[WIP] Add Sequential Monte Carlo samplers.

Name of the parameter.

.clang-tidy

@@ -0,0 +1,9 @@
+# NB: to use clang-tidy on the MEX source code, make sure that you have
+# libomp-dev installed (the LLVM implementation of OpenMP)
+
+# TODO: add the following check families:
+# - performance-*
+# - bugprone-*
+# - cppcoreguidelines-
+
+Checks: 'modernize-*,-modernize-use-trailing-return-type,-clang-diagnostic-unqualified-std-cast-call'

README.md

@@ -479,7 +479,7 @@ If you want a certain version (e.g. 5.x) , then add `--single-branch --branch 5.
 ```sh
 export BUILDDIR=build-matlab
 export MATLABPATH=/Applications/MATLAB_R2023b.app
-arch -$ARCH meson setup --native-file scripts/homebrew-native-$ARCH.ini -Dmatlab_path=$MATLABPATH -Dbuildtype=debugoptimized -Dfortran_args="['-B','$DYNAREDIR/slicot/lib']" $BUILDDIR
+arch -$ARCH meson setup --native-file macOS/homebrew-native-$ARCH.ini -Dmatlab_path=$MATLABPATH -Dbuildtype=debugoptimized -Dfortran_args="['-B','$DYNAREDIR/slicot/lib']" $BUILDDIR
 ```
 where you need to adapt the path to MATLAB.
 Similarly, if you want to compile for Octave, replace the `-Dmatlab_path` option by `-Dbuild_for=octave`, and change the build directory to `build-octave`.
@@ -514,4 +514,4 @@ e.g. by adding this to your mod file. Alternatively, you can create a `startup.m
 ## Docker
 We offer a variety of pre-configured Docker containers for Dynare, pre-configured with Octave and MATLAB including all recommended toolboxes.
 These are readily available for your convenience on [Docker Hub](https://hub.docker.com/r/dynare/dynare).
-The docker folder contains [information and instructions](docker/README.md) to interact, built and customize the containers.
+The `scripts/docker` folder contains [information and instructions](scripts/docker/README.md) to interact, built and customize the containers.

doc/manual/source/the-configuration-file.rst

@@ -15,11 +15,16 @@ related to the model (and hence not placed in the model file). At the
 moment, it is only used when using Dynare to run parallel
 computations.
 
-On Linux and macOS, the default location of the configuration file is
-``$HOME/.dynare``, while on Windows it is ``%APPDATA%\dynare.ini``
-(typically ``c:\Users\USERNAME\AppData\dynare.ini``). You
-can specify a non standard location using the ``conffile`` option of
-the ``dynare`` command (see :ref:`dyn-invoc`).
+On Linux and macOS, the configuration file is searched by default under
+``dynare/dynare.ini`` in the configuration directories defined by the XDG
+specification (typically ``$HOME/.config/dynare/dynare.ini`` for the
+user-specific configuration and ``/etc/xdg/dynare/dynare.ini`` for the
+system-wide configuration, the former having precedence over the latter). Under
+Windows, the configuration file is searched by default in
+``%APPDATA%\dynare\dynare.ini`` (typically
+``c:\Users\USERNAME\AppData\Roaming\dynare\dynare.ini``). You can specify a non
+standard location using the ``conffile`` option of the ``dynare`` command (see
+:ref:`dyn-invoc`).
 
 The parsing of the configuration file is case-sensitive and it should
 take the following form, with each option/choice pair placed on a
@@ -76,8 +81,15 @@ processing. Currently, there is only one option available.
 
     .. option:: GlobalInitFile = PATH_AND_FILE
 
-        The location of the global initialization file to be run at
-        the end of ``global_initialization.m``.
+        The location of a global initialization file that can be used to
+        customize some Dynare internals (typically default option values). This
+        is a MATLAB/Octave script.
+
+        If this option is not specified, Dynare will look for a
+        ``global_init.m`` file in its configuration directory (typically
+        ``$HOME/.config/dynare/global_init.m`` under Linux and macOS, and
+        ``c:\Users\USERNAME\AppData\Roaming\dynare\global_init.m`` under
+        Windows).
 
     *Example*
 

doc/manual/source/the-model-file.rst

@@ -4787,31 +4787,6 @@ Computing the stochastic solution
        and this option has no effect. More references can be found
        `here <https://archives.dynare.org/DynareWiki/PartialInformation>`__ .
 
-    .. option:: sylvester = OPTION
-
-       Determines the algorithm used to solve the Sylvester equation
-       for block decomposed model. Possible values for OPTION are:
-
-           ``default``
-
-                Uses the default solver for Sylvester equations
-                (``gensylv``) based on Ondra Kamenik’s algorithm (see
-                `here
-                <https://www.dynare.org/assets/team-presentations/sylvester.pdf>`__
-                for more information).
-
-           ``fixed_point``
-
-                Uses a fixed point algorithm to solve the Sylvester
-                equation (``gensylv_fp``). This method is faster than
-                the default one for large scale models.
-
-       |br| Default value is ``default``.
-
-    .. option:: sylvester_fixed_point_tol = DOUBLE
-
-       The convergence criterion used in the fixed point
-       Sylvester solver. Its default value is ``1e-12``.
 
     .. option:: dr = OPTION
 
@@ -7479,6 +7454,18 @@ observed variables.
                Chain draws than the MH-algorithm. Its relative (in)efficiency can be investigated via 
                the reported inefficiency factors.
 
+               ``'hssmc'``
+
+               Instructs Dynare to use the *Herbst and Schorfheide (2014)*
+               version of the Sequential Monte-Carlo sampler instead of the
+               standard Random-Walk Metropolis-Hastings.
+
+               ``'dsmh'``
+
+               Instructs Dynare to use the Dynamic Striated Metropolis Hastings
+               sampler proposed by *Waggoner, Wu and Zha (2016)* instead of the
+               standard Random-Walk Metropolis-Hastings.
+
     .. option:: posterior_sampler_options = (NAME, VALUE, ...)
 
        A list of NAME and VALUE pairs. Can be used to set options for
@@ -7923,15 +7910,6 @@ observed variables.
 
        See :opt:`aim_solver`.
 
-    .. option:: sylvester = OPTION
-
-       See :opt:`sylvester <sylvester = OPTION>`.
-
-    .. option:: sylvester_fixed_point_tol = DOUBLE
-
-       See :opt:`sylvester_fixed_point_tol <sylvester_fixed_point_tol
-       = DOUBLE>` .
-
     .. option:: lyapunov = OPTION
 
        Determines the algorithm used to solve the Lyapunov equation to
@@ -9475,16 +9453,6 @@ adding prior information comes at the cost of a loss in efficiency of the estima
         See :opt:`lyapunov_doubling_tol <lyapunov_doubling_tol = DOUBLE>`.
         Default: ``1e-16``.
 
-    .. option:: sylvester = OPTION
-
-        See :opt:`sylvester <sylvester = OPTION>`.
-        Default: ``default``, i.e. uses ``gensylv``.
-
-    .. option:: sylvester_fixed_point_tol = DOUBLE
-
-        See :opt:`sylvester_fixed_point_tol <sylvester_fixed_point_tol = DOUBLE>`.
-        Default: ``1e-12``.
-
     .. option:: qz_criterium = DOUBLE
 
         See :opt:`qz_criterium <qz_criterium = DOUBLE>`.

examples/dynare++/README.txt

@@ -1,6 +0,0 @@
-Assuming that the dynare++ binary is in your PATH, you can run the example by using the following command
-in a Command Prompt Window:
-
-... > dynare++ example1.mod
-
-Please, read the manual (doc\dynare++\dynare++-tutorial.pdf) for a description of the generated output.

examples/dynare++/example1.mod

@@ -1,45 +0,0 @@
-/*
- * This Dynare++ mod-file implements the RBC model with time-to-build
- * described in Kamenik (2011): "DSGE Models with Dynare++. A Tutorial."
- * Note that Dynare++ uses the same stock-at-the-end-of-period timing convention
- * as the regular Dynare
-*/
-
-var Y, C, K, A, H, B;
-
-varexo EPS, NU;
-
-parameters beta, rho, alpha, delta, theta, psi, tau;
-
-alpha = 0.36;
-rho = 0.95;
-tau = 0.025;
-beta = 1/(1.03^0.25);
-delta = 0.025;
-psi = 0;
-theta = 2.95;
-
-model;
-  C*theta*H^(1+psi) = (1-alpha)*Y;
-  beta*exp(B)*C/exp(B(1))/C(1)*
-  (exp(B(1))*alpha*Y(1)/K(1)+1-delta) = 1;
-  Y = exp(A)*K^alpha*H^(1-alpha);
-  K = exp(B(-1))*(Y(-1)-C(-1)) + (1-delta)*K(-1);
-  A = rho*A(-1) + tau*B(-1) + EPS;
-  B = tau*A(-1) + rho*B(-1) + NU;
-end;
-
-initval;
-  A = 0;
-  B = 0;
-  H = ((1-alpha)/(theta*(1-(delta*alpha)/(1/beta-1+delta))))^(1/(1+psi));
-  Y = (alpha/(1/beta-1+delta))^(alpha/(1-alpha))*H;
-  K = alpha/(1/beta-1+delta)*Y;
-  C = Y - delta*K;
-end;
-
-vcov = [0.0002 0.00005;
-0.00005 0.0001
-];
-
-order = 7;

macOS/build.sh

@@ -54,7 +54,7 @@ LIB64="$ROOTDIR"/macOS/deps/"$PKG_ARCH"/lib64
 ## - the macOS linker is different from GNU ld and does not have the equivalent of -Bstatic/-Bdynamic
 ## - libgfortran.spec does not include --as-needed on macOS, hence it will link the library anyways
 ## Also, it does not seem possible to override libgfortran.spec with the --specs option.
-GCC_VERSION=$(sed -En "/^c[[:space:]]*=/s/c[[:space:]]*=[[:space:]]*'.*gcc-([0-9]+)'/\1/p" "$ROOTDIR"/scripts/homebrew-native-"$PKG_ARCH".ini)
+GCC_VERSION=$(sed -En "/^c[[:space:]]*=/s/c[[:space:]]*=[[:space:]]*'.*gcc-([0-9]+)'/\1/p" "$ROOTDIR"/macOS/homebrew-native-"$PKG_ARCH".ini)
 
 QUADMATH_DIR=$(mktemp -d)
 ln -s "$BREWDIR"/opt/gcc/lib/gcc/"$GCC_VERSION"/libquadmath.a "$QUADMATH_DIR"
@@ -67,7 +67,7 @@ cd "$ROOTDIR"
 # NB: the addition of -Wl,-ld_classic is a workaround for https://github.com/mesonbuild/meson/issues/12282 (see also the native file)
 common_meson_opts=(-Dbuild_for=matlab -Dbuildtype=release -Dprefer_static=true -Dfortran_args="[ '-B', '$LIB64/Slicot/' ]" \
                    -Dc_link_args="[ '-Wl,-ld_classic', '-L$QUADMATH_DIR' ]" -Dcpp_link_args="[ '-Wl,-ld_classic', '-L$QUADMATH_DIR' ]" -Dfortran_link_args="[ '-Wl,-ld_classic', '-L$QUADMATH_DIR' ]" \
-                   --native-file scripts/homebrew-native-$PKG_ARCH.ini)
+                   --native-file macOS/homebrew-native-$PKG_ARCH.ini)
 
 # Build for MATLAB ⩾ R2018b (x86_64) and MATLAB ⩾ R2023b (arm64)
 arch -"$PKG_ARCH" meson setup "${common_meson_opts[@]}" -Dmatlab_path="$MATLAB_PATH" build-matlab --wipe

macOS/deps/Makefile

@@ -22,7 +22,7 @@ DEPS_ARCH ?= x86_64 # use x86_64 by default
 
 BREWDIR := $(if $(filter arm64,$(DEPS_ARCH)),/opt/homebrew,/usr/local)
 
-GCC_VERSION = $(shell sed -En "/^c[[:space:]]*=/s/c[[:space:]]*=[[:space:]]*'.*gcc-([0-9]+)'/\1/p" ../../scripts/homebrew-native-$(DEPS_ARCH).ini)
+GCC_VERSION = $(shell sed -En "/^c[[:space:]]*=/s/c[[:space:]]*=[[:space:]]*'.*gcc-([0-9]+)'/\1/p" ../homebrew-native-$(DEPS_ARCH).ini)
 
 ROOT_PATH = $(realpath .)
 

matlab/+mom/default_option_mom_values.m

@@ -160,8 +160,6 @@ options_mom_ = set_default_option(options_mom_,'lyapunov_srs',false);
 options_mom_ = set_default_option(options_mom_,'lyapunov_complex_threshold',1e-15);     % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
 options_mom_ = set_default_option(options_mom_,'lyapunov_fixed_point_tol',1e-10);       % convergence criterion used in the fixed point Lyapunov solver
 options_mom_ = set_default_option(options_mom_,'lyapunov_doubling_tol',1e-16);          % convergence criterion used in the doubling algorithm
-options_mom_ = set_default_option(options_mom_,'sylvester_fp',false);                   % determines whether to use fixed point algorihtm to solve Sylvester equation (gensylv_fp), faster for large scale models
-options_mom_ = set_default_option(options_mom_,'sylvester_fixed_point_tol',1e-12);      % convergence criterion used in the fixed point Sylvester solver
 
