produce a file @code{ts0.m} with the following content:
+
+@noindent will produce a file @code{ts0.m} with the following content:
+
 @example
 % File created on 14-Nov-2013 12:08:52.
 
@@ -9979,6 +10625,7 @@ Variable_2 = [
               1
               1];
 @end example
+
 @noindent The generated (@code{csv}, @code{m}, or @code{mat}) files can be loaded when instantiating a @dseries object as explained above.
 
 @end deftypefn
@@ -9987,7 +10634,12 @@ Variable_2 = [
 
 @deftypefn{dseries} {@var{B} =} set_names (@var{A}, @var{s1}, @var{s2}, ...)
 
-Renames variables in @dseries object @var{A}, returns a @dseries object @var{B} with new names @var{s1}, @var{s2}, @var{s3}, ... The number of input arguments after the first one (@dseries object @var{A}) must be equal to @code{A.vobs} (the number of variables in @var{A}). @var{s1} will be the name of the first variable in @var{B}, @var{s2} the name of the second variable in @var{B}, and so on.
+Renames variables in @dseries object @var{A} and returns a @dseries
+object @var{B} with new names @var{s1}, @var{s2}, @var{s3}, ... The
+number of input arguments after the first one (@dseries object
+@var{A}) must be equal to @code{A.vobs} (the number of variables in
+@var{A}). @var{s1} will be the name of the first variable in @var{B},
+@var{s2} the name of the second variable in @var{B}, and so on.
 
 @examplehead
 @example
@@ -10024,7 +10676,8 @@ ans =
 
 @deftypefn{dseries} {@var{B} =} tex_rename (@var{A}, @var{name}, @var{newtexname})
 
-Redefines the tex name of variable @var{name} to @var{newtexname} in @dseries object @var{A}, returns a @dseries object.
+Redefines the tex name of variable @var{name} to @var{newtexname}
+in @dseries object @var{A}. Returns a @dseries object.
 
 @end deftypefn
 
@@ -10055,9 +10708,14 @@ ts1 is a dseries object:
 
 @sp 1
 
-@deftypefn{dseries} {@var{D} =} vertcat (@var{A}, @var{B}[, ]...)
+@deftypefn{dseries} {@var{D} =} vertcat (@var{A}, @var{B}[, ...])
 
-Overloads the @code{vertcat} Matlab/Octave's method for @dseries objects. This method is used to append more observations to a @dseries object. Returns a @dseries object @var{D} containing the variables in @dseries objects passed as inputs. All the input arguments must be @dseries objects with the same variables defined on @emph{different time ranges}.
+Overloads the @code{vertcat} Matlab/Octave method for @dseries
+objects. This method is used to append more observations to a @dseries
+object. Returns a @dseries object @var{D} containing the variables
+in @dseries objects passed as inputs. All the input arguments must
+be @dseries objects with the same variables defined on @emph{different
+time ranges}.
 
 @examplehead
 @example
@@ -10094,7 +10752,7 @@ created through Dynare or pick out the pieces you want for inclusion
 in your own paper.
 
 Reports are created and modified by calling methods on class
-objects. The objects are hierarchichal, with the following order (from
+objects. The objects are hierarchical, with the following order (from
 highest to lowest): @code{Report, Page, Section, Graph/Table/Vspace,
 Series}. For simplicity of syntax, we abstract away from these
 classes, allowing you to operate directly on a @code{Report} object,
@@ -10113,8 +10771,8 @@ with the example at the end of the section.
 Options to the methods are passed differently than those to Dynare
 commands. They take the form of named options to Matlab functions
 where the arguments come in pairs (@i{e.g.}
-@code{function_name(`option_1_name', `option_1_value',
-`option_2_name', `option_2_value', ...)}, where @code{option_X_name}
+@code{function_name('option_1_name', `option_1_value',
+'option_2_name', 'option_2_value', ...)}, where @code{option_X_name}
 is the name of the option while @code{option_X_value} is the value
 assigned to that option). The ordering of the option pairs matters
 only in the unusual case when an option is provided twice (probably
@@ -10206,7 +10864,7 @@ command. Default: @code{`!'}
 @end table
 @end defmethod
 
