NEWS

+Announcement for Dynare 4.4.0 (on 2013-12-11)
+=============================================
+
+We are pleased to announce the release of Dynare 4.4.0.
+
+This major release adds new features and fixes various bugs.
+
+The Windows package is already available for download. 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

@@ -78,9 +78,8 @@ 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).

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], [4.4.0])
 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"])
@@ -192,10 +188,6 @@ 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 @ abb30501

-Subproject commit 29c446f7fe8a773860ebee347662c4ae2494c5e9
+Subproject commit abb3050121bb92b63e665f697930cdf11fe018f5

utilities_dw @ 216abedb

-Subproject commit b388d5eac8c8d092088db5ef805584ae4dd2d952
+Subproject commit 216abedb9ab4df5dd4dca07c721f7c07392801e9

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;

matlab/@dseries/disp.m

@@ -23,7 +23,7 @@ separator = repmat(' | ',A.nobs+1,1);
 vspace = ' ';
 TABLE = ' ';
 for t=1:A.nobs
-    TABLE = char(TABLE, format(A.dates(t)));
+    TABLE = char(TABLE, date2string(A.dates(t)));
 end
 for i = 1:A.vobs
     TABLE = horzcat(TABLE,separator);

matlab/@dseries/mpower.m

+function A = mpower(B,C) % --*-- Unitary tests --*--
+
+%@info:
+%! @deftypefn {Function File} {@var{A} =} mpower (@var{B},@var{C})
+%! @anchor{@dseries/mpower}
+%! @sp 1
+%! Overloads the mpower method for the Dynare time series class (@ref{dseries}).
+%! @sp 2
+%! @strong{Inputs}
+%! @sp 1
+%! @table @ @var
+%! @item B
+%! Dynare time series object instantiated by @ref{dseries}, with T observations and N variables.
+%! @item C
+%! Real scalar or a dseries object with T observations and N variables.
+%! @end table
+%! @sp 1
+%! @strong{Outputs}
+%! @sp 1
+%! @table @ @var
+%! @item A
+%! dseries object with T observations and N variables.
+%! @end deftypefn
+%@eod:
+
+% 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 isdseries(B) && isnumeric(C) && isreal(C) && isscalar(C)
+    A = dseries();
+    A.freq = B.freq;
+    A.init = B.init;
+    A.dates = B.dates;
+    A.nobs = B.nobs;
+    A.vobs = B.vobs;
+    A.name = cell(A.vobs,1);
+    A.tex = cell(A.vobs,1);
+    for i=1:A.vobs
+        A.name(i) = {['power(' B.name{i} ',' num2str(C) ')']};
+        A.tex(i) = {[B.tex{i} '^' num2str(C) ]};
+    end
+    A.data = B.data.^C;
+    return
+end
+
+if isdseries(B) && isdseries(C)
+    if isequal(B.nobs,C.nobs) && isequal(B.vobs,C.vobs) && isequal(B.freq,C.freq)
+        A = dseries();
+        A.freq = B.freq;
+        A.init = B.init;
+        A.dates = B.dates;
+        A.nobs = B.nobs;
+        A.vobs = B.vobs;
+        A.name = cell(A.vobs,1);
+        A.tex = cell(A.vobs,1);
+        for i=1:A.vobs
+            A.name(i) = {['power(' B.name{i} ',' C.name{i} ')']};
+            A.tex(i) = {[B.tex{i} '^{' C.tex{i} '}']};
+        end
+        A.data = B.data.^C.data;
+    else
+        error('dseries::mpower: If both input arguments are dseries objects, they must have the same numbers of variables and observations and common frequency!')
+    end
+    return
+end
+
+error(['dseries::mpower: Wrong calling sequence!'])
+
+%@test:1
+%$ % Define a datasets.
+%$ A = rand(10,2); B = randn(10,2);
+%$
+%$ % Define names
+%$ A_name = {'A1';'A2'}; B_name = {'B1';'B2'};
+%$
+%$
+%$ % Instantiate a time series object.
+%$ try
+%$    ts1 = dseries(A,[],A_name,[]);
+%$    ts2 = dseries(B,[],B_name,[]);
+%$    ts3 = ts1^ts2;
+%$    t = 1;
+%$ catch
+%$    t = 0;
+%$ end
+%$
+%$ if t(1)
+%$    t(2) = dyn_assert(ts3.vobs,2);
+%$    t(3) = dyn_assert(ts3.nobs,10);
+%$    t(4) = dyn_assert(ts3.data,A.^B,1e-15);
+%$    t(5) = dyn_assert(ts3.name,{'power(A1,B1)';'power(A2,B2)'});
+%$    t(6) = dyn_assert(ts3.tex,{'A1^{B1}';'A2^{B2}'});
+%$ end
+%$ T = all(t);
+%@eof:1
+
+%@test:2
+%$ % Define a datasets.
+%$ A = rand(10,2);
+%$
+%$ % Define names
+%$ A_name = {'A1';'A2'};
+%$
+%$
+%$ % Instantiate a time series object.
+%$ try
+%$    ts1 = dseries(A,[],A_name,[]);
+%$    ts3 = ts1^2;
+%$    t = 1;
+%$ catch
+%$    t = 0;
+%$ end
+%$
+%$ if t(1)
+%$    t(2) = dyn_assert(ts3.vobs,2);
+%$    t(3) = dyn_assert(ts3.nobs,10);
+%$    t(4) = dyn_assert(ts3.data,A.^2,1e-15);
+%$    t(5) = dyn_assert(ts3.name,{'power(A1,2)';'power(A2,2)'});
+%$    t(6) = dyn_assert(ts3.tex,{'A1^2';'A2^2'});
+%$ end
+%$ T = all(t);
+%@eof:2
\ No newline at end of file