 % mode check plot
 options_mom_.mode_check.nolik = false;                                                          % we don't do likelihood (also this initializes mode_check substructure)

matlab/@dprior/admissible.m

@@ -0,0 +1,146 @@
+function b = admissible(o, d)
+
+% Return true iff d is an admissible draw in a distribution characterized by o.
+%
+% INPUTS
+% - o      [dprior]   Distribution specification for a n×1 vector of independent continuous random variables
+% - d      [double]   n×1 vector.
+%
+% OUTPUTS
+% - b      [logical]  scalar.
+%
+% REMARKS
+% None.
+%
+% EXAMPLE
+%
+% >> Prior = dprior(bayestopt_, options_.prior_trunc);
+% >> d = Prior.draw()
+% >> Prior.admissible(d)
+%    ans =
+%
+%   logical
+%
+%   1
+
+% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
+
+b = false;
+
+if ~isequal(length(d), length(o.lb))
+    return
+end
+
+if all(d>=o.lb & d<=o.ub)
+    b = true;
+end
+
+return % --*-- Unit tests --*--
+
+%@test:1
+% Fill global structures with required fields...
+prior_trunc = 1e-10;
+p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1);    % Prior shape
+p1 = .4*ones(14,1);                          % Prior mean
+p2 = .2*ones(14,1);                          % Prior std.
+p3 = NaN(14,1);
+p4 = NaN(14,1);
+p5 = NaN(14,1);
+p6 = NaN(14,1);
+p7 = NaN(14,1);
+
+for i=1:14
+    switch p0(i)
+      case 1
+        % Beta distribution
+        p3(i) = 0;
+        p4(i) = 1;
+        [p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
+      case 2
+        % Gamma distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
+      case 3
+        % Normal distribution
+        p3(i) = -Inf;
+        p4(i) = Inf;
+        p6(i) = p1(i);
+        p7(i) = p2(i);
+        p5(i) = p1(i);
+      case 4
+        % Inverse Gamma (type I) distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
+      case 5
+        % Uniform distribution
+        [p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
+        p3(i) = p6(i);
+        p4(i) = p7(i);
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
+      case 6
+        % Inverse Gamma (type II) distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
+      case 8
+        % Weibull distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
+      otherwise
+        error('This density is not implemented!')
+    end
+end
+
+BayesInfo.pshape = p0;
+BayesInfo.p1 = p1;
+BayesInfo.p2 = p2;
+BayesInfo.p3 = p3;
+BayesInfo.p4 = p4;
+BayesInfo.p5 = p5;
+BayesInfo.p6 = p6;
+BayesInfo.p7 = p7;
+
+ndraws = 10;
+
+% Call the tested routine
+try
+    % Instantiate dprior object
+    o = dprior(BayesInfo, prior_trunc, false);
+    % Do simulations in a loop and estimate recursively the mean and the variance.
+    for i = 1:ndraws
+        draw = o.draw();
+        if ~o.admissible(draw)
+            error()
+        end
+    end
+    t(1) = true;
+catch
+    t(1) = false;
+end
+
+T = all(t);
+
+%@eof:1

matlab/@dprior/densities.m

@@ -1,7 +1,15 @@
-function [tlogpostkern,loglik] = tempered_likelihood(TargetFun,xparam1,lambda,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_)
-% function [tlogpostkern,loglik] = tempered_likelihood(TargetFun,xparam1,lambda,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_)
+function lpd = densities(o, X)
 
-% Copyright © 2022 Dynare Team
+% Evaluate the logged prior densities at X (for each column).
+%
+% INPUTS
+% - o       [dprior]
+% - X       [double]   m×n matrix, n points where the prior density is evaluated.
+%
+% OUTPUTS
+% - lpd     [double]   1×n, values of the logged prior density at X.
+
+% Copyright © 2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -18,7 +26,10 @@ function [tlogpostkern,loglik] = tempered_likelihood(TargetFun,xparam1,lambda,da
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
-    logpostkern = -feval(TargetFun,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
-    logprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
-    loglik = logpostkern-logprior ;
-    tlogpostkern = lambda*loglik + logprior;
+n = columns(X);
+
+lpd = NaN(1, n);
+
+parfor i=1:n
+    lpd(i) = density(o, X(:,i));
+end

matlab/@dprior/density.m

@@ -0,0 +1,384 @@
+function [lpd, dlpd, d2lpd, info] = density(o, x)
+
+% Evaluate the logged prior density at x.
+%
+% INPUTS
+% - o       [dprior]
+% - x       [double]   m×1 vector, point where the prior density is evaluated.
+%
+% OUTPUTS
+% - lpd     [double]   scalar, value of the logged prior density at x.
+% - dlpd    [double]   m×1 vector, first order derivatives.
+% - d2lpd   [double]   m×1 vector, second order derivatives.
+%
+% REMARKS
+% Second order derivatives holder, d2lpd, has the same rank and shape than dlpd because the priors are
+% independent (we would have to use a matrix if non orthogonal priors were allowed in Dynare).
+%
+% EXAMPLE
+%
+% >> Prior = dprior(bayestopt_, options_.prior_trunc);
+% >> lpd = Prior.dsensity(x)
+
+% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
+
+lpd = 0.0;
+if nargout>1
+    dlpd = zeros(1, length(x));
+    if nargout>2
+        d2lpd = dlpd;
+        if nargout>3
+            info = [];
+        end
+    end
+end
+if o.isuniform
+    if any(x(o.iduniform)-o.p3(o.iduniform)<0) || any(x(o.iduniform)-o.p4(o.iduniform)>0)
+        lpd = -Inf ;
+        if nargout==4
+            info = o.iduniform((x(o.iduniform)-o.p3(o.iduniform)<0) || (x(o.iduniform)-o.p4(o.iduniform)>0));
+        end
+        return
+    end
+    lpd = lpd - sum(log(o.p4(o.iduniform)-o.p3(o.iduniform))) ;
+    if nargout>1
+        dlpd(o.iduniform) = zeros(length(o.iduniform), 1);
+        if nargout>2
+            d2lpd(o.iduniform) = zeros(length(o.iduniform), 1);
+        end
+    end
+end
+if o.isgaussian
+    switch nargout
+      case 1
+        lpd = lpd + sum(lpdfnorm(x(o.idgaussian), o.p6(o.idgaussian), o.p7(o.idgaussian)));
+      case 2
+        [tmp, dlpd(o.idgaussian)] = lpdfnorm(x(o.idgaussian), o.p6(o.idgaussian), o.p7(o.idgaussian));
+        lpd = lpd + sum(tmp);
+      case {3,4}
+        [tmp, dlpd(o.idgaussian), d2lpd(o.idgaussian)] = lpdfnorm(x(o.idgaussian), o.p6(o.idgaussian), o.p7(o.idgaussian));
+        lpd = lpd + sum(tmp);
+    end
+end
+if o.isgamma
+    switch nargout
+      case 1
+        lpd = lpd + sum(lpdfgam(x(o.idgamma)-o.p3(o.idgamma), o.p6(o.idgamma), o.p7(o.idgamma)));
+        if isinf(lpd), return, end
+      case 2
+        [tmp, dlpd(o.idgamma)] = lpdfgam(x(o.idgamma)-o.p3(o.idgamma), o.p6(o.idgamma), o.p7(o.idgamma));
+        lpd = lpd + sum(tmp);
+        if isinf(lpd), return, end
+      case 3
+        [tmp, dlpd(o.idgamma), d2lpd(o.idgamma)] = lpdfgam(x(o.idgamma)-o.p3(o.idgamma), o.p6(o.idgamma), o.p7(o.idgamma));
+        lpd = lpd + sum(tmp);
+        if isinf(lpd), return, end
+      case 4
+        [tmp, dlpd(o.idgamma), d2lpd(o.idgamma)] = lpdfgam(x(o.idgamma)-o.p3(o.idgamma), o.p6(o.idgamma), o.p7(o.idgamma));
+        lpd = lpd + sum(tmp);
+        if isinf(lpd)
+            info = o.idgamma(isinf(tmp));
+            return
+        end
+    end
+end
+if o.isbeta
+    switch nargout
+      case 1
+        lpd = lpd + sum(lpdfgbeta(x(o.idbeta), o.p6(o.idbeta), o.p7(o.idbeta), o.p3(o.idbeta), o.p4(o.idbeta)));
+        if isinf(lpd), return, end
+      case 2
+        [tmp, dlpd(o.idbeta)] = lpdfgbeta(x(o.idbeta), o.p6(o.idbeta), o.p7(o.idbeta), o.p3(o.idbeta), o.p4(o.idbeta));
+        lpd = lpd + sum(tmp);
+        if isinf(lpd), return, end
+      case 3
+        [tmp, dlpd(o.idbeta), d2lpd(o.idbeta)] = lpdfgbeta(x(o.idbeta), o.p6(o.idbeta), o.p7(o.idbeta), o.p3(o.idbeta), o.p4(o.idbeta));
+        lpd = lpd + sum(tmp);
+        if isinf(lpd), return, end
+      case 4
+        [tmp, dlpd(o.idbeta), d2lpd(o.idbeta)] = lpdfgbeta(x(o.idbeta), o.p6(o.idbeta), o.p7(o.idbeta), o.p3(o.idbeta), o.p4(o.idbeta));
+        lpd = lpd + sum(tmp);
+        if isinf(lpd)
+            info = o.idbeta(isinf(tmp));
+            return
+        end
+    end
+end
+if o.isinvgamma1
+    switch nargout
+      case 1
+        lpd = lpd + sum(lpdfig1(x(o.idinvgamma1)-o.p3(o.idinvgamma1), o.p6(o.idinvgamma1), o.p7(o.idinvgamma1)));
+        if isinf(lpd), return, end
+      case 2
+        [tmp, dlpd(o.idinvgamma1)] = lpdfig1(x(o.idinvgamma1)-o.p3(o.idinvgamma1), o.p6(o.idinvgamma1), o.p7(o.idinvgamma1));
+        lpd = lpd + sum(tmp);
+        if isinf(lpd), return, end
+      case 3
+        [tmp, dlpd(o.idinvgamma1), d2lpd(o.idinvgamma1)] = lpdfig1(x(o.idinvgamma1)-o.p3(o.idinvgamma1), o.p6(o.idinvgamma1), o.p7(o.idinvgamma1));
+        lpd = lpd + sum(tmp);
+        if isinf(lpd), return, end
+      case 4
+        [tmp, dlpd(o.idinvgamma1), d2lpd(o.idinvgamma1)] = lpdfig1(x(o.idinvgamma1)-o.p3(o.idinvgamma1), o.p6(o.idinvgamma1), o.p7(o.idinvgamma1));
+        lpd = lpd + sum(tmp);
+        if isinf(lpd)
+            info = o.idinvgamma1(isinf(tmp));
+            return
+        end
+    end
+end
+if o.isinvgamma2
+    switch nargout
+      case 1
+        lpd = lpd + sum(lpdfig2(x(o.idinvgamma2)-o.p3(o.idinvgamma2), o.p6(o.idinvgamma2), o.p7(o.idinvgamma2)));
+        if isinf(lpd), return, end
+      case 2
+        [tmp, dlpd(o.idinvgamma2)] = lpdfig2(x(o.idinvgamma2)-o.p3(o.idinvgamma2), o.p6(o.idinvgamma2), o.p7(o.idinvgamma2));
+        lpd = lpd + sum(tmp);
+        if isinf(lpd), return, end
+      case 3
+        [tmp, dlpd(o.idinvgamma2), d2lpd(o.idinvgamma2)] = lpdfig2(x(o.idinvgamma2)-o.p3(o.idinvgamma2), o.p6(o.idinvgamma2), o.p7(o.idinvgamma2));
+        lpd = lpd + sum(tmp);
+        if isinf(lpd), return, end
+      case 4
+        [tmp, dlpd(o.idinvgamma2), d2lpd(o.idinvgamma2)] = lpdfig2(x(o.idinvgamma2)-o.p3(o.idinvgamma2), o.p6(o.idinvgamma2), o.p7(o.idinvgamma2));
+        lpd = lpd + sum(tmp);
+        if isinf(lpd)
+            info = o.idinvgamma2(isinf(tmp));
+            return
+        end
+    end
+end
+if o.isweibull
+    switch nargout
+      case 1
+        lpd = lpd + sum(lpdfgweibull(x(o.idweibull), o.p6(o.idweibull), o.p7(o.idweibull)));
+        if isinf(lpd), return, end
+      case 2
+        [tmp, dlpd(o.idweibull)] = lpdfgweibull(x(o.idweibull), o.p6(o.idweibull), o.p7(o.idweibull));
+        lpd = lpd + sum(tmp);
+        if isinf(lpd), return, end
+      case 3
+        [tmp, dlpd(o.idweibull), d2lpd(o.idweibull)] = lpdfgweibull(x(o.idweibull), o.p6(o.idweibull), o.p7(o.idweibull));
+        lpd = lpd + sum(tmp);
+        if isinf(lpd), return, end
+      case 4
+        [tmp, dlpd(o.idweibull), d2lpd(o.idweibull)] = lpdfgweibull(x(o.idweibull), o.p6(o.idweibull), o.p7(o.idweibull));
+        lpd = lpd + sum(tmp);
+        if isinf(lpd)
+            info = o.idweibull(isinf(tmp));
+            return
+        end
+    end
+end
+
+return % --*-- Unit tests --*--
+
+%@test:1
+% Fill global structures with required fields...
+prior_trunc = 1e-10;
+p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1);    % Prior shape
+p1 = .4*ones(14,1);                          % Prior mean
+p2 = .2*ones(14,1);                          % Prior std.
+p3 = NaN(14,1);
+p4 = NaN(14,1);
+p5 = NaN(14,1);
+p6 = NaN(14,1);
+p7 = NaN(14,1);
+
+for i=1:14
+    switch p0(i)
+      case 1
+        % Beta distribution
+        p3(i) = 0;
+        p4(i) = 1;
+        [p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
+      case 2
+        % Gamma distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
+      case 3
+        % Normal distribution
+        p3(i) = -Inf;
+        p4(i) = Inf;
+        p6(i) = p1(i);
+        p7(i) = p2(i);
+        p5(i) = p1(i);
+      case 4
+        % Inverse Gamma (type I) distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
+      case 5
+        % Uniform distribution
+        [p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
+        p3(i) = p6(i);
+        p4(i) = p7(i);
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
+      case 6
+        % Inverse Gamma (type II) distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
+      case 8
+        % Weibull distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
+      otherwise
+        error('This density is not implemented!')
+    end
+end
+
+BayesInfo.pshape = p0;
+BayesInfo.p1 = p1;
+BayesInfo.p2 = p2;
+BayesInfo.p3 = p3;
+BayesInfo.p4 = p4;
+BayesInfo.p5 = p5;
+BayesInfo.p6 = p6;
+BayesInfo.p7 = p7;
+
+% Call the tested routine
+try
+    Prior = dprior(BayesInfo, prior_trunc, false);
+
+    % Compute density at the prior mode
+    lpdstar = Prior.density(p5);
+
+    % Draw random deviates in a loop and evaluate the density.
+    LPD = NaN(10000,1);
+    parfor i = 1:10000
+        x = Prior.draw();
+        LPD(i) = Prior.density(x);
+    end
+    t(1) = true;
+catch
+    t(1) = false;
+end
+
+if t(1)
+    t(2) = all(LPD<=lpdstar);
+end
+T = all(t);
+%@eof:1
+
+%@test:2
+% Fill global structures with required fields...
+prior_trunc = 1e-10;
+p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1);    % Prior shape
+p1 = .4*ones(14,1);                          % Prior mean
+p2 = .2*ones(14,1);                          % Prior std.
+p3 = NaN(14,1);
+p4 = NaN(14,1);
+p5 = NaN(14,1);
+p6 = NaN(14,1);
+p7 = NaN(14,1);
+
+for i=1:14
+    switch p0(i)
+      case 1
+        % Beta distribution
+        p3(i) = 0;
+        p4(i) = 1;
+        [p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
+      case 2
+        % Gamma distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
+      case 3
+        % Normal distribution
+        p3(i) = -Inf;
+        p4(i) = Inf;
+        p6(i) = p1(i);
+        p7(i) = p2(i);
+        p5(i) = p1(i);
+      case 4
+        % Inverse Gamma (type I) distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
+      case 5
+        % Uniform distribution
+        [p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
+        p3(i) = p6(i);
+        p4(i) = p7(i);
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
+      case 6
+        % Inverse Gamma (type II) distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
+      case 8
+        % Weibull distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
+      otherwise
+        error('This density is not implemented!')
+    end
+end
+
+BayesInfo.pshape = p0;
+BayesInfo.p1 = p1;
+BayesInfo.p2 = p2;
+BayesInfo.p3 = p3;
+BayesInfo.p4 = p4;
+BayesInfo.p5 = p5;
+BayesInfo.p6 = p6;
+BayesInfo.p7 = p7;
+
+% Call the tested routine
+try
+    Prior = dprior(BayesInfo, prior_trunc, false);
+    mu = NaN(14,1);
+    std = NaN(14,1);
+
+    for i=1:14
+        % Define conditional density (it's also a marginal since priors are orthogonal)
+        f = @(x) exp(Prior.densities(substitute(p5, i, x)));
+        % TODO: Check the version of Octave we use (integral is available as a compatibility wrapper in latest Octave version)
+        m = integral(f, p3(i), p4(i));
+        density = @(x) f(x)/m; % rescaling is required since the probability mass depends on the conditioning.
+        % Compute the conditional expectation
+        mu(i) = integral(@(x) x.*density(x), p3(i), p4(i));
+        std(i) = sqrt(integral(@(x) ((x-mu(i)).^2).*density(x), p3(i), p4(i)));
+    end
+
+    t(1) = true;
+catch
+    t(1) = false;
+end
+
+if t(1)
+    t(2) = all(abs(mu-.4)<1e-6);
+    t(3) = all(abs(std-.2)<1e-6);
+end
+T = all(t);
+%@eof:2