-@defmethod Report addGraph data, figname, graphSize, showGrid, showLegend, showLegendBox, legendLocation, legendOrientation, legendFontSize, seriesToUse, shade, shadeColor, shadeOpacity, title, xlabel, ylabel, xrange, xTickLabels, yrange, showZeroline
+@defmethod Report addGraph data, figname, figDirName, graphSize, showGrid, showLegend, showLegendBox, legendLocation, legendOrientation, legendFontSize, seriesToUse, shade, shadeColor, shadeOpacity, title, xlabel, ylabel, xrange, xTicks, xTickLabels, yrange, showZeroline
 Adds a @code{Graph} to a @code{Section}.
 @optionshead
 @table @code
@@ -10219,6 +10877,10 @@ The @code{dseries} that provides the data for the graph. Default:
 The name to use when saving this figure. Default: @code{[tempname
 `.tex']}
 
+@item figDirName, @code{STRING}
+The name of the folder in which to store this figure. Default:
+@code{tmpFigDir}
+
 @item graphSize, @code{NUMERICAL_VECTOR}
 The width and height to be passed to the third and fourth elements of
 the array passed to the @code{`Position'} option of Matlab's
@@ -10275,9 +10937,16 @@ The y-axis label. Default: @code{none}
 @item xrange, @code{dates}
 The boundary on the x-axis to display in the graph. Default: all
 
+@anchor{xTicks}
+@item xTicks, @code{NUMERICAL_VECTOR}
+Used only in conjunction with @ref{xTickLabels}, this option denotes
+the numerical position of the label along the x-axis. The positions
+begin at @math{1}. Default: set by Matlab/Octave.
+
+@anchor{xTickLabels}
 @item xTickLabels, @code{CELL_ARRAY_STRINGS}
-The labels to use for the xticks in the graph. Default: the dates of
-the @code{dseries}
+The labels to be mapped to the ticks provided by
+@ref{xTicks}. Default: the dates of the @code{dseries}
 
 @item yrange, @code{NUMERICAL_VECTOR}
 The boundary on the y-axis to display in the graph, represented as a
@@ -10290,16 +10959,11 @@ Display a solid black line at @math{y = 0}. Default: @code{false}
 @end table
 @end defmethod
 
-@defmethod Report addTable annualAverages, data, showHlines, precision, range, seriesToUse, title, titleSize, vlineAfter, vlineAfterEndOfPeriod, showVlines
+@defmethod Report addTable data, showHlines, precision, range, seriesToUse, title, titleSize, vlineAfter, vlineAfterEndOfPeriod, showVlines
 Adds a @code{Table} to a @code{Section}.
 @optionshead
 @table @code
 
-@item annualAverages, @code{bool}
-Compute the average over every year in the table and display it in a
-column to the right of the data (one column for every year). Only
-works for quarterly data. Default: @code{false}
-
 @item data, @code{dseries}
 @xref{data}.
 
@@ -10321,8 +10985,9 @@ Title for the table. Default: @code{none}
 @item titleSize, @code{STRING}
 @LaTeX{} string representing the size of the table title. Default: @code{large}
 
-@item vlineAfter, @code{dates}
-Show a vertical line after the specified date. Default: @code{empty}
+@item vlineAfter, @code{dates} | @code{CELL_ARRAY_DATES}
+Show a vertical line after the specified date (or dates if a cell
+array of dates is passed). Default: @code{empty}
 
 @item vlineAfterEndOfPeriod, @code{BOOLEAN}
 Show a vertical line after the end of every period (@i{i.e.} after
@@ -10334,7 +10999,7 @@ Whether or not to show vertical lines separating the columns. Default: @code{fal
 @end defmethod
 
 @anchor{addSeries}
-@defmethod Report addSeries data, graphLineColor, graphLineStyle, graphLineWidth, graphMarker, graphMarkerEdgeColor, graphMarkerFaceColor, graphMarkerSize, tableRowColor, tableShowMarkers, tableAlignRight, tableNegColor, tablePosColor, tableSubSectionHeader, zerotol
+@defmethod Report addSeries data, graphLineColor, graphLineStyle, graphLineWidth, graphMarker, graphMarkerEdgeColor, graphMarkerFaceColor, graphMarkerSize, tableDataRhs, tableRowColor, tableShowMarkers, tableAlignRight, tableNegColor, tablePosColor, tableSubSectionHeader, zerotol
 Adds a @code{Series} to a @code{Graph} or a @code{Table}.
 @optionshead
 @table @code
@@ -10363,6 +11028,13 @@ The face color of the graph marker. Default: @code{`auto'}
 @item graphMarkerSize, @code{DOUBLE}
 The size of the graph marker. Default: @code{6}
 
+@item tableDataRhs, @code{dseries}
+A series to be added to the right of the current series. Usefull for
+displaying aggregate data for a series. @i{e.g} if the series is
+quarterly @code{tableDataRhs} could point to the yearly averages of
+the quarterly series. This would cause quarterly data to be displayed
+followed by annual data. Default: @code{empty}
+
 @item tableRowColor, @code{STRING}
 The color that you want the row to be. Predefined values include
 @code{LightCyan} and @code{Gray}. Default: @code{white}.
@@ -10515,8 +11187,12 @@ Two examples of a small RBC model in a stochastic setup, presented in
 @cite{Collard (2001)} (see the file @file{guide.pdf} which comes with
 Dynare).
 