matlab/@dseries/plot.m

@@ -56,6 +56,7 @@ switch ndseries
         end
         hh = plot(ts.data);
     end
+    axis tight;
     id = get(gca,'XTick');
     if isequal(id(1),0)
         dates = strings([ts.dates(1)-1,ts.dates(id(2:end))]);

matlab/CheckPath.m

-function DirectoryName = CheckPath(type,dname)
+function [DirectoryName, info] = CheckPath(type,dname)
 % Creates the subfolder "./M_.dname/type" if it does not exist yet.
 %
 % INPUTS
@@ -6,12 +6,13 @@ function DirectoryName = CheckPath(type,dname)
 %    dname  [string]    Name of the directory
 %
 % OUTPUTS
-%    none.
+%    DirectoryName      string, name of the directory (with path).
+%    info               integer scalar, equal to 1 if the routine creates directory dname/type, zero otherwise.
 %
 % SPECIAL REQUIREMENTS
 %    none
 
-% Copyright (C) 2005-2012 Dynare Team
+% Copyright (C) 2005-2013 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -28,6 +29,8 @@ function DirectoryName = CheckPath(type,dname)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
+info = 0;
+
 DirectoryName = [ dname '/' type ];
 
 if ~isdir(dname)
@@ -44,4 +47,5 @@ if ~isdir(DirectoryName)
         delete(DirectoryName)
     end
     mkdir('.',DirectoryName);
+    info = 1;
 end

matlab/check.m

@@ -107,7 +107,7 @@ if options.noprint == 0
     if result
         disp('The rank condition is verified.')
     else
-        disp('The rank conditions ISN''T verified!')
+        disp('The rank condition ISN''T verified!')
     end
     skipline()
 end

matlab/check_posterior_analysis_data.m

@@ -22,16 +22,19 @@ if nargout>1
     description = '';
 end
 
-%% Get informations about mcmc files. 
-if ~exist([ M_.dname '/metropolis'],'dir')
+[MetropolisFolder, info] = CheckPath('metropolis',M_.dname);
+
+% Get informations about mcmc files.
+if info
     disp('check_posterior_analysis_data:: Can''t find any mcmc file!')
     return
 end
+
 mhname = get_name_of_the_last_mh_file(M_);
-mhdate = get_date_of_a_file(mhname);
+mhdate = get_date_of_a_file([MetropolisFolder filesep mhname]);
 