matlab/@dprior/dprior.m

@@ -1,26 +1,48 @@
-classdef dprior
+classdef dprior < handle
+
+% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
 
     properties
-        p6 = [];                         % Prior first hyperparameter.
-        p7 = [];                         % Prior second hyperparameter.
+        p1 = [];                         % Prior mean.
+        p2 = [];                         % Prior stddev.
         p3 = [];                         % Lower bound of the prior support.
         p4 = [];                         % Upper bound of the prior support.
+        p5 = [];                         % Prior mode.
+        p6 = [];                         % Prior first hyperparameter.
+        p7 = [];                         % Prior second hyperparameter.
+        p11 = [];                        % Prior median
         lb = [];                         % Truncated prior lower bound.
         ub = [];                         % Truncated prior upper bound.
-        uniform_index = [];              % Index for the uniform priors.
-        gaussian_index = [];             % Index for the gaussian priors.
-        gamma_index = [];                % Index for the gamma priors.
-        beta_index = [];                 % Index for the beta priors.
-        inverse_gamma_1_index = [];      % Index for the inverse gamma type 1 priors.
-        inverse_gamma_2_index = [];      % Index for the inverse gamma type 2 priors.
-        weibull_index = [];              % Index for the weibull priors.
-        uniform_draws = false;
-        gaussian_draws = false;
-        gamma_draws = false;
-        beta_draws = false;
-        inverse_gamma_1_draws = false;
-        inverse_gamma_2_draws = false;
-        weibull_draws = false;
+        name = {};                       % Name of the parameter
+        iduniform = [];                  % Index for the uniform priors.
+        idgaussian = [];                 % Index for the gaussian priors.
+        idgamma = [];                    % Index for the gamma priors.
+        idbeta = [];                     % Index for the beta priors.
+        idinvgamma1 = [];                % Index for the inverse gamma type 1 priors.
+        idinvgamma2 = [];                % Index for the inverse gamma type 2 priors.
+        idweibull = [];                  % Index for the weibull priors.
+        isuniform = false;
+        isgaussian = false;
+        isgamma = false;
+        isbeta = false;
+        isinvgamma1 = false;
+        isinvgamma2 = false;
+        isweibull = false;
     end
 
     methods
@@ -38,10 +60,17 @@ classdef dprior
         %
         % REQUIREMENTS
         % None.
-            o.p6 = bayestopt_.p6;
-            o.p7 = bayestopt_.p7;
-            o.p3 = bayestopt_.p3;
-            o.p4 = bayestopt_.p4;
+            if ~nargin % Empty object
+                return
+            end
+            if isfield(bayestopt_, 'p1'), o.p1 = bayestopt_.p1; end
+            if isfield(bayestopt_, 'p2'), o.p2 = bayestopt_.p2; end
+            if isfield(bayestopt_, 'p3'), o.p3 = bayestopt_.p3; end
+            if isfield(bayestopt_, 'p4'), o.p4 = bayestopt_.p4; end
+            if isfield(bayestopt_, 'p5'), o.p5 = bayestopt_.p5; end
+            if isfield(bayestopt_, 'p6'), o.p6 = bayestopt_.p6; end
+            if isfield(bayestopt_, 'p7'), o.p7 = bayestopt_.p7; end
+            if isfield(bayestopt_, 'p11'), o.p11 = bayestopt_.p11; end
             bounds = prior_bounds(bayestopt_, PriorTrunc);
             o.lb = bounds.lb;
             o.ub = bounds.ub;
@@ -50,138 +79,38 @@ classdef dprior
             else
                 prior_shape = bayestopt_.pshape;
             end
-            o.beta_index = find(prior_shape==1);
-            if ~isempty(o.beta_index)
-                o.beta_draws = true;
+            o.idbeta = find(prior_shape==1);
+            if ~isempty(o.idbeta)
+                o.isbeta = true;
             end
-            o.gamma_index = find(prior_shape==2);
-            if ~isempty(o.gamma_index)
-                o.gamma_draws = true;
+            o.idgamma = find(prior_shape==2);
+            if ~isempty(o.idgamma)
+                o.isgamma = true;
             end
-            o.gaussian_index = find(prior_shape==3);
-            if ~isempty(o.gaussian_index)
-                o.gaussian_draws = true;
+            o.idgaussian = find(prior_shape==3);
+            if ~isempty(o.idgaussian)
+                o.isgaussian = true;
             end
-            o.inverse_gamma_1_index = find(prior_shape==4);
-            if ~isempty(o.inverse_gamma_1_index)
-                o.inverse_gamma_1_draws = true;
+            o.idinvgamma1 = find(prior_shape==4);
+            if ~isempty(o.idinvgamma1)
+                o.isinvgamma1 = true;
             end
-            o.uniform_index = find(prior_shape==5);
-            if ~isempty(o.uniform_index)
-                o.uniform_draws = true;
+            o.iduniform = find(prior_shape==5);
+            if ~isempty(o.iduniform)
+                o.isuniform = true;
             end
-            o.inverse_gamma_2_index = find(prior_shape==6);
-            if ~isempty(o.inverse_gamma_2_index)
-                o.inverse_gamma_2_draws = true;
+            o.idinvgamma2 = find(prior_shape==6);
+            if ~isempty(o.idinvgamma2)
+                o.isinvgamma2 = true;
             end
-            o.weibull_index = find(prior_shape==8);
-            if ~isempty(o.weibull_index)
-                o.weibull_draws = true;
+            o.idweibull = find(prior_shape==8);
+            if ~isempty(o.idweibull)
+                o.isweibull = true;
             end
-        end
-
-        function p = draw(o)
-        % Return a random draw from the prior distribution.
-        %
-        % INPUTS
-        % - o    [dprior]
-        %
-        % OUTPUTS
-        % - p    [double]   m×1 vector, random draw from the prior distribution (m is the number of estimated parameters).
-        %
-        % REMARKS
-        % None.
-        %
-        % EXAMPLE
-        %
-        % >> Prior = dprior(bayestopt_, options_.prior_trunc);
-        % >> d = Prior.draw()
-            p = NaN(rows(o.lb), 1);
-            if o.uniform_draws
-                p(o.uniform_index) = rand(length(o.uniform_index),1).*(o.p4(o.uniform_index)-o.p3(o.uniform_index)) + o.p3(o.uniform_index);
-                out_of_bound = find( (p(o.uniform_index)>o.ub(o.uniform_index)) | (p(o.uniform_index)<o.lb(o.uniform_index)));
-                while ~isempty(out_of_bound)
-                    p(o.uniform_index) = rand(length(o.uniform_index), 1).*(o.p4(o.uniform_index)-o.p3(o.uniform_index)) + o.p3(o.uniform_index);
-                    out_of_bound = find( (p(o.uniform_index)>o.ub(o.uniform_index)) | (p(o.uniform_index)<o.lb(o.uniform_index)));
-                end
-            end
-            if o.gaussian_draws
-                p(o.gaussian_index) = randn(length(o.gaussian_index), 1).*o.p7(o.gaussian_index) + o.p6(o.gaussian_index);
-                out_of_bound = find( (p(o.gaussian_index)>o.ub(o.gaussian_index)) | (p(o.gaussian_index)<o.lb(o.gaussian_index)));
-                while ~isempty(out_of_bound)
-                    p(o.gaussian_index(out_of_bound)) = randn(length(o.gaussian_index(out_of_bound)), 1).*o.p7(o.gaussian_index(out_of_bound)) + o.p6(o.gaussian_index(out_of_bound));
-                    out_of_bound = find( (p(o.gaussian_index)>o.ub(o.gaussian_index)) | (p(o.gaussian_index)<o.lb(o.gaussian_index)));
-                end
-            end
-            if o.gamma_draws
-                p(o.gamma_index) = gamrnd(o.p6(o.gamma_index), o.p7(o.gamma_index))+o.p3(o.gamma_index);
-                out_of_bound = find( (p(o.gamma_index)>o.ub(o.gamma_index)) | (p(o.gamma_index)<o.lb(o.gamma_index)));
-                while ~isempty(out_of_bound)
-                    p(o.gamma_index(out_of_bound)) = gamrnd(o.p6(o.gamma_index(out_of_bound)), o.p7(o.gamma_index(out_of_bound)))+o.p3(o.gamma_index(out_of_bound));
-                    out_of_bound = find( (p(o.gamma_index)>o.ub(o.gamma_index)) | (p(o.gamma_index)<o.lb(o.gamma_index)));
-                end
-            end
-            if o.beta_draws
-                p(o.beta_index) = (o.p4(o.beta_index)-o.p3(o.beta_index)).*betarnd(o.p6(o.beta_index), o.p7(o.beta_index))+o.p3(o.beta_index);
-                out_of_bound = find( (p(o.beta_index)>o.ub(o.beta_index)) | (p(o.beta_index)<o.lb(o.beta_index)));
-                while ~isempty(out_of_bound)
-                    p(o.beta_index(out_of_bound)) = (o.p4(o.beta_index(out_of_bound))-o.p3(o.beta_index(out_of_bound))).*betarnd(o.p6(o.beta_index(out_of_bound)), o.p7(o.beta_index(out_of_bound)))+o.p3(o.beta_index(out_of_bound));
-                    out_of_bound = find( (p(o.beta_index)>o.ub(o.beta_index)) | (p(o.beta_index)<o.lb(o.beta_index)));
-                end
-            end
-            if o.inverse_gamma_1_draws
-                p(o.inverse_gamma_1_index) = ...
-                    sqrt(1./gamrnd(o.p7(o.inverse_gamma_1_index)/2, 2./o.p6(o.inverse_gamma_1_index)))+o.p3(o.inverse_gamma_1_index);
-                out_of_bound = find( (p(o.inverse_gamma_1_index)>o.ub(o.inverse_gamma_1_index)) | (p(o.inverse_gamma_1_index)<o.lb(o.inverse_gamma_1_index)));
-                while ~isempty(out_of_bound)
-                    p(o.inverse_gamma_1_index(out_of_bound)) = ...
-                        sqrt(1./gamrnd(o.p7(o.inverse_gamma_1_index(out_of_bound))/2, 2./o.p6(o.inverse_gamma_1_index(out_of_bound))))+o.p3(o.inverse_gamma_1_index(out_of_bound));
-                    out_of_bound = find( (p(o.inverse_gamma_1_index)>o.ub(o.inverse_gamma_1_index)) | (p(o.inverse_gamma_1_index)<o.lb(o.inverse_gamma_1_index)));
-                end
-            end
-            if o.inverse_gamma_2_draws
-                p(o.inverse_gamma_2_index) = ...
-                    1./gamrnd(o.p7(o.inverse_gamma_2_index)/2, 2./o.p6(o.inverse_gamma_2_index))+o.p3(o.inverse_gamma_2_index);
-                out_of_bound = find( (p(o.inverse_gamma_2_index)>o.ub(o.inverse_gamma_2_index)) | (p(o.inverse_gamma_2_index)<o.lb(o.inverse_gamma_2_index)));
-                while ~isempty(out_of_bound)
-                    p(o.inverse_gamma_2_index(out_of_bound)) = ...
-                        1./gamrnd(o.p7(o.inverse_gamma_2_index(out_of_bound))/2, 2./o.p6(o.inverse_gamma_2_index(out_of_bound)))+o.p3(o.inverse_gamma_2_index(out_of_bound));
-                    out_of_bound = find( (p(o.inverse_gamma_2_index)>o.ub(o.inverse_gamma_2_index)) | (p(o.inverse_gamma_2_index)<o.lb(o.inverse_gamma_2_index)));
-                end
-            end
-            if o.weibull_draws
-                p(o.weibull_index) = wblrnd(o.p7(o.weibull_index), o.p6(o.weibull_index)) + o.p3(o.weibull_index);
-                out_of_bound = find( (p(o.weibull_index)>o.ub(o.weibull_index)) | (p(o.weibull_index)<o.lb(o.weibull_index)));
-                while ~isempty(out_of_bound)
-                    p(o.weibull_index(out_of_bound)) = wblrnd(o.p7(o.weibull_index(out_of_bound)), o.p6(o.weibull_index(out_of_bound)))+o.p3(o.weibull_index(out_of_bound));
-                    out_of_bound = find( (p(o.weibull_index)>o.ub(o.weibull_index)) | (p(o.weibull_index)<o.lb(o.weibull_index)));
-                end
-            end
-        end
-
-        function P = draws(o, n)
-        % Return n independent random draws from the prior distribution.
-        %
-        % INPUTS
-        % - o    [dprior]
-        %
-        % OUTPUTS
-        % - P    [double]   m×n matrix, random draw from the prior distribution.
-        %
-        % REMARKS
-        % If the Parallel Computing Toolbox is available, the main loop is run in parallel.
-        %
-        % EXAMPLE
-        %
-        % >> Prior = dprior(bayestopt_, options_.prior_trunc);
-        % >> Prior.draws(1e6)
-            P = NaN(rows(o.lb), 1);
-            parfor i=1:n
-                P(:,i) = draw(o);
-            end
-        end
+        end % dprior (constructor)
 