+@item example3.mod
+A small RBC model in a stochastic setup, presented in
+@cite{Collard (2001)}. The steady state is solved analytically using the  @code{steady_state_model} block (@pxref{steady_state_model}).
+
 @item fs2000.mod
-A cash in advance model, estimated by @cite{Schorfheide (2000)}.
+A cash in advance model, estimated by @cite{Schorfheide (2000)}. The file shows how to use Dynare for estimation.
 
 @item fs2000_nonstationary.mod
 The same model than @file{fs2000.mod}, but written in non-stationary
@@ -10524,12 +11200,15 @@ form. Detrending of the equations is done by Dynare.
 
 @item bkk.mod
 Multi-country RBC model with time to build, presented in @cite{Backus,
-Kehoe and Kydland (1992)}.
+Kehoe and Kydland (1992)}. The file shows how to use Dynare's macro-processor.
 
 @item agtrend.mod
 Small open economy RBC model with shocks to the growth trend, presented
 in @cite{Aguiar and Gopinath (2004)}.
 
+@item NK_baseline.mod
+Baseline New Keynesian Model estimated in @cite{Fernández-Villaverde (2010)}. It demonstrates how to use an explicit steady state file to update parameters and call a numerical solver.
+
 @end table
 
 @node Dynare misc commands
@@ -10570,10 +11249,10 @@ At this time, will work properly for only a small number of routines. At
 the top of the (available)  Matlab/Octave routines a commented block for
 the internal documentation  is written in the  GNU texinfo documentation
 format.   This   block   is    processed   by   calling   texinfo   from
-matlab. Consequently, texinfo has to be installed on your machine.
+MATLAB. Consequently, texinfo has to be installed on your machine.
 
 @item --display-mh-history
-Displays informations about the previously saved MCMC draws generated by a mod file named @var{MODFILENAME}. This file must be in the current directory.
+Displays information about the previously saved MCMC draws generated by a mod file named @var{MODFILENAME}. This file must be in the current directory.
 @examplehead
 @example
 >> internals --display-mh-history MODFILENAME
@@ -10627,7 +11306,7 @@ Aguiar, Mark and Gopinath, Gita (2004): ``Emerging Market Business
 Cycles: The Cycle is the Trend,'' @i{NBER Working Paper}, 10734
 
 @item
-Andreasen, Martin M., Fernández-Villaverde, Jesús and Juan Rubio-Ramírez (2013): ``The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications,'' @i{NBER Working Paper}, 18983
+Andreasen, Martin M., Jesús Fernández-Villaverde, and Juan Rubio-Ramírez (2013): ``The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications,'' @i{NBER Working Paper}, 18983
 
 @item
 Backus, David K., Patrick J. Kehoe, and Finn E. Kydland (1992):
@@ -10645,8 +11324,7 @@ monitoring  convergence   of  iterative  simulations,''   @i{Journal  of
 computational and graphical statistics}, 7, pp. 434--455
 
 @item
-Cardoso, Margarida F., R. L. Salcedo and S. Feyo de Azevedo (1996): ``The simplex simulated annealing approach to continuous non-linear optimization'', @i{Computers chem. Engng}, 20(9), 1065-1080
-
+Cardoso, Margarida F., R. L. Salcedo and S. Feyo de Azevedo (1996): ``The simplex simulated annealing approach to continuous non-linear optimization,'' @i{Computers chem. Engng}, 20(9), 1065-1080
 
 @item
 Collard, Fabrice (2001): ``Stochastic simulations with Dynare: A practical guide''
@@ -10678,8 +11356,8 @@ Models: New Solution Algorithms,'' @i{Macroeconomic Dynamics}, 11(1),
 31--55
 
 @item
-Durbin, J. and S. J. Koopman (2001), @i{Time Series Analysis by State
-Space Methods}, Oxford University Press
+Durbin, J. and S. J. Koopman (2012), @i{Time Series Analysis by State
+Space Methods}, Second Revised Edition, Oxford University Press
 