-%% Get informations about _posterior_draws files.
-drawsinfo = dir([ M_.dname '/metropolis/' M_.fname '_posterior_draws*.mat']);
+% Get informations about _posterior_draws files.
+drawsinfo = dir([ MetropolisFolder filesep M_.fname '_posterior_draws*.mat']);
 if isempty(drawsinfo)
     info = 1; % select_posterior_draws has to be called first.
     if nargout>1
@@ -40,8 +43,7 @@ if isempty(drawsinfo)
     return
 else
     number_of_last_posterior_draws_file = length(drawsinfo);
-    pddate = get_date_of_a_file([ M_.dname '/metropolis/' M_.fname '_posterior_draws'...
-                        int2str(number_of_last_posterior_draws_file) '.mat']);
+    pddate = get_date_of_a_file([ MetropolisFolder filesep M_.fname '_posterior_draws' int2str(number_of_last_posterior_draws_file) '.mat']);
     if pddate<mhdate
         info = 2; % _posterior_draws files have to be updated.
         if nargout>1
@@ -56,7 +58,7 @@ else
     end
 end
 
-%% Get informations about posterior data files.
+% Get informations about posterior data files.
 switch type
   case 'variance'
     generic_post_data_file_name = 'Posterior2ndOrderMoments';
@@ -69,7 +71,7 @@ switch type
   otherwise
     disp('This feature is not yest implemented!')
 end
-pdfinfo = dir([ M_.dname '/metropolis/' M_.fname '_' generic_post_data_file_name '*']);
+pdfinfo = dir([ MetropolisFolder filesep M_.fname '_' generic_post_data_file_name '*']);
 if isempty(pdfinfo)
     info = 4; % posterior draws have to be processed.
     if nargout>1
@@ -79,8 +81,7 @@ if isempty(pdfinfo)
 else
     number_of_the_last_post_data_file = length(pdfinfo);
     name_of_the_last_post_data_file = ...
-        [ pwd filesep M_.dname ...
-          filesep 'metropolis' filesep ...
+        [ pwd filesep MetropolisFolder filesep ...
           M_.fname '_' ... 
           generic_post_data_file_name ...
           int2str(number_of_the_last_post_data_file) ...