     end % methods
+
 end % classdef --*-- Unit tests --*--
 
 %@test:1
@@ -263,114 +192,22 @@ end % classdef --*-- Unit tests --*--
 %$ try
 %$    % Instantiate dprior object
 %$    o = dprior(bayestopt_, prior_trunc, false);
-%$    % Do simulations in a loop and estimate recursively the mean and the variance.
-%$    for i = 1:ndraws
-%$         draw = o.draw();
-%$         m1 = m0 + (draw-m0)/i;
-%$         m2 = m1*m1';
-%$         v0 = v0 + ((draw*draw'-m2-v0) + (i-1)*(m0*m0'-m2'))/i;
-%$         m0 = m1;
-%$    end
 %$    t(1) = true;
 %$ catch
 %$     t(1) = false;
 %$ end
 %$
-%$ if t(1)
-%$     t(2) = all(abs(m0-bayestopt_.p1)<3e-3);
-%$     t(3) = all(all(abs(v0-diag(bayestopt_.p2.^2))<5e-3));
-%$ end
 %$ T = all(t);
 %@eof:1
 
 %@test:2
-%$ % Fill global structures with required fields...
-%$ prior_trunc = 1e-10;
-%$ p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1);    % Prior shape
-%$ p1 = .4*ones(14,1);                          % Prior mean
-%$ p2 = .2*ones(14,1);                          % Prior std.
-%$ p3 = NaN(14,1);
-%$ p4 = NaN(14,1);
-%$ p5 = NaN(14,1);
-%$ p6 = NaN(14,1);
-%$ p7 = NaN(14,1);
-%$
-%$ for i=1:14
-%$    switch p0(i)
-%$      case 1
-%$        % Beta distribution
-%$        p3(i) = 0;
-%$        p4(i) = 1;
-%$        [p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
-%$        p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
-%$      case 2
-%$        % Gamma distribution
-%$        p3(i) = 0;
-%$        p4(i) = Inf;
-%$        [p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
-%$        p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
-%$      case 3
-%$        % Normal distribution
-%$        p3(i) = -Inf;
-%$        p4(i) = Inf;
-%$        p6(i) = p1(i);
-%$        p7(i) = p2(i);
-%$        p5(i) = p1(i);
-%$      case 4
-%$        % Inverse Gamma (type I) distribution
-%$        p3(i) = 0;
-%$        p4(i) = Inf;
-%$        [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
-%$        p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
-%$      case 5
-%$        % Uniform distribution
-%$        [p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
-%$        p3(i) = p6(i);
-%$        p4(i) = p7(i);
-%$        p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
-%$      case 6
-%$        % Inverse Gamma (type II) distribution
-%$        p3(i) = 0;
-%$        p4(i) = Inf;
-%$        [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
-%$        p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
-%$      case 8
-%$        % Weibull distribution
-%$        p3(i) = 0;
-%$        p4(i) = Inf;
-%$        [p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
-%$        p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
-%$      otherwise
-%$        error('This density is not implemented!')
-%$    end
-%$ end
-%$
-%$ bayestopt_.pshape = p0;
-%$ bayestopt_.p1 = p1;
-%$ bayestopt_.p2 = p2;
-%$ bayestopt_.p3 = p3;
-%$ bayestopt_.p4 = p4;
-%$ bayestopt_.p5 = p5;
-%$ bayestopt_.p6 = p6;
-%$ bayestopt_.p7 = p7;
-%$
-%$ ndraws = 1e5;
-%$
-%$ % Call the tested routine
 %$ try
-%$    % Instantiate dprior object.
-%$    o = dprior(bayestopt_, prior_trunc, false);
-%$    X = o.draws(ndraws);
-%$    m = mean(X, 2);
-%$    v = var(X, 0, 2);
+%$    % Instantiate dprior object
+%$    o = dprior();
 %$    t(1) = true;
 %$ catch
 %$     t(1) = false;
 %$ end
 %$
-%$ if t(1)
-%$     t(2) = all(abs(m-bayestopt_.p1)<3e-3);
-%$     t(3) = all(all(abs(v-bayestopt_.p2.^2)<5e-3));
-%$ end
 %$ T = all(t);
 %@eof:2