 @item
 Fair, Ray and John Taylor (1983): ``Solution and Maximum Likelihood
@@ -10696,9 +11374,13 @@ Fernández-Villaverde, Jesús and Juan Rubio-Ramírez (2005): ``Estimating
 Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood,''
 @i{Journal of Applied Econometrics}, 20, 891--910
 
+@item
+Fernández-Villaverde, Jesús (2010): ``The econometrics of DSGE models,''
+@i{SERIEs}, 1, 3--49
+
 @item
 Geweke, John (1992): ``Evaluating the accuracy of sampling-based approaches
-to the calculation of posterior moments'', in J.O. Berger, J.M. Bernardo, 
+to the calculation of posterior moments,'' in J.O. Berger, J.M. Bernardo,
 A.P. Dawid, and A.F.M. Smith (eds.) Proceedings of the Fourth Valencia
 International Meeting on Bayesian Statistics, pp. 169--194, Oxford University Press
 
@@ -10731,6 +11413,9 @@ Kim, Jinill, Sunghyun Kim, Ernst Schaumburg, and Christopher A. Sims
 discrete time dynamic equilibrium models,'' @i{Journal of Economic
 Dynamics and Control}, 32(11), 3397--3414
 
+@item
+Koop, Gary (2003), @i{Bayesian Econometrics}, John Wiley & Sons
+
 @item
 Koopman, S. J. and J. Durbin (2003): ``Filtering and Smoothing of
 State Vector for Diffuse State Space Models,'' @i{Journal of Time
@@ -10755,6 +11440,9 @@ Pearlman, Joseph, David Currie, and Paul Levine (1986): ``Rational
 expectations models with partial information,'' @i{Economic
 Modelling}, 3(2), 90--105
 
+@item
+Pfeifer, Johannes (2013): ``A Guide to Specifying Observation Equations for the Estimation of DSGE Models''
+
 @item
 Rabanal, Pau and Juan Rubio-Ramirez (2003): ``Comparing New Keynesian
 Models of the Business Cycle: A Bayesian Approach,'' Federal Reserve

dynare++/kord/journal.cweb

@@ -101,7 +101,7 @@ void SystemResources::getRUS(double& load_avg, long int& pg_avail,
 	majflt = -1;
 #endif
 
-#if !defined(__MINGW32__) && !defined(__CYGWIN32__)
+#if !defined(__MINGW32__) && !defined(__CYGWIN32__) && !defined(__CYGWIN__) && !defined(__MINGW64__) && !defined(__CYGWIN64__)
 	getloadavg(&load_avg, 1);
 #else
 	load_avg = -1.0;

dynare++/src/forw_subst_builder.cpp

 // Copyright (C) 2006-2011, Ondra Kamenik
 
+
 #include "forw_subst_builder.h"
 
 using namespace ogdyn;
@@ -47,8 +48,10 @@ void ForwSubstBuilder::substitute_for_term(int t, int i, int j)
 		// first make lagsubst be substitution setting f(x(+4)) to f(x(+1))
 		// this is lag = -3 (1-mlead)
 		map<int,int> lagsubst;
-		model.variable_shift_map(model.eqs.nulary_of_term(t), 1-mlead, lagsubst);
+        unordered_set<int> nult = model.eqs.nulary_of_term(t);// make copy of nult!
+		model.variable_shift_map(nult, 1-mlead, lagsubst);
 		int lagt = model.eqs.add_substitution(t, lagsubst);
+
         // now maxlead of lagt is +1
         // add AUXLD_*_*_1 = f(x(+1)) to the model
         char name[100];
@@ -82,7 +85,9 @@ void ForwSubstBuilder::substitute_for_term(int t, int i, int j)
 			aux_map.insert(Tsubstmap::value_type(ss, lagt));
 		}
 