matlab/dynare_estimation_init.m

@@ -130,13 +130,18 @@ if ~isempty(estim_params_) && ~isempty(options_.mode_file) && ~options_.mh_poste
     number_of_estimated_parameters = length(xparam1);
     mode_file = load(options_.mode_file);
     if number_of_estimated_parameters>length(mode_file.xparam1)
+        % More estimated parameters than parameters in the mode file.
         skipline()
         disp(['The posterior mode file ' options_.mode_file ' has been generated using another specification of the model or another model!'])
         disp(['Your mode file contains estimates for ' int2str(length(mode_file.xparam1)) ' parameters, while you are attempting to estimate ' int2str(number_of_estimated_parameters) ' parameters:'])
+        md = []; xd = [];
         for i=1:number_of_estimated_parameters
             id = strmatch(deblank(bayestopt_.name(i,:)),mode_file.parameter_names,'exact');
             if isempty(id)
-                disp(['--> Estimated parameter ' bayestopt_.name{i} ' is not present in the loaded mod_file.'])
+                disp(['--> Estimated parameter ' bayestopt_.name{i} ' is not present in the loaded mode file (prior mean will be used, if possible).'])
+            else
+                xd = [xd; i];
+                md = [md; id];
             end
         end
         for i=1:length(mode_file.xparam1)
@@ -145,20 +150,30 @@ if ~isempty(estim_params_) && ~isempty(options_.mode_file) && ~options_.mh_poste
                 disp(['--> Parameter ' mode_file.parameter_names{i} ' is not estimated according to the current mod file.'])
             end
         end
-        error('Please change the mode_file option or the list of estimated parameters.')
+        if ~options_.mode_compute
+            % The posterior mode is not estimated.
+            error('Please change the mode_file option, the list of estimated parameters or set mode_compute>0.')
+        else
+            % The posterior mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean. 
+            if ~isempty(xd)
+                xparam1(xd) = mode_file.xparam1(md);
+            else
+                error('Please remove the mode_file option.')
+            end
+        end
     elseif number_of_estimated_parameters<length(mode_file.xparam1)
+        % Less estimated parameters than parameters in the mode file.
         skipline()
         disp(['The posterior mode file ' options_.mode_file ' has been generated using another specification of the model or another model!'])
         disp(['Your mode file contains estimates for ' int2str(length(mode_file.xparam1)) ' parameters, while you are attempting to estimate only ' int2str(number_of_estimated_parameters) ' parameters:'])
-        Id = [];
+        md = []; xd = [];
         for i=1:number_of_estimated_parameters
             id = strmatch(deblank(bayestopt_.name(i,:)),mode_file.parameter_names,'exact');
             if isempty(id)
-                disp(['--> Estimated parameter ' deblank(bayestopt_.name(i,:)) ' is not present in the loaded mode file.'])
-                Id = [];
-                break
+                disp(['--> Estimated parameter ' deblank(bayestopt_.name(i,:)) ' is not present in the loaded mode file (prior mean will be used, if possible).'])
             else
-                Id = [Id; id];
+                xd = [xd; i];
+                md = [md; id];
             end
         end
         for i=1:length(mode_file.xparam1)
@@ -167,59 +182,72 @@ if ~isempty(estim_params_) && ~isempty(options_.mode_file) && ~options_.mh_poste
                 disp(['--> Parameter ' mode_file.parameter_names{i} ' is not estimated according to the current mod file.'])
             end
         end
-        if isempty(Id)
-            % None of the estimated parameters are present in the mode_file.
-            error('Please change the mode_file option or the list of estimated parameters.')
-        else
-            % If possible, fix the mode_file.
-            if isequal(length(Id),number_of_estimated_parameters)
+        if ~options_.mode_compute
+            % The posterior mode is not estimated. If possible, fix the mode_file.
+            if isequal(length(xd),number_of_estimated_parameters)
                 disp('==> Fix mode file (remove unused parameters).')
-                mode_file.parameter_names = mode_file.parameter_names(Id,:);
-                mode_file.xparam1 = mode_file.xparam1(Id);
+                xparam1 = mode_file.xparam1(md);
                 if isfield(mode_file,'hh')
-                    mode_file.hh = mode_file.hh(Id,Id);
+                    hh = mode_file.hh(md,md);
                 end
+            else
+                error('Please change the mode_file option, the list of estimated parameters or set mode_compute>0.')
+            end
+        else
+            % The posterior mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean. 
+            if ~isempty(xd)
+                xparam1(xd) = mode_file.xparam1(md);
+            else
+                % None of the estimated parameters are present in the mode_file.
+                error('Please remove the mode_file option.')
             end
         end
     else
         % The number of declared estimated parameters match the number of parameters in the mode file. 
         % Check that the parameters in the mode file and according to the current mod file are identical.
         if isequal(mode_file.parameter_names, bayestopt_.name)
-            % Ok! Nothing to do here.
+            xparam1 = mode_file.xparam1;
+            if isfield(mode_file,'hh')
+                hh = mode_file.hh;
+            end
         else
             skipline()
             disp(['The posterior mode file ' options_.mode_file ' has been generated using another specification of the model or another model!'])
-            % Check if this only an ordering issue.
-            Id = [];
+            % Check if this only an ordering issue or if the missing parameters can be initialized with the prior mean.
+            md = []; xd = [];
             for i=1:number_of_estimated_parameters
                 id = strmatch(deblank(bayestopt_.name(i,:)), mode_file.parameter_names,'exact');
                 if isempty(id)
                     disp(['--> Estimated parameter ' bayestopt_.name{i} ' is not present in the loaded mode file.'])
-                    Id = [];
-                    break
                 else
-                    Id = [Id; id];
+                    xd = [xd; i];
+                    md = [md; id];
                 end
             end
-            if isempty(Id)
-                % None of the estimated parameters are present in the mode_file.
-                error('Please change the mode_file option or the list of estimated parameters.')
-            else
-                % If possible, fix the mode_file.
-                if isequal(length(Id),number_of_estimated_parameters)
-                    disp('==> Fix mode file (reorder the parameters).')
-                    mode_file.parameter_names = mode_file.parameter_names(Id,:);
-                    mode_file.xparam1 = mode_file.xparam1(Id);
+            if ~options_.mode_compute
+                % The posterior mode is not estimated
+                if isequal(length(xd), number_of_estimated_parameters)
+                    % This is an ordering issue.
+                    xparam1 = mode_file.xparam1(md);
                     if isfield(mode_file,'hh')
-                        mode_file.hh = mode_file.hh(Id,Id);
+                        hh = mode_file.hh(md,md);
                     end
+                else
+                    error('Please change the mode_file option, the list of estimated parameters or set mode_compute>0.')
                 end
+            else
+                % The posterior mode is estimated, the Hessian evaluated at the mode is not needed so we set values for the parameters missing in the mode file using the prior mean. 
+                if ~isempty(xd)
+                    xparam1(xd) = mode_file.xparam1(md);
+                    if isfield(mode_file,'hh')
+                        hh(xd,xd) = mode_file.hh(md,md);
+                    end
+                else
+                    % None of the estimated parameters are present in the mode_file.
+                    error('Please remove the mode_file option.')
                 end
             end
         end
-    xparam1 = mode_file.xparam1;
-    if isfield(mode_file,'hh')
-        hh = mode_file.hh;
     end
     skipline()
 end