matlab/@dprior/draw.m

@@ -0,0 +1,197 @@
+function p = draw(o)
+
+% Return a random draw from the prior distribution.
+%
+% INPUTS
+% - o    [dprior]
+%
+% OUTPUTS
+% - p    [double]   m×1 vector, random draw from the prior distribution (m is the number of estimated parameters).
+%
+% REMARKS
+% None.
+%
+% EXAMPLE
+%
+% >> Prior = dprior(bayestopt_, options_.prior_trunc);
+% >> d = Prior.draw()
+
+% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
+
+p = NaN(rows(o.lb), 1);
+if o.isuniform
+    p(o.iduniform) = rand(length(o.iduniform),1).*(o.p4(o.iduniform)-o.p3(o.iduniform)) + o.p3(o.iduniform);
+    oob = find( (p(o.iduniform)>o.ub(o.iduniform)) | (p(o.iduniform)<o.lb(o.iduniform)));
+    while ~isempty(oob)
+        p(o.iduniform) = rand(length(o.iduniform), 1).*(o.p4(o.iduniform)-o.p3(o.iduniform)) + o.p3(o.iduniform);
+        oob = find( (p(o.iduniform)>o.ub(o.iduniform)) | (p(o.iduniform)<o.lb(o.iduniform)));
+    end
+end
+if o.isgaussian
+    p(o.idgaussian) = randn(length(o.idgaussian), 1).*o.p7(o.idgaussian) + o.p6(o.idgaussian);
+    oob = find( (p(o.idgaussian)>o.ub(o.idgaussian)) | (p(o.idgaussian)<o.lb(o.idgaussian)));
+    while ~isempty(oob)
+        p(o.idgaussian(oob)) = randn(length(o.idgaussian(oob)), 1).*o.p7(o.idgaussian(oob)) + o.p6(o.idgaussian(oob));
+        oob = find( (p(o.idgaussian)>o.ub(o.idgaussian)) | (p(o.idgaussian)<o.lb(o.idgaussian)));
+    end
+end
+if o.isgamma
+    p(o.idgamma) = gamrnd(o.p6(o.idgamma), o.p7(o.idgamma))+o.p3(o.idgamma);
+    oob = find( (p(o.idgamma)>o.ub(o.idgamma)) | (p(o.idgamma)<o.lb(o.idgamma)));
+    while ~isempty(oob)
+        p(o.idgamma(oob)) = gamrnd(o.p6(o.idgamma(oob)), o.p7(o.idgamma(oob)))+o.p3(o.idgamma(oob));
+        oob = find( (p(o.idgamma)>o.ub(o.idgamma)) | (p(o.idgamma)<o.lb(o.idgamma)));
+    end
+end
+if o.isbeta
+    p(o.idbeta) = (o.p4(o.idbeta)-o.p3(o.idbeta)).*betarnd(o.p6(o.idbeta), o.p7(o.idbeta))+o.p3(o.idbeta);
+    oob = find( (p(o.idbeta)>o.ub(o.idbeta)) | (p(o.idbeta)<o.lb(o.idbeta)));
+    while ~isempty(oob)
+        p(o.idbeta(oob)) = (o.p4(o.idbeta(oob))-o.p3(o.idbeta(oob))).*betarnd(o.p6(o.idbeta(oob)), o.p7(o.idbeta(oob)))+o.p3(o.idbeta(oob));
+        oob = find( (p(o.idbeta)>o.ub(o.idbeta)) | (p(o.idbeta)<o.lb(o.idbeta)));
+    end
+end
+if o.isinvgamma1
+    p(o.idinvgamma1) = ...
+        sqrt(1./gamrnd(o.p7(o.idinvgamma1)/2, 2./o.p6(o.idinvgamma1)))+o.p3(o.idinvgamma1);
+    oob = find( (p(o.idinvgamma1)>o.ub(o.idinvgamma1)) | (p(o.idinvgamma1)<o.lb(o.idinvgamma1)));
+    while ~isempty(oob)
+        p(o.idinvgamma1(oob)) = ...
+            sqrt(1./gamrnd(o.p7(o.idinvgamma1(oob))/2, 2./o.p6(o.idinvgamma1(oob))))+o.p3(o.idinvgamma1(oob));
+        oob = find( (p(o.idinvgamma1)>o.ub(o.idinvgamma1)) | (p(o.idinvgamma1)<o.lb(o.idinvgamma1)));
+    end
+end
+if o.isinvgamma2
+    p(o.idinvgamma2) = ...
+        1./gamrnd(o.p7(o.idinvgamma2)/2, 2./o.p6(o.idinvgamma2))+o.p3(o.idinvgamma2);
+    oob = find( (p(o.idinvgamma2)>o.ub(o.idinvgamma2)) | (p(o.idinvgamma2)<o.lb(o.idinvgamma2)));
+    while ~isempty(oob)
+        p(o.idinvgamma2(oob)) = ...
+            1./gamrnd(o.p7(o.idinvgamma2(oob))/2, 2./o.p6(o.idinvgamma2(oob)))+o.p3(o.idinvgamma2(oob));
+        oob = find( (p(o.idinvgamma2)>o.ub(o.idinvgamma2)) | (p(o.idinvgamma2)<o.lb(o.idinvgamma2)));
+    end
+end
+if o.isweibull
+    p(o.idweibull) = wblrnd(o.p7(o.idweibull), o.p6(o.idweibull)) + o.p3(o.idweibull);
+    oob = find( (p(o.idweibull)>o.ub(o.idweibull)) | (p(o.idweibull)<o.lb(o.idweibull)));
+    while ~isempty(oob)
+        p(o.idweibull(oob)) = wblrnd(o.p7(o.idweibull(oob)), o.p6(o.idweibull(oob)))+o.p3(o.idweibull(oob));
+        oob = find( (p(o.idweibull)>o.ub(o.idweibull)) | (p(o.idweibull)<o.lb(o.idweibull)));
+    end
+end
+
+return % --*-- Unit tests --*--
+
+%@test:1
+% Fill global structures with required fields...
+prior_trunc = 1e-10;
+p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1);    % Prior shape
+p1 = .4*ones(14,1);                          % Prior mean
+p2 = .2*ones(14,1);                          % Prior std.
+p3 = NaN(14,1);
+p4 = NaN(14,1);
+p5 = NaN(14,1);
+p6 = NaN(14,1);
+p7 = NaN(14,1);
+
+for i=1:14
+   switch p0(i)
+     case 1
+       % Beta distribution
+       p3(i) = 0;
+       p4(i) = 1;
+       [p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
+       p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
+     case 2
+       % Gamma distribution
+       p3(i) = 0;
+       p4(i) = Inf;
+       [p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
+       p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
+     case 3
+       % Normal distribution
+       p3(i) = -Inf;
+       p4(i) = Inf;
+       p6(i) = p1(i);
+       p7(i) = p2(i);
+       p5(i) = p1(i);
+     case 4
+       % Inverse Gamma (type I) distribution
+       p3(i) = 0;
+       p4(i) = Inf;
+       [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
+       p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
+     case 5
+       % Uniform distribution
+       [p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
+       p3(i) = p6(i);
+       p4(i) = p7(i);
+       p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
+     case 6
+       % Inverse Gamma (type II) distribution
+       p3(i) = 0;
+       p4(i) = Inf;
+       [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
+       p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
+     case 8
+       % Weibull distribution
+       p3(i) = 0;
+       p4(i) = Inf;
+       [p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
+       p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
+     otherwise
+       error('This density is not implemented!')
+   end
+end
+
+BayesInfo.pshape = p0;
+BayesInfo.p1 = p1;
+BayesInfo.p2 = p2;
+BayesInfo.p3 = p3;
+BayesInfo.p4 = p4;
+BayesInfo.p5 = p5;
+BayesInfo.p6 = p6;
+BayesInfo.p7 = p7;
+
+ndraws = 1e5;
+m0 = BayesInfo.p1; %zeros(14,1);
+v0 = diag(BayesInfo.p2.^2); %zeros(14);
+
+% Call the tested routine
+try
+   % Instantiate dprior object
+   o = dprior(BayesInfo, prior_trunc, false);
+   % Do simulations in a loop and estimate recursively the mean and the variance.
+   for i = 1:ndraws
+        draw = o.draw();
+        m1 = m0 + (draw-m0)/i;
+        m2 = m1*m1';
+        v0 = v0 + ((draw*draw'-m2-v0) + (i-1)*(m0*m0'-m2'))/i;
+        m0 = m1;
+   end
+   t(1) = true;
+catch
+    t(1) = false;
+end
+
+if t(1)
+    t(2) = all(abs(m0-BayesInfo.p1)<3e-3);
+    t(3) = all(all(abs(v0-diag(BayesInfo.p2.^2))<5e-3));
+end
+T = all(t);
+%@eof:1

matlab/@dprior/draws.m

@@ -0,0 +1,133 @@
+function P = draws(o, n)
+
+% Return n independent random draws from the prior distribution.
+%
+% INPUTS
+% - o    [dprior]
+%
+% OUTPUTS
+% - P    [double]   m×n matrix, random draw from the prior distribution.
+%
+% REMARKS
+% If the Parallel Computing Toolbox is available, the main loop is run in parallel.
+%
+% EXAMPLE
+%
+% >> Prior = dprior(bayestopt_, options_.prior_trunc);
+% >> Prior.draws(1e6)
+
+% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
+
+P = NaN(rows(o.lb), 1);
+parfor i=1:n
+    P(:,i) = draw(o);
+end
+
+return % --*-- Unit tests --*--
+
+%@test:1
+% Fill global structures with required fields...
+prior_trunc = 1e-10;
+p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1);    % Prior shape
+p1 = .4*ones(14,1);                          % Prior mean
+p2 = .2*ones(14,1);                          % Prior std.
+p3 = NaN(14,1);
+p4 = NaN(14,1);
+p5 = NaN(14,1);
+p6 = NaN(14,1);
+p7 = NaN(14,1);
+
+for i=1:14
+    switch p0(i)
+      case 1
+        % Beta distribution
+        p3(i) = 0;
+        p4(i) = 1;
+        [p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
+      case 2
+        % Gamma distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
+      case 3
+        % Normal distribution
+        p3(i) = -Inf;
+        p4(i) = Inf;
+        p6(i) = p1(i);
+        p7(i) = p2(i);
+        p5(i) = p1(i);
+      case 4
+        % Inverse Gamma (type I) distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
+      case 5
+        % Uniform distribution
+        [p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
+        p3(i) = p6(i);
+        p4(i) = p7(i);
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
+      case 6
+        % Inverse Gamma (type II) distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
+      case 8
+        % Weibull distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
+      otherwise
+        error('This density is not implemented!')
+    end
+end
+
+BayesInfo.pshape = p0;
+BayesInfo.p1 = p1;
+BayesInfo.p2 = p2;
+BayesInfo.p3 = p3;
+BayesInfo.p4 = p4;
+BayesInfo.p5 = p5;
+BayesInfo.p6 = p6;
+BayesInfo.p7 = p7;
+
+ndraws = 1e5;
+
+% Call the tested routine
+try
+    % Instantiate dprior object.
+    o = dprior(BayesInfo, prior_trunc, false);
+    X = o.draws(ndraws);
+    m = mean(X, 2);
+    v = var(X, 0, 2);
+    t(1) = true;
+catch
+    t(1) = false;
+end
+
+if t(1)
+    t(2) = all(abs(m-BayesInfo.p1)<3e-3);
+    t(3) = all(all(abs(v-BayesInfo.p2.^2)<5e-3));
+end
+T = all(t);
+%@eof:1

matlab/@dprior/length.m

@@ -0,0 +1,31 @@
+function n = length(o)
+
+% Return the dimension of the random vector.
+%
+% INPUTS
+% - o      [dprior]   Distribution specification for a n×1 vector of independent continuous random variables
+%
+% OUTPUTS
+% - n      [integer]  scalar.
+%
+% REMARKS
+% None.
+
+% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
+
+n = length(o.lb);

matlab/@dprior/mean.m

@@ -0,0 +1,42 @@
+function m = mean(o, resetmoments)
+
+% Return the prior mean.
+%
+% INPUTS
+% - o               [dprior]
+% - resetmoments    [logical]     Force the computation of the prior mean
+%
+% OUTPUTS
+% - m               [double]      n×1 vector, prior mean
+
+% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
+
+if nargin<2
+    resetmoments = false;
+end
+
+if any(isnan(o.p1))
+    resetmoments = true;
+end
+
+if resetmoments
+    o.p1 = NaN(size(o.p1));
+    o.moments('mean');
+end
+
+m = o.p1;

matlab/@dprior/median.m

@@ -0,0 +1,42 @@
+function m = median(o, resetmoments)
+
+% Return the prior median.
+%
+% INPUTS
+% - o               [dprior]
+% - resetmoments    [logical]     Force the computation of the prior median
+%
+% OUTPUTS
+% - m               [double]      n×1 vector, prior median
+
+% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
+
+if nargin<2
+    resetmoments = false;
+end
+
+if any(isnan(o.p11))
+    resetmoments = true;
+end
+
+if resetmoments
+    o.p11 = NaN(size(o.p11));
+    o.moments('median');
+end
+
+m = o.p11;

matlab/@dprior/mode.m

@@ -0,0 +1,42 @@
+function m = mode(o, resetmoments)
+
+% Return the prior mode.
+%
+% INPUTS
+% - o               [dprior]
+% - resetmoments    [logical]     Force the computation of the prior mode
+%
+% OUTPUTS
+% - m               [double]      n×1 vector, prior mode
+
+% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
+
+if nargin<2
+    resetmoments = false;
+end
+
+if any(isnan(o.p5))
+    resetmoments = true;
+end
+
+if resetmoments
+    o.p5 = NaN(size(o.p5));
+    o.moments('mode');
+end
+
+m = o.p5;