-		// now we have to substitute AUXLEAD_*_*{mlead-1}(+1) for t
+		// now we have to substitute AUXLD_*_*{mlead-1}(+1) for t
+        sprintf(name, "AUXLD_%d_%d_%d", i, j, mlead-1);
+        ss = model.atoms.get_name_storage().query(name);
 		model.substitute_atom_for_term(ss, +1, t);
 	}
 }

examples/NK_baseline.mod

@@ -15,7 +15,14 @@
  * equations. Moreover, it makes use of a steady state file to i) set 
  * parameters that depend on other parameters that are potentially estimated
  * and ii) solve a nonlinear equation using a numerical solver to find the steady
- * state of labor.
+ * state of labor. It provides an example on how the steady state file can be used
+ * to circumvent some of the limitation of Dynare mod-file by accessing an external
+ * file that allows calling general Matlab routines. These capacities will mostly be 
+ * interesting for power users. If one just wants to provide analytical steady state 
+ * values and update parameters, the steady_state_model-block allows an easy and convenient
+ * alternative. It even allows calling numerical solvers like fsolve. For an example, see
+ * example3.mod
+ * 
  * 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.

examples/NK_baseline_steadystate.m

-function [ys,check] = NK_baseline_steadystate(ys,exe)
-global M_ lgy_
-
-if isfield(M_,'param_nbr') == 1
+function [ys,check] = NK_baseline_steadystate(ys,exo)
+% function [ys,check] = NK_baseline_steadystate(ys,exo)
+% computes the steady state for the NK_baseline.mod and uses a numerical
+% solver to do so
+% Inputs: 
+%   - ys        [vector] vector of initial values for the steady state of
+%                   the endogenous variables
+%   - exo       [vector] vector of values for the exogenous variables
+%
+% Output: 
+%   - ys        [vector] vector of steady state values fpr the the endogenous variables
+%   - check     [scalar] set to 0 if steady state computation worked and to
+%                    1 of not (allows to impos restriction on parameters)
+
+global M_ 
+
+% read out parameters to access them with their name
 NumberOfParameters = M_.param_nbr;
-for i = 1:NumberOfParameters
-  paramname = deblank(M_.param_names(i,:));
-  eval([ paramname ' = M_.params(' int2str(i) ');']);
+for ii = 1:NumberOfParameters
+  paramname = deblank(M_.param_names(ii,:));
+  eval([ paramname ' = M_.params(' int2str(ii) ');']);
 end
+% initialize indicator
 check = 0;
-end
 
 
 %% Enter model equations here
@@ -32,6 +45,11 @@ Lambdax=mu_z;
 
 %set the parameter gammma1
 gammma1=mu_z*mu_I/betta-(1-delta);
+if gammma1<0 % parameter violates restriction; Preventing this cannot be implemented via prior restriction as it is a composite of different parameters and the valid prior region has unknown form
+    check=1; %set failure indicator
+    return; %return without updating steady states
+end
+
 
 r=1*gammma1;
 R=1+(PI*mu_z/betta-1);
@@ -49,9 +67,17 @@ vp=(1-thetap)/(1-thetap*PI^((1-chi)*epsilon))*PIstar^(-epsilon);
 vw=(1-thetaw)/(1-thetaw*PI^((1-chiw)*eta)*mu_z^eta)*PIstarw^(-eta);
 tempvaromega=alppha/(1-alppha)*w/r*mu_z*mu_I;
 
-ld=fsolve(@(ld)(1-betta*thetaw*mu_z^(eta-1)*PI^(-(1-chiw)*(1-eta)))/(1-betta*thetaw*mu_z^(eta*(1+gammma))*PI^(eta*(1-chiw)*(1+gammma)))...
+[ld,fval,exitflag]=fsolve(@(ld)(1-betta*thetaw*mu_z^(eta-1)*PI^(-(1-chiw)*(1-eta)))/(1-betta*thetaw*mu_z^(eta*(1+gammma))*PI^(eta*(1-chiw)*(1+gammma)))...
 -(eta-1)/eta*wstar/(varpsi*PIstarw^(-eta*gammma)*ld^gammma)*((1-h*mu_z^(-1))^(-1)-betta*h*(mu_z-h)^(-1))*...
 ((mu_A*mu_z^(-1)*vp^(-1)*tempvaromega^alppha-tempvaromega*(1-(1-delta)*(mu_z*mu_I)^(-1)))*ld-vp^(-1)*Phi)^(-1),0.25,options);
+if exitflag <1
+    %indicate the SS computation was not sucessful; this would also be detected by Dynare
+    %setting the indicator here shows how to use this functionality to
+    %filter out parameter draws
+    check=1; %set failure indicator
+    return; %return without updating steady states
+end
+
 
 l=vw*ld;
 k=tempvaromega*ld;
@@ -68,27 +94,12 @@ g2=epsilon/(epsilon-1)*g1;
 
 %% end own model equations
 
-
-for iter = 1:length(M_.params)
+for iter = 1:length(M_.params) %update parameters set in the file
   eval([ 'M_.params(' num2str(iter) ') = ' M_.param_names(iter,:) ';' ])
 end
 