matlab/dynare_identification.m

@@ -207,15 +207,23 @@ else
     name_tex = [cellstr(M_.exo_names_tex); cellstr(M_.param_names_tex)];
 end
 
+skipline()
+disp(['==== Identification analysis ====' ]),
+skipline()
+if nparam<2,
+    options_ident.advanced=0;
+    advanced = options_ident.advanced;
+    disp('There is only one parameter to study for identitification.')
+    disp('The advanced option is re-set to 0.')
+    skipline()
+end
+
 options_ident = set_default_option(options_ident,'max_dim_cova_group',min([2,nparam-1]));
 options_ident.max_dim_cova_group = min([options_ident.max_dim_cova_group,nparam-1]);
 
 
 MaxNumberOfBytes=options_.MaxNumberOfBytes;
 store_options_ident = options_ident;
-skipline()
-disp(['==== Identification analysis ====' ]),
-skipline()
 
 if iload <=0,
     
@@ -326,6 +334,8 @@ if iload <=0,
             disp('----------- ')
             skipline()
             return
+        else
+            parameters = 'Random_prior_params';
         end
     else
     idehess_point.params=params;
@@ -336,6 +346,7 @@ if iload <=0,
 %     normJ = max(abs(siJ)')';
 %     normLRE = max(abs(siLRE)')';
     save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idehess_point', 'idemoments_point','idemodel_point', 'idelre_point','store_options_ident')
+    save([IdentifDirectoryName '/' M_.fname '_' parameters '_identif.mat'], 'idehess_point', 'idemoments_point','idemodel_point', 'idelre_point','store_options_ident')
     disp_identification(params, idemodel_point, idemoments_point, name, advanced);
     if ~options_.nograph,
         plot_identification(params,idemoments_point,idehess_point,idemodel_point,idelre_point,advanced,parameters,name,IdentifDirectoryName);

matlab/dynare_sensitivity.m

@@ -281,10 +281,12 @@ if options_gsa.redform && ~isempty(options_gsa.namendo),% ...
         % check existence of the SS_ANOVA toolbox
         if isempty(options_gsa.threshold_redform) && ...
          ~(exist('gsa_sdp','file')==6 || exist('gsa_sdp','file')==2),
-            disp('Download Mapping routines at:')
-            disp('http://eemc.jrc.ec.europa.eu/softwareDYNARE-Dowload.htm')
-            disp(' ' )
-            error('Mapping routines missing!')
+            fprintf('\nThe "SS-ANOVA-R: MATLAB Toolbox for the estimation of Smoothing Spline ANOVA models with Recursive algorithms" is missing.\n')
+            fprintf('To obtain it, go to:\n\n')
+            fprintf('http://ipsc.jrc.ec.europa.eu/?id=790 \n\n')
+            fprintf('and follow the instructions there.\n')
+            fprintf('After obtaining the files, you need to unpack them and set a Matlab Path to those files.\n')
+            error('SS-ANOVA-R Toolbox missing!')
         end
 
         redform_map(OutputDirectoryName,options_gsa);