matlab/@dprior/moments.m

@@ -0,0 +1,291 @@
+function o = moments(o, name)
+
+% Compute the prior moments.
+%
+% INPUTS
+% - o       [dprior]
+%
+% OUTPUTS
+% - o       [dprior]
+
+% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
+
+switch name
+  case 'mean'
+    m = o.p1;
+  case 'median'
+    m = o.p11;
+  case 'std'
+    m = o.p2;
+  case 'mode'
+    m = o.p5;
+  otherwise
+    error('%s is not an implemented moemnt.', name)
+end
+id = isnan(m);
+if any(id)
+    % For some parameters the prior mean is not defined.
+    % We compute the first order moment from the
+    % hyperparameters, if the hyperparameters are not
+    % available an error is thrown.
+    if o.isuniform
+        jd = intersect(o.iduniform, find(id));
+        if ~isempty(jd)
+            if any(isnan(o.p3(jd))) || any(isnan(o.p4(jd)))
+                error('dprior::mean: Some hyperparameters are missing (uniform distribution).')
+            end
+            switch name
+              case 'mean'
+                m(jd) = o.p3(jd) + .5*(o.p4(jd)-o.p3(jd));
+              case 'median'
+                m(jd) = o.p3(jd) + .5*(o.p4(jd)-o.p3(jd));
+              case 'std'
+                m(jd) = (o.p4(jd)-o.p3(jd))/sqrt(12);
+              case 'mode' % Actually we have a continuum of modes with the uniform distribution.
+                m(jd) = o.p3(jd) + .5*(o.p4(jd)-o.p3(jd));
+            end
+        end
+    end
+    if o.isgaussian
+        jd = intersect(o.idgaussian, find(id));
+        if ~isempty(jd)
+            if any(isnan(o.p6(jd))) || any(isnan(o.p7(jd)))
+                error('dprior::mean: Some hyperparameters are missing (gaussian distribution).')
+            end
+            switch name
+              case 'mean'
+                m(jd) = o.p6(jd);
+              case 'median'
+                m(jd) = o.p6(jd);
+              case 'std'
+                m(jd) = o.p7(jd);
+              case 'mode' % Actually we have a continuum of modes with the uniform distribution.
+                m(jd) = o.p6(jd);
+            end
+        end
+    end
+    if o.isgamma
+        jd = intersect(o.idgamma, find(id));
+        if ~isempty(jd)
+            if any(isnan(o.p6(jd))) || any(isnan(o.p7(jd))) || any(isnan(o.p3(jd)))
+                error('dprior::mean: Some hyperparameters are missing (gamma distribution).')
+            end
+            % α → o.p6, β → o.p7
+            switch name
+              case 'mean'
+                m(jd) = o.p3(jd) + o.p6(jd).*o.p7(jd);
+              case 'median'
+                m(jd) = o.p3(jd) + gaminv(.5, o.p6(jd), o.p7(jda));
+              case 'std'
+                m(jd) = sqrt(o.p6(jd)).*o.p7(jd);
+              case 'mode'
+                m(jd) = 0;
+                hd = o.p6(jd)>1;
+                m(jd(hd)) = (o.p6(jd(hd))-1).*o.p7(jd(hd));
+            end
+        end
+    end
+    if o.isbeta
+        jd = intersect(o.idbeta, find(id));
+        if ~isempty(jd)
+            if any(isnan(o.p6(jd))) || any(isnan(o.p7(jd))) || any(isnan(o.p3(jd))) || any(isnan(o.p4(jd)))
+                error('dprior::mean: Some hyperparameters are missing (beta distribution).')
+            end
+            % α → o.p6, β → o.p7
+            switch name
+              case 'mean'
+                m(jd) = o.p3(jd) + (o.p6(jd)./(o.p6(jd)+o.p7(jd))).*(o.p4(jd)-o.p3(jd));
+              case 'median'
+                m(jd) = o.p3(jd) + betainv(.5, o.p6(jd), o.p7(jd)).*(o.p4(jd)-o.p3(jd));
+              case 'std'
+                m(jd) = (o.p4(jd)-o.p3(jd)).*sqrt(o.p6(jd).*o.p7(jd)./((o.p6(jd)+o.p7(jd)).^2.*(o.p6(jd)+o.p7(jd)+1)));
+              case 'mode'
+                h0 = true(jd, 1);
+                h1 = o.p6(jd)<=1 & o.p7(jd)>1; h0 = h0 & ~h1;
+                h2 = o.p7(jd)<=1 & o.p6(jd)>1; h0 = h0 & ~h2;
+                h3 = o.p6(jd)<1 & o.p7(jd)<1; h0 = h0 & ~h3;
+                h4 = ismembertol(o.p6(jd), 1) & ismembertol(o.p7(jd),1); h0 = h0 & ~h4;
+                m(jd(h1)) = o.p3(jd(h1));                                  % Standard β has a mode at 0
+                m(jd(h2)) = o.p4(jd(h2));                                  % Standard β has a mode at 1
+                m(jd(h3)) = o.p3(jd(h3));                                  % Standard β is bimodal, we pick the lowest mode (0)
+                m(jd(h4)) = o.p3(jd(h4)) + .5*(o.p4(jd(h4))-o.p3(jd(h4))); % Standard β is the uniform distribution (continuum of modes), we pick the mean as the mode
+                m(jd(h0)) = o.p3(jd(h0))+(o.p4(jd(h0))-o.p3(jd(h0))).*((o.p6(jd(h0))-1)./(o.p6(jd(h0))+o.p7(jd(h0))-2)); % β distribution is concave and has a unique interior mode.
+            end
+        end
+    end
+    if o.isinvgamma1
+        jd = intersect(o.idinvgamma1, find(id));
+        if ~isempty(jd)
+            if any(isnan(o.p6(jd))) || any(isnan(o.p7(jd))) || any(isnan(o.p3(jd)))
+                error('dprior::mean: Some hyperparameters are missing (inverse gamma type 1 distribution).')
+            end
+            % s → o.p6, ν → o.p7
+            switch name
+              case 'mean'
+                m(jd) = o.p3(jd) + sqrt(.5*o.p6(jd)) .*(gamma(.5*(o.p7(jd)-1))./gamma(.5*o.p7(jd)));
+              case 'median'
+                m(jd) = o.p3(jd) + 1.0/sqrt(gaminv(.5, o.p7(jd)/2.0, 2.0/o.p6(jd)));
+              case 'std'
+                m(jd) = sqrt( o.p6(jd)./(o.p7(jd)-2)-(.5*o.p6(jd)).*(gamma(.5*(o.p7(jd)-1))./gamma(.5*o.p7(jd))).^2);
+              case 'mode'
+                m(jd) = sqrt((o.p7(jd)-1)./o.p6(jd));
+            end
+        end
+    end
+    if o.isinvgamma2
+        jd = intersect(o.idinvgamma2, find(id));
+        if ~isempty(jd)
+            if any(isnan(o.p6(jd))) || any(isnan(o.p7(jd))) || any(isnan(o.p3(jd)))
+                error('dprior::mean: Some hyperparameters are missing (inverse gamma type 2 distribution).')
+            end
+            % s → o.p6, ν → o.p7
+            switch name
+              case 'mean'
+                m(jd) =  o.p3(jd) + o.p6(jd)./(o.p7(jd)-2);
+              case 'median'
+                m(jd) = o.p3(jd) + 1.0/gaminv(.5, o.p7(jd)/2.0, 2.0/o.p6(jd));
+              case 'std'
+                m(jd) = sqrt(2./(o.p7(jd)-4)).*o.p6(jd)./(o.p7(jd)-2);
+              case 'mode'
+                m(jd) = o.p6(jd)./(o.p7(jd)+2);
+            end
+        end
+    end
+    if o.isweibull
+        jd = intersect(o.idweibull, find(id));
+        if ~isempty(jd)
+            if any(isnan(o.p6(jd))) || any(isnan(o.p7(jd))) || any(isnan(o.p3(jd)))
+                error('dprior::mean: Some hyperparameters are missing (weibull distribution).')
+            end
+            % k → o.p6 (shape parameter), λ → o.p7 (scale parameter)
+            % See https://en.wikipedia.org/wiki/Weibull_distribution
+            switch name
+              case 'mean'
+                m(jd) =  o.p3(jd) + o.p7(jd).*gamma(1+1./o.p6(jd));
+              case 'median'
+                m(jd) = o.p3(jd) + o.p7(jd).*log(2).^(1./o.p6(jd));
+              case 'std'
+                m(jd) = o.p7(jd).*sqrt(gamma(1+2./o.p6(jd))-gamma(1+1./o.p6(jd)).^2);
+              case 'mode'
+                m(jd) = 0;
+                hd = o.p6(jd)>1;
+                m(jd(hd)) = o.p3(jd(hd)) + o.p7(jd(hd)).*((o.p6(jd(hd))-1)./o.p6(jd(hd))).^(1./o.p6(jd(hd)));
+            end
+        end
+    end
+    switch name
+      case 'mean'
+        o.p1 = m;
+      case 'median'
+        o.p11 = m;
+      case 'std'
+        o.p2 = m;
+      case 'mode'
+        o.p5 = m;
+    end
+end
+
+return % --*-- Unit tests --*--
+
+%@test:5
+% Fill global structures with required fields...
+prior_trunc = 1e-10;
+p0 = repmat([1; 2; 3; 4; 5; 6; 8], 2, 1);    % Prior shape
+p1 = .4*ones(14,1);                          % Prior mean
+p2 = .2*ones(14,1);                          % Prior std.
+p3 = NaN(14,1);
+p4 = NaN(14,1);
+p5 = NaN(14,1);
+p6 = NaN(14,1);
+p7 = NaN(14,1);
+
+for i=1:14
+    switch p0(i)
+      case 1
+        % Beta distribution
+        p3(i) = 0;
+        p4(i) = 1;
+        [p6(i), p7(i)] = beta_specification(p1(i), p2(i)^2, p3(i), p4(i));
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 1);
+      case 2
+        % Gamma distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = gamma_specification(p1(i), p2(i)^2, p3(i), p4(i));
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 2);
+      case 3
+        % Normal distribution
+        p3(i) = -Inf;
+        p4(i) = Inf;
+        p6(i) = p1(i);
+        p7(i) = p2(i);
+        p5(i) = p1(i);
+      case 4
+        % Inverse Gamma (type I) distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 1, false);
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 4);
+      case 5
+        % Uniform distribution
+        [p1(i), p2(i), p6(i), p7(i)] = uniform_specification(p1(i), p2(i), p3(i), p4(i));
+        p3(i) = p6(i);
+        p4(i) = p7(i);
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 5);
+      case 6
+        % Inverse Gamma (type II) distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = inverse_gamma_specification(p1(i), p2(i)^2, p3(i), 2, false);
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 6);
+      case 8
+        % Weibull distribution
+        p3(i) = 0;
+        p4(i) = Inf;
+        [p6(i), p7(i)] = weibull_specification(p1(i), p2(i)^2, p3(i));
+        p5(i) = compute_prior_mode([p6(i) p7(i)], 8);
+      otherwise
+        error('This density is not implemented!')
+    end
+end
+
+BayesInfo.pshape = p0;
+BayesInfo.p1 = p1;
+BayesInfo.p2 = p2;
+BayesInfo.p3 = p3;
+BayesInfo.p4 = p4;
+BayesInfo.p5 = p5;
+BayesInfo.p6 = p6;
+BayesInfo.p7 = p7;
+
+% Call the tested routine
+try
+    Prior = dprior(BayesInfo, prior_trunc, false);
+    t(1) = true;
+catch
+    t(1) = false;
+end
+
+if t(1)
+    t(2) = all(Prior.mean()==.4);
+    t(3) = all(ismembertol(Prior.mean(true),.4));
+    t(4) = all(ismembertol(Prior.variance(),.04));
+    t(5) = all(ismembertol(Prior.variance(true),.04));
+end
+T = all(t);
+%@eof:5

matlab/@dprior/subsref.m

@@ -0,0 +1,41 @@
+function p = subsref(o, S)
+
+% Overload subsref method.
+
+% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
+
+switch S(1).type
+  case '.'
+    if ismember(S(1).subs, {'p1','p2','p3','p4','p5','p6','p7','lb','ub'})
+        p = builtin('subsref', o, S(1));
+    elseif ismember(S(1).subs, {'draw','length'})
+        p = feval(S(1).subs, o);
+    elseif ismember(S(1).subs, {'draws', 'density', 'densities', 'moments', 'admissible'})
+        p = feval(S(1).subs, o , S(2).subs{:});
+    elseif ismember(S(1).subs, {'mean', 'median', 'variance', 'mode'})
+        if (length(S)==2 && isempty(S(2).subs)) || length(S)==1
+            p = feval(S(1).subs, o);
+        else
+            p = feval(S(1).subs, o , S(2).subs{:});
+        end
+    else
+        error('dprior::subsref: unknown method (%s).', S(1).subs)
+    end
+  otherwise
+    error('dprior::subsref: %s indexing not implemented.', S(1).type)
+end

matlab/@dprior/variance.m

@@ -0,0 +1,42 @@
+function m = variance(o, resetmoments)
+
+% Return the prior variance.
+%
+% INPUTS
+% - o               [dprior]
+% - resetmoments    [logical]     Force the computation of the prior variance
+%
+% OUTPUTS
+% - m               [double]      n×1 vector, prior variance
+
+% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
+
+if nargin<2
+    resetmoments = false;
+end
+
+if any(isnan(o.p2))
+    resetmoments = true;
+end
+
+if resetmoments
+    o.p2 = NaN(size(o.p2));
+    o.moments('std');
+end
+
+m = o.p2.^2;

matlab/AHessian.m

@@ -1,152 +0,0 @@
-function [AHess, DLIK, LIK] = AHessian(T,R,Q,H,P,Y,DT,DYss,DOm,DH,DP,start,mf,kalman_tol,riccati_tol)
-% function [AHess, DLIK, LIK] = AHessian(T,R,Q,H,P,Y,DT,DYss,DOm,DH,DP,start,mf,kalman_tol,riccati_tol)
-%
-% computes the asymptotic hessian matrix of the log-likelihood function of
-% a state space model (notation as in kalman_filter.m in DYNARE
-% Thanks to  Nikolai Iskrev
-%
-% NOTE: the derivative matrices (DT,DR ...) are 3-dim. arrays with last
-% dimension equal to the number of structural parameters
-
-% Copyright © 2011-2017 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 <https://www.gnu.org/licenses/>.
-
-
-k = size(DT,3);                                 % number of structural parameters
-smpl = size(Y,2);                               % Sample size.
-pp   = size(Y,1);                               % Maximum number of observed variables.
-mm   = size(T,2);                               % Number of state variables.
-a    = zeros(mm,1);                             % State vector.
-Om   = R*Q*transpose(R);                        % Variance of R times the vector of structural innovations.
-t    = 0;                                       % Initialization of the time index.
-oldK = 0;
-notsteady   = 1;                                % Steady state flag.
-F_singular  = 1;
-
-lik  = zeros(smpl,1);                           % Initialization of the vector gathering the densities.
-LIK  = Inf;                                     % Default value of the log likelihood.
-if nargout > 1
-    DLIK  = zeros(k,1);                             % Initialization of the score.
-end
-AHess  = zeros(k,k);                             % Initialization of the Hessian
-Da    = zeros(mm,k);                             % State vector.
-Dv = zeros(length(mf),k);
-
-%     for ii = 1:k
-%         DOm = DR(:,:,ii)*Q*transpose(R) + R*DQ(:,:,ii)*transpose(R) + R*Q*transpose(DR(:,:,ii));
-%     end
-
-while notsteady && t<smpl
-    t  = t+1;
-    v  = Y(:,t)-a(mf);
-    F  = P(mf,mf) + H;
-    if rcond(F) < kalman_tol
-        if ~all(abs(F(:))<kalman_tol)
-            return
-        else
-            a = T*a;
-            P = T*P*transpose(T)+Om;
-        end
-    else
-        F_singular = 0;
-        iF     = inv(F);
-        K      = P(:,mf)*iF;
-        lik(t) = log(det(F))+transpose(v)*iF*v;
-
-        [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K);
-
-        for ii = 1:k
-            Dv(:,ii)   = -Da(mf,ii) - DYss(mf,ii);
-            Da(:,ii)   = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
-            if t>=start && nargout > 1
-                DLIK(ii,1)  = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
-            end
-        end
-        vecDPmf = reshape(DP(mf,mf,:),[],k);
-        %                     iPmf = inv(P(mf,mf));
-        if t>=start
-            AHess = AHess + Dv'*iF*Dv + .5*(vecDPmf' * kron(iF,iF) * vecDPmf);
-        end
-        a      = T*(a+K*v);
-        P      = T*(P-K*P(mf,:))*transpose(T)+Om;
-        DP     = DP1;
-    end
-    notsteady = max(max(abs(K-oldK))) > riccati_tol;
-    oldK = K;
-end
-
-if F_singular
-    error('The variance of the forecast error remains singular until the end of the sample')
-end
-
-
-if t < smpl
-    t0 = t+1;
-    while t < smpl
-        t = t+1;
-        v = Y(:,t)-a(mf);
-        for ii = 1:k
-            Dv(:,ii)   = -Da(mf,ii)-DYss(mf,ii);
-            Da(:,ii)   = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
-            if t>=start && nargout >1
-                DLIK(ii,1)  = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
-            end
-        end
-        if t>=start
-            AHess = AHess + Dv'*iF*Dv;
-        end
-        a = T*(a+K*v);
-        lik(t) = transpose(v)*iF*v;
-    end
-    AHess = AHess + .5*(smpl-t0+1)*(vecDPmf' * kron(iF,iF) * vecDPmf);
-    if nargout > 1
-        for ii = 1:k
-            %             DLIK(ii,1)  = DLIK(ii,1) + (smpl-t0+1)*trace( iF*DF(:,:,ii) );
-        end
-    end
-    lik(t0:smpl) = lik(t0:smpl) + log(det(F));
-    %         for ii = 1:k;
-    %             for jj = 1:ii
-    %              H(ii,jj) = trace(iPmf*(.5*DP(mf,mf,ii)*iPmf*DP(mf,mf,jj) + Dv(:,ii)*Dv(:,jj)'));
-    %             end
-    %         end
-end
-
-AHess = -AHess;
-if nargout > 1
-    DLIK = DLIK/2;
-end
-% adding log-likelihhod constants
-lik = (lik + pp*log(2*pi))/2;
-
-LIK = sum(lik(start:end)); % Minus the log-likelihood.
-                           % end of main function
-
-function [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K)
-
-k      = size(DT,3);
-tmp    = P-K*P(mf,:);
-
-for ii = 1:k
-    DF(:,:,ii)  = DP(mf,mf,ii) + DH(:,:,ii);
-    DiF(:,:,ii) = -iF*DF(:,:,ii)*iF;
-    DK(:,:,ii)  = DP(:,mf,ii)*iF + P(:,mf)*DiF(:,:,ii);
-    Dtmp        = DP(:,:,ii) - DK(:,:,ii)*P(mf,:) - K*DP(mf,:,ii);
-    DP1(:,:,ii) = DT(:,:,ii)*tmp*T' + T*Dtmp*T' + T*tmp*DT(:,:,ii)' + DOm(:,:,ii);
-end
-
-% end of computeDKalman