-if isfield(M_,'param_nbr') == 1
-
-if isfield(M_,'orig_endo_nbr') == 1
-NumberOfEndogenousVariables = M_.orig_endo_nbr;
-else
-NumberOfEndogenousVariables = M_.endo_nbr;
-end
-ys = zeros(NumberOfEndogenousVariables,1);
-for i = 1:NumberOfEndogenousVariables
-  varname = deblank(M_.endo_names(i,:));
-  eval(['ys(' int2str(i) ') = ' varname ';']);
-end
-else
-ys=zeros(length(lgy_),1);
-for i = 1:length(lgy_)
-    ys(i) = eval(lgy_(i,:));
-end
-check = 0;
+NumberOfEndogenousVariables = M_.orig_endo_nbr; %auxiliary variables are set automatically
+for ii = 1:NumberOfEndogenousVariables
+  varname = deblank(M_.endo_names(ii,:));
+  eval(['ys(' int2str(ii) ') = ' varname ';']);
 end

examples/example3.mod

+/*
+ * Example 1 from F. Collard (2001): "Stochastic simulations with DYNARE:
+ * A practical guide" (see "guide.pdf" in the documentation directory).
+ * 
+ * This file uses the steady_state_model-block to provide analytical steady state values.
+ * To do so, the equations of the model have been transformed into a non-linear equation in 
+ * labor h. Within the steady_state_model-block, a helper function is called that uses fsolve
+ * to solve this non-linear equation. The use of the helper function is necessary to avoid 
+ * interference of the Matlab syntax with Dynare's preprocessor. A more complicated alternative 
+ * that provides more flexibility in the type of commands executed and functions called is the use 
+ * of an explicit steady state file. See the NK_baseline.mod in the Examples Folder.
+ * 
+ * This mod-file also shows how to use Dynare's capacities to generate TeX-files of the model equations. 
+ * If you want to see the model equations belonging to this mod-file, run it using Dynare 
+ * and then use a TeX-editor to compile the TeX-files generated.
+ */
+
+/*
+ * Copyright (C) 2013 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 <http://www.gnu.org/licenses/>.
+ */
+
+
+var y, c, k, a, h, b;
+varexo e, u;
+
+parameters beta $\beta$
+     rho $\rho$
+     alpha $\alpha$
+     delta $\delta$
+     theta $\theta$
+     psi $\psi$
+     tau $\tau$;
+
+alpha = 0.36;
+rho   = 0.95;
+tau   = 0.025;
+beta  = 0.99;
+delta = 0.025;
+psi   = 0;
+theta = 2.95;
+
+phi   = 0.1;
+
+model;
+c*theta*h^(1+psi)=(1-alpha)*y;
+k = beta*(((exp(b)*c)/(exp(b(+1))*c(+1)))
+    *(exp(b(+1))*alpha*y(+1)+(1-delta)*k));
+y = exp(a)*(k(-1)^alpha)*(h^(1-alpha));
+k = exp(b)*(y-c)+(1-delta)*k(-1);
+a = rho*a(-1)+tau*b(-1) + e;
+b = tau*a(-1)+rho*b(-1) + u;
+end;
+
+steady_state_model;
+h=example3_steady_state_helper(alpha,beta,delta,psi,theta);
+k=((1/beta-(1-delta))/alpha)^(1/(alpha-1))*h;
+y = k^alpha*h^(1-alpha);
+c=(1-alpha)*y/(theta*h^(1+psi));
+a=0;
+b=0;
+end;
+
+shocks;
+var e; stderr 0.009;
+var u; stderr 0.009;
+var e, u = phi*0.009*0.009;
+end;
+
+//use command to generate TeX-Files with dynamic and static model equations
+write_latex_dynamic_model;
+write_latex_static_model;
+
+stoch_simul;

examples/example3_steady_state_helper.m

+function h=example3_steady_state_helper(alpha,beta,delta,psi,theta)
+options=optimset('Display','Final','TolX',1e-10,'TolFun',1e-10);
+h=fsolve(@(h)1- ((((((1/beta-(1-delta))/alpha)^(1/(alpha-1))*h)^(alpha-1))*(h^(1-alpha))-(((1-alpha)*((((1/beta-(1-delta))/alpha)^(1/(alpha-1)))^alpha))/(theta*h^psi))/(((1/beta-(1-delta))/alpha)^(1/(alpha-1))*h))+(1-delta)),0.2,options);
+
+

examples/fs2000.mod

@@ -87,6 +87,32 @@ var e_a; stderr 0.014;
 var e_m; stderr 0.005;
 end;
 