matlab/DsgeSmoother.m

@@ -275,7 +275,7 @@ if kalman_algo == 1 || kalman_algo == 3
             kalman_algo = 2;
         elseif kalman_algo == 3
             fprintf('\nDsgeSmoother: Switching to univariate filter. This is usually due to co-integration in diffuse filter,\n')
-            fprintf(' otherwise it may be a sign of stochastic singularity.\n')
+            fprintf('otherwise it may be a sign of stochastic singularity.\n')
             kalman_algo = 4;
         else
             error('This case shouldn''t happen')

matlab/GetAllPosteriorDraws.m

@@ -1,23 +1,24 @@
-function Draws = GetAllPosteriorDraws(dname, fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFile, NumberOfDraws, nblcks, blck)
-% function Draws = GetAllPosteriorDraws(dname, fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFile, NumberOfDraws, nblcks, blck)
-% Gets all posterior draws
+function draws = GetAllPosteriorDraws(options_, dname, fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFile, NumberOfDraws, nblcks, blck)
+
+% Gets all posterior draws.
 %
 % INPUTS
-%    dname:                name of directory with results
-%    fname:                name of mod file
-%    column:               column of desired parameter in draw matrix
-%    FirstMhFile:          first mh file
-%    FirstLine:            first line
-%    TotalNumberOfMhFile:  total number of mh file
-%    NumberOfDraws:        number of draws
-%    nblcks:               total number of blocks 
-%    blck:                 desired block to read
+% - options_               [struct]   Dynare's options.
+% - dname                  [char]     name of directory with results.
+% - fname                  [char]     name of mod file.
+% - column                 [integer]  scalar, parameter index.
+% - FirstMhFile            [integer]  scalar, first MH file.
+% - FirstLine              [integer]  scalar, first line in first MH file.
+% - TotalNumberOfMhFile    [integer]  scalar, total number of MH file.
+% - NumberOfDraws          [integer]  scalar, number of posterior draws.
+% - nblcks                 [integer]  scalar, total number of blocks.
+% - blck:                  [integer]  scalar, desired block to read.
 %
 % OUTPUTS
-%    Draws:                draws from posterior distribution
+% - draws:                 [double]   NumberOfDraws×1 vector, draws from posterior distribution.
 %
-% SPECIAL REQUIREMENTS
-%    none
+% REMARKS
+% Only the first and third input arguments are required for SMC samplers.
 
 % Copyright © 2005-2023 Dynare Team
 %
@@ -36,55 +37,61 @@ function Draws = GetAllPosteriorDraws(dname, fname, column, FirstMhFile, FirstLi
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
-iline = FirstLine;
-linee = 1;
-DirectoryName = CheckPath('metropolis',dname);
-
-if nblcks>1 && nargin<9
-    Draws = zeros(NumberOfDraws*nblcks,1);
-    iline0=iline;
-    if column>0
-        for blck = 1:nblcks
-            iline=iline0;
+if ishssmc(options_)
+    % Load draws from the posterior distribution
+    pfiles = dir(sprintf('%s/hssmc/particles-*.mat', dname));
+    posterior = load(sprintf('%s/hssmc/particles-%u-%u.mat', dname, length(pfiles), length(pfiles)));
+    draws = transpose(posterior.particles(column,:));
+else
+    iline = FirstLine;
+    linee = 1;
+    DirectoryName = CheckPath('metropolis',dname);
+    if nblcks>1 && nargin<10
+        draws = zeros(NumberOfDraws*nblcks,1);
+        iline0=iline;
+        if column>0
+            for blck = 1:nblcks
+                iline=iline0;
+                for file = FirstMhFile:TotalNumberOfMhFile
+                    load([DirectoryName '/'  fname '_mh' int2str(file) '_blck' int2str(blck)],'x2')
+                    NumberOfLines = size(x2(iline:end,:),1);
+                    draws(linee:linee+NumberOfLines-1) = x2(iline:end,column);
+                    linee = linee+NumberOfLines;
+                    iline = 1;
+                end
+            end
+        else
+            for blck = 1:nblcks
+                iline=iline0;
+                for file = FirstMhFile:TotalNumberOfMhFile
+                    load([DirectoryName '/'  fname '_mh' int2str(file) '_blck' int2str(blck)],'logpo2')
+                    NumberOfLines = size(logpo2(iline:end),1);
+                    draws(linee:linee+NumberOfLines-1) = logpo2(iline:end);
+                    linee = linee+NumberOfLines;
+                    iline = 1;
+                end
+            end
+        end
+    else
+        if nblcks==1
+            blck=1;
+        end
+        if column>0
             for file = FirstMhFile:TotalNumberOfMhFile
                 load([DirectoryName '/'  fname '_mh' int2str(file) '_blck' int2str(blck)],'x2')
                 NumberOfLines = size(x2(iline:end,:),1);
-                Draws(linee:linee+NumberOfLines-1) = x2(iline:end,column);
+                draws(linee:linee+NumberOfLines-1) = x2(iline:end,column);
                 linee = linee+NumberOfLines;
                 iline = 1;
             end
-        end
-    else
-        for blck = 1:nblcks
-            iline=iline0;
+        else
             for file = FirstMhFile:TotalNumberOfMhFile
                 load([DirectoryName '/'  fname '_mh' int2str(file) '_blck' int2str(blck)],'logpo2')
-                NumberOfLines = size(logpo2(iline:end),1);
-                Draws(linee:linee+NumberOfLines-1) = logpo2(iline:end);
+                NumberOfLines = size(logpo2(iline:end,:),1);
+                draws(linee:linee+NumberOfLines-1) = logpo2(iline:end);
                 linee = linee+NumberOfLines;
                 iline = 1;
             end
         end
     end
-else
-    if nblcks==1
-        blck=1;
-    end
-    if column>0
-        for file = FirstMhFile:TotalNumberOfMhFile
-            load([DirectoryName '/'  fname '_mh' int2str(file) '_blck' int2str(blck)],'x2')
-            NumberOfLines = size(x2(iline:end,:),1);
-            Draws(linee:linee+NumberOfLines-1) = x2(iline:end,column);
-            linee = linee+NumberOfLines;
-            iline = 1;
-        end
-    else
-        for file = FirstMhFile:TotalNumberOfMhFile
-            load([DirectoryName '/'  fname '_mh' int2str(file) '_blck' int2str(blck)],'logpo2')
-            NumberOfLines = size(logpo2(iline:end,:),1);
-            Draws(linee:linee+NumberOfLines-1) = logpo2(iline:end);
-            linee = linee+NumberOfLines;
-            iline = 1;
-        end
-    end
-end
+end

matlab/GetPosteriorMeanVariance.m

@@ -1,18 +1,15 @@
-function [mean,variance] = GetPosteriorMeanVariance(M_,drop)
+function [mean, variance] = GetPosteriorMeanVariance(options_, M_)
 %function [mean,variance] = GetPosteriorMeanVariance(M,drop)
 % Computes the posterior mean and variance
 % (+updates of oo_ & TeX output).
 %
 % INPUTS
-%   M_               [structure]    Dynare model structure
-%   drop             [double]       share of draws to drop
+% - options_         [struct]    Dynare's options.
+% - M_               [struct]    Description of the model.
 %
 % OUTPUTS
-%   mean             [double]       mean parameter vector
-%   variance         [double]       variance 
-%
-% SPECIAL REQUIREMENTS
-%   None.
+% - mean             [double]    n×1 vector, posterior expectation.
+% - variance         [double]    n×n matrix, posterior variance.
 
 % Copyright © 2012-2023 Dynare Team
 %
@@ -31,37 +28,46 @@ function [mean,variance] = GetPosteriorMeanVariance(M_,drop)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
-MetropolisFolder = CheckPath('metropolis',M_.dname);
-FileName = M_.fname;
-BaseName = [MetropolisFolder filesep FileName];
-record=load_last_mh_history_file(MetropolisFolder, FileName);
-NbrDraws = sum(record.MhDraws(:,1));
-NbrFiles = sum(record.MhDraws(:,2));
-NbrBlocks = record.Nblck;
-mean = 0;
-variance = 0;
-
-NbrKeptDraws = 0;
-for i=1:NbrBlocks
-    NbrDrawsCurrentBlock = 0;
-    for j=1:NbrFiles
-        o = load([BaseName '_mh' int2str(j) '_blck' int2str(i),'.mat']);
-        NbrDrawsCurrentFile = size(o.x2,1);
-        if NbrDrawsCurrentBlock + NbrDrawsCurrentFile <= drop*NbrDraws
+if ishssmc(options_)
+    % Load draws from the posterior distribution
+    pfiles = dir(sprintf('%s/hssmc/particles-*.mat', M_.dname));
+    posterior = load(sprintf('%s/hssmc/particles-%u-%u.mat', M_.dname, length(pfiles), length(pfiles)));
+    % Compute the posterior mean
+    mean = sum(posterior.particles, 2)/length(posterior.tlogpostkernel);
+    % Compute the posterior covariance
+    variance = (posterior.particles-mean)*(posterior.particles-mean)'/length(posterior.tlogpostkernel);
+else
+    MetropolisFolder = CheckPath('metropolis',M_.dname);
+    FileName = M_.fname;
+    BaseName = [MetropolisFolder filesep FileName];
+    record=load_last_mh_history_file(MetropolisFolder, FileName);
+    NbrDraws = sum(record.MhDraws(:,1));
+    NbrFiles = sum(record.MhDraws(:,2));
+    NbrBlocks = record.Nblck;
+    mean = 0;
+    variance = 0;
+    NbrKeptDraws = 0;
+    for i=1:NbrBlocks
+        NbrDrawsCurrentBlock = 0;
+        for j=1:NbrFiles
+            o = load([BaseName '_mh' int2str(j) '_blck' int2str(i),'.mat']);
+            NbrDrawsCurrentFile = size(o.x2,1);
+            if NbrDrawsCurrentBlock + NbrDrawsCurrentFile <= options_.mh_drop*NbrDraws
+                NbrDrawsCurrentBlock = NbrDrawsCurrentBlock + NbrDrawsCurrentFile;
+                continue
+            elseif NbrDrawsCurrentBlock < options_.mh_drop*NbrDraws
+                FirstDraw = ceil(options_.mh_drop*NbrDraws - NbrDrawsCurrentBlock + 1);
+                x2 = o.x2(FirstDraw:end,:);
+            else
+                x2 = o.x2;
+            end
+            NbrKeptDrawsCurrentFile = size(x2,1);
+            %recursively compute mean and variance
+            mean = (NbrKeptDraws*mean + sum(x2)')/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
+            x2Demeaned = bsxfun(@minus,x2,mean');
+            variance = (NbrKeptDraws*variance + x2Demeaned'*x2Demeaned)/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
             NbrDrawsCurrentBlock = NbrDrawsCurrentBlock + NbrDrawsCurrentFile;
-            continue
-        elseif NbrDrawsCurrentBlock < drop*NbrDraws
-            FirstDraw = ceil(drop*NbrDraws - NbrDrawsCurrentBlock + 1);
-            x2 = o.x2(FirstDraw:end,:);
-        else
-            x2 = o.x2;
+            NbrKeptDraws = NbrKeptDraws + NbrKeptDrawsCurrentFile;
         end
-        NbrKeptDrawsCurrentFile = size(x2,1);
-        %recursively compute mean and variance
-        mean = (NbrKeptDraws*mean + sum(x2)')/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
-        x2Demeaned = bsxfun(@minus,x2,mean');
-        variance = (NbrKeptDraws*variance + x2Demeaned'*x2Demeaned)/(NbrKeptDraws+NbrKeptDrawsCurrentFile);
-        NbrDrawsCurrentBlock = NbrDrawsCurrentBlock + NbrDrawsCurrentFile;
-        NbrKeptDraws = NbrKeptDraws + NbrKeptDrawsCurrentFile;
     end
 end

matlab/GetPosteriorParametersStatistics.m

@@ -1,5 +1,5 @@
 function oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_, pnames)
-% function oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_, pnames)
+
 % This function prints and saves posterior estimates after the mcmc
 % (+updates of oo_ & TeX output).
 %
@@ -34,10 +34,6 @@ function oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bay
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
-%if ~options_.mh_replic && options_.load_mh_file
-%   load([M_.fname '_results.mat'],'oo_');
-%end
-
 TeX     = options_.TeX;
 nvx     = estim_params_.nvx;
 nvn     = estim_params_.nvn;
@@ -45,19 +41,20 @@ ncx     = estim_params_.ncx;
 ncn     = estim_params_.ncn;
 np      = estim_params_.np ;
 