+steady_state_model;
+  dA = exp(gam);
+  gst = 1/dA;
+  m = mst;
+  khst = ( (1-gst*bet*(1-del)) / (alp*gst^alp*bet) )^(1/(alp-1));
+  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  = psi*mst*n/( (1-psi)*(1-n) );
+  c  = mst/P;
+  d  = l - mst + 1;
+  y  = k^alp*n^(1-alp)*gst^alp;
+  R  = mst/bet;
+  W  = l/n;
+  ist  = y-c;
+  q  = 1 - d;
+
+  e = 1;
+  
+  gp_obs = m/dA;
+  gy_obs = dA;
+end;
+
 steady;
 
 check;

license.txt

 Format: http://www.debian.org/doc/packaging-manuals/copyright-format/1.0/
 Upstream-Name: Dynare
-Upstream-Contact: Dynare Team, whose members in 2013 are:
+Upstream-Contact: Dynare Team, whose members in 2014 are:
                   Stéphane Adjemian <stephane.adjemian@univ-lemans.fr>
                   Houtan Bastani <houtan@dynare.org>
                   Michel Juillard <michel.juillard@mjui.fr>
@@ -14,7 +14,7 @@ Upstream-Contact: Dynare Team, whose members in 2013 are:
 Source: http://www.dynare.org
 
 Files: *
-Copyright: 1996-2013 Dynare Team
+Copyright: 1996-2014 Dynare Team
 License: GPL-3+
 
 Files: matlab/AIM/SP*
@@ -75,17 +75,13 @@ Copyright: 1995-2007 Kurt Hornik
            2008-2009 Dynare Team
 License: GPL-3+
 
-Files: matlab/missing/bicgstab/bicgstab.m
-Copyright: 2008 Radek Salac
-License: GPL-3+
-
 Files: matlab/missing/strjoin/strjoin.m
 Copyright: 2007 Muthiah Annamalai <muthiah.annamalai@uta.edu>
            2013 Dynare Team
 License: GPL-3+
 
 Files: doc/dynare.texi doc/*.tex doc/*.svg doc/*.dia doc/*.pdf doc/*.bib
-Copyright: 1996-2013 Dynare Team
+Copyright: 1996-2014 Dynare Team
 License: GFDL-NIV-1.3+
 
 Files: doc/userguide/*.tex doc/userguide/*.bib doc/userguide/*.pdf
@@ -119,17 +115,11 @@ License: GPL-3+ with Autoconf exception
 Files: m4/ax_boost_base.m4
 Copyright: 2008 Thomas Porschberg <thomas@randspringer.de>
            2009 Dynare Team
-License: other
- Copying and distribution of this file, with or without modification, are
- permitted in any medium without royalty provided the copyright notice
- and this notice are preserved.
+License: permissive
 
 Files: m4/ax_compare_version.m4
 Copyright: 2008 Tim Toolan <toolan@ele.uri.edu>
-License: other
- Copying and distribution of this file, with or without modification, are
- permitted in any medium without royalty provided the copyright notice
- and this notice are preserved.
+License: permissive
 
 Files: m4/ax_latex_bibtex_test.m4 m4/ax_latex_class.m4 m4/ax_tex_test.m4
 Copyright: 2008 Boretti Mathieu <boretti@eig.unige.ch>
@@ -345,3 +335,8 @@ License: LGPL-3+
  .
  You should have received a copy of the GNU Lesser General Public License
  along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+License: permissive
+ Copying and distribution of this file, with or without modification, are
+ permitted in any medium without royalty provided the copyright notice
+ and this notice are preserved.

m4/ax_matio.m4

 dnl Detect the MATIO Library.
 dnl
-dnl Copyright (C) 2012 Dynare Team
+dnl Copyright (C) 2012-2014 Dynare Team
 dnl
 dnl This file is part of Dynare.
 dnl
@@ -41,9 +41,10 @@ AC_ARG_WITH(matio, AC_HELP_STRING([--with-matio=DIR], [prefix to MATIO installat
   LDFLAGS="$LDFLAGS_MATIO $LDFLAGS"
 
   dnl Workaround for the matio from RHEL 6 + EPEL 6
-  dnl If detected, libz is added to LIBS, used for matio test
+  dnl If detected, libz and libhdf5 are added to LIBS, used for matio test
   LIBS=""
   AC_CHECK_LIB([z], [compress])
+  AC_CHECK_LIB([hdf5], [H5Fcreate])
 
   AC_CHECK_HEADER([matio.h], [], [has_matio=no])
   AC_CHECK_LIB([matio], [Mat_Open], [LIBADD_MATIO="-lmatio $LIBS"], [has_matio=no])

m4/ax_matlab_version.m4

-dnl Copyright (C) 2009-2013 Dynare Team
+dnl Copyright (C) 2009-2015 Dynare Team
 dnl
 dnl This file is part of Dynare.
 dnl
@@ -22,6 +22,15 @@ AC_REQUIRE([AX_MATLAB])
 AC_MSG_CHECKING([for MATLAB version])
 if test "x$MATLAB_VERSION" != "x"; then
   case $MATLAB_VERSION in
+    *2015a | *2015A)
+      MATLAB_VERSION="8.5"
+      ;;
+    *2014b | *2014B)
+      MATLAB_VERSION="8.4"
+      ;;
+    *2014a | *2014A)
+      MATLAB_VERSION="8.3"
+      ;;
     *2013b | *2013B)
       MATLAB_VERSION="8.2"
       ;;

matlab/@dates/ne.m

+function C = ne(A,B) % --*-- Unitary tests --*--
+
+% Overloads ~= operator for dates objects.
+%
+% INPUTS 
+%  o A    dates object with n or 1 elements.
+%  o B    dates object with n or 1 elements.
+%
+% OUTPUTS 
+%  o C    column vector of max(n,1) elements (zeros or ones).
+
+% Copyright (C) 2013 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 <http://www.gnu.org/licenses/>.
+
+if ~isequal(nargin,2)
+    error('dates::ne: I need exactly two input arguments!')
+end
+
+if ~isdates(A) || ~isdates(B)
+    error(['dates::ne: Input arguments ''' inputname(1) ''' and ''' inputname(2) ''' have to be dates objects!'])
+end
+
+if ~isequal(A.freq,B.freq)
+    C = 0;
+    return
+end
+
+if isequal(A.ndat, B.ndat)
+    C = transpose(any(transpose(ne(A.time,B.time))));
+else
+    if isequal(A.ndat,1) || isequal(B.ndat,1)
+        C = transpose(any(transpose(bsxfun(@ne,A.time,B.time))));
+    else
+        C = 0;
+    end
+end
+
+%@test:1
+%$ % Define some dates objects
+%$ d1 = dates('1950Q1','1950Q2','1950Q3','1950Q4') ;
+%$ d2 = dates('1960Q1','1960Q2','1960Q3','1960Q4') ;
+%$ d3 = dates('1950Q1','1960Q2','1950Q3','1960Q4') ;
+%$
+%$ % Call the tested routine.
+%$ t1 = d1~=d1;
+%$ t2 = d1~=d2;
+%$ t3 = d1~=d3;
+%$
+%$ % Check the results.
+%$ t(1) = dyn_assert(t1,zeros(4,1));
+%$ t(2) = dyn_assert(t2,ones(4,1));
+%$ t(3) = dyn_assert(t3,[0; 1; 0; 1]);
+%$ T = all(t);
+%@eof:1
+
+%@test:2
+%$ % Define some dates objects
+%$ d1 = dates('1950Q1') ;
+%$ d2 = dates('1960Q1') ;
+%$ d3 = dates('1960Q1') ;
+%$
+%$ % Call the tested routine.
+%$ t1 = d1~=d1;
+%$ t2 = d1~=d2;
+%$ t3 = d1~=d3;
+%$
+%$ % Check the results.
+%$ t(1) = dyn_assert(t1,0);
+%$ t(2) = dyn_assert(t2,1);
+%$ t(3) = dyn_assert(t3,1);
+%$ T = all(t);
+%@eof:2
+
+%@test:3
+%$ % Define some dates objects
+%$ d1 = dates('1950Q1','1950Q2','1950Q3','1950Q4') ;
+%$ d2 = dates('1950Q2') ;
+%$ d3 = dates('1970Q1') ;
+%$
+%$ % Call the tested routine.
+%$ t1 = d1~=d2;
+%$ t2 = d1~=d3;
+%$
+%$ % Check the results.
+%$ t(1) = dyn_assert(t1,[1; 0; 1; 1]);
+%$ t(2) = dyn_assert(t2,ones(4,1));
+%$ T = all(t);
+%@eof:3