-MetropolisFolder = CheckPath('metropolis',M_.dname);
 latexFolder = CheckPath('latex',M_.dname);
 FileName = M_.fname;
 
-record=load_last_mh_history_file(MetropolisFolder,FileName);
-
-FirstLine = record.KeepedDraws.FirstLine;
-TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
-TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
-FirstMhFile = record.KeepedDraws.FirstMhFile;
-NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
-mh_nblck = size(record.LastParameters,1);
-clear record;
+if ~issmc(options_)
+    MetropolisFolder = CheckPath('metropolis',M_.dname);
+    record=load_last_mh_history_file(MetropolisFolder,FileName);
+    FirstLine = record.KeepedDraws.FirstLine;
+    TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
+    TotalNumberOfMhDraws = sum(record.MhDraws(:,1));
+    FirstMhFile = record.KeepedDraws.FirstMhFile;
+    NumberOfDraws = TotalNumberOfMhDraws-floor(options_.mh_drop*TotalNumberOfMhDraws);
+    mh_nblck = size(record.LastParameters,1);
+    clear record;
+end
 
 header_width = row_header_width(M_, estim_params_, bayestopt_);
 hpd_interval=[num2str(options_.mh_conf_sig*100), '% HPD interval'];
@@ -68,13 +65,26 @@ skipline(2)
 disp('ESTIMATION RESULTS')
 skipline()
 
-if ~isfield(oo_,'MarginalDensity') || ~isfield(oo_.MarginalDensity,'ModifiedHarmonicMean')
-    [~,oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_);
+if ishssmc(options_)
+    dprintf('Log data density is %f.', oo_.MarginalDensity.hssmc);
+    % Set function handle for GetAllPosteriorDraws
+    getalldraws = @(i) GetAllPosteriorDraws(options_, M_.dname, [], i);
+else
+    if ~isfield(oo_,'MarginalDensity') || (issmc(options_) && ~isfield(oo_.MarginalDensity,'ModifiedHarmonicMean'))
+        [~, oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_);
+    end
+    fprintf('Log data density is %f.', oo_.MarginalDensity.ModifiedHarmonicMean);
+    % Set function handle for GetAllPosteriordraws
+    getalldraws = @(i) GetAllPosteriorDraws(options_, M_.dname, M_.fname, i, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
 end
-fprintf('\nLog data density is %f.\n', oo_.MarginalDensity.ModifiedHarmonicMean);
 
-num_draws=NumberOfDraws*mh_nblck;
-hpd_draws = round((1-options_.mh_conf_sig)*num_draws);
+if ishssmc(options_)
+    num_draws = options_.posterior_sampler_options.hssmc.particles;
+    hpd_draws = round((1-options_.mh_conf_sig)*num_draws);
+else
+    num_draws=NumberOfDraws*mh_nblck;
+    hpd_draws = round((1-options_.mh_conf_sig)*num_draws);
+end
 
 if hpd_draws<2
     fprintf('posterior_moments: There are not enough draws computes to compute HPD Intervals. Skipping their computation.\n')
@@ -93,9 +103,9 @@ if np
     disp(tit2)
     ip = nvx+nvn+ncx+ncn+1;
     for i=1:np
-        if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
-            Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
-            [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
+        if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh) || ishssmc(options_)
+            draws = getalldraws(ip);
+            [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
             name = bayestopt_.name{ip};
             oo_ = Filloo(oo_, name, type, post_mean, hpd_interval, post_median, post_var, post_deciles, density);
         else
@@ -103,8 +113,8 @@ if np
                 name = bayestopt_.name{ip};
                 [post_mean, hpd_interval, post_var] = Extractoo(oo_, name, type);
             catch
-                Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
-                [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
+                draws = getalldraws(ip);
+                [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
                 name = bayestopt_.name{ip};
                 oo_ = Filloo(oo_, name, type, post_mean, hpd_interval, post_median, post_var, post_deciles, density);
             end
@@ -137,8 +147,8 @@ if nvx
     disp(tit2)
     for i=1:nvx
         if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
-            Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
-            [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
+            draws = getalldraws(ip);
+            [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
             k = estim_params_.var_exo(i,1);
             name = M_.exo_names{k};
             oo_ = Filloo(oo_, name, type, post_mean, hpd_interval, post_median, post_var, post_deciles, density);
@@ -149,9 +159,8 @@ if nvx
                 name = M_.exo_names{k};
                 [post_mean, hpd_interval, post_var] = Extractoo(oo_, name, type);
             catch
-                Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
-                [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
-                    posterior_moments(Draws, 1, options_.mh_conf_sig);
+                draws = getalldraws(ip);
+                [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
                 k = estim_params_.var_exo(i,1);
                 name = M_.exo_names{k};
                 oo_ = Filloo(oo_, name, type, post_mean, hpd_interval, post_median, post_var, post_deciles, density);
@@ -181,8 +190,8 @@ if nvn
     ip = nvx+1;
     for i=1:nvn
         if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
-            Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
-            [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
+            draws = getalldraws(ip);
+            [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
             name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
             oo_ = Filloo(oo_, name, type, post_mean, hpd_interval, post_median, post_var, post_deciles, density);
         else
@@ -190,8 +199,8 @@ if nvn
                 name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
                 [post_mean,hpd_interval,post_var] = Extractoo(oo_,name,type);
             catch
-                Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws, mh_nblck);
-                [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws,1,options_.mh_conf_sig);
+                draws = getalldraws(ip);
+                [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws,1,options_.mh_conf_sig);
                 name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
                 oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
             end
@@ -220,8 +229,8 @@ if ncx
     ip = nvx+nvn+1;
     for i=1:ncx
         if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
-            Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
-            [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws,1,options_.mh_conf_sig);
+            draws = getalldraws(ip);
+            [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws,1,options_.mh_conf_sig);
             k1 = estim_params_.corrx(i,1);
             k2 = estim_params_.corrx(i,2);
             name = sprintf('%s,%s', M_.exo_names{k1}, M_.exo_names{k2});
@@ -237,8 +246,8 @@ if ncx
                 NAME = sprintf('%s_%s', M_.exo_names{k1}, M_.exo_names{k2});
                 [post_mean,hpd_interval,post_var] = Extractoo(oo_, NAME, type);
             catch
-                Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
-                [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
+                draws = getalldraws(ip);
+                [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
                 k1 = estim_params_.corrx(i,1);
                 k2 = estim_params_.corrx(i,2);
                 name = sprintf('%s,%s', M_.exo_names{k1}, M_.exo_names{k2});
@@ -259,6 +268,7 @@ if ncx
         TeXEnd(fid, 4, 'correlation of structural shocks');
     end
 end
+
 if ncn
     type = 'measurement_errors_corr';
     if TeX
@@ -270,8 +280,8 @@ if ncn
     ip = nvx+nvn+ncx+1;
     for i=1:ncn
         if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
-            Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws, mh_nblck);
-            [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
+            draws = getalldraws(ip);
+            [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
             k1 = estim_params_.corrn(i,1);
             k2 = estim_params_.corrn(i,2);
             name = sprintf('%s,%s', M_.endo_names{k1}, M_.endo_names{k2});
@@ -285,8 +295,8 @@ if ncn
                 NAME = sprintf('%s_%s', M_.endo_names{k1}, M_.endo_names{k2});
                 [post_mean,hpd_interval,post_var] = Extractoo(oo_, NAME, type);
             catch
-                Draws = GetAllPosteriorDraws(M_.dname, M_.fname, ip, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, mh_nblck);
-                [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(Draws, 1, options_.mh_conf_sig);
+                draws = getalldraws(ip);
+                [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = posterior_moments(draws, 1, options_.mh_conf_sig);
                 k1 = estim_params_.corrn(i,1);
                 k2 = estim_params_.corrn(i,2);
                 name = sprintf('%s,%s', M_.endo_names{k1}, M_.endo_names{k2});
@@ -333,11 +343,8 @@ fprintf(fidTeX, '  & \\multicolumn{3}{c}{Prior}  &  \\multicolumn{4}{c}{Posterio
 fprintf(fidTeX, '  \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-8}\n');
 fprintf(fidTeX, '  & Dist. & Mean  & Stdev. & Mean & Stdev. & HPD inf & HPD sup\\\\\n');
 fprintf(fidTeX, '\\midrule \\endhead \n');
-
 fprintf(fidTeX, '\\bottomrule \\multicolumn{8}{r}{(Continued on next page)} \\endfoot \n');
 fprintf(fidTeX, '\\bottomrule \\endlastfoot \n');
-
-
 fid = fidTeX;
 
 
@@ -375,4 +382,4 @@ hpd_interval = zeros(2,1);
 post_mean = oo.posterior_mean.(type).(name);
 hpd_interval(1) = oo.posterior_hpdinf.(type).(name);
 hpd_interval(2) = oo.posterior_hpdsup.(type).(name);
-post_var = oo.posterior_variance.(type).(name);
+post_var = oo.posterior_variance.(type).(name);

matlab/Herbst_Schorfheide_sampler.m

@@ -1,246 +0,0 @@
-function Herbst_Schorfheide_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
-% function Herbst_Schorfheide_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
-% SMC sampler from JAE 2014 .
-%
-% INPUTS
-%   o TargetFun  [char]     string specifying the name of the objective
-%                           function (posterior kernel).
-%   o xparam1    [double]   (p*1) vector of parameters to be estimated (initial values).
-%   o mh_bounds  [double]   (p*2) matrix defining lower and upper bounds for the parameters.
-%   o dataset_              data structure
-%   o dataset_info          dataset info structure
-%   o options_              options structure
-%   o M_                    model structure
-%   o estim_params_         estimated parameters structure
-%   o bayestopt_            estimation options structure
-%   o oo_                   outputs structure
-%
-% SPECIAL REQUIREMENTS
-%   None.
-%
-% PARALLEL CONTEXT
-% The most computationally intensive part of this function may be executed
-% in parallel. The code suitable to be executed in
-% parallel on multi core or cluster machine (in general a 'for' cycle)
-% has been removed from this function and been placed in the posterior_sampler_core.m funtion.
-%
-% The DYNARE parallel packages comprise a i) set of pairs of Matlab functions that can be executed in
-% parallel and called name_function.m and name_function_core.m and ii) a second set of functions used
-% to manage the parallel computations.
-%
-% This function was the first function to be parallelized. Later, other
-% functions have been parallelized using the same methodology.
-% Then the comments write here can be used for all the other pairs of
-% parallel functions and also for management functions.
-
-% Copyright © 2006-2023 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 <https://www.gnu.org/licenses/>.
-
-
-% Create the tempering schedule
-phi = bsxfun(@power,(bsxfun(@minus,1:1:options_.posterior_sampler_options.HSsmc.nphi,1)/(options_.posterior_sampler_options.HSsmc.nphi-1)),options_.posterior_sampler_options.HSsmc.lambda) ;
-% tuning for MH algorithms matrices
-zhat    = 0 ;                                    % normalization constant
-csim    = zeros(options_.posterior_sampler_options.HSsmc.nphi,1) ;         % scale parameter
-ESSsim  = zeros(options_.posterior_sampler_options.HSsmc.nphi,1) ;         % ESS
-acptsim = zeros(options_.posterior_sampler_options.HSsmc.nphi,1) ;         % average acceptance rate
-% Step 0: Initialization of the sampler
-[ param, tlogpost_i, loglik, bayestopt_] = ...
-    SMC_samplers_initialization(TargetFun, xparam1, mh_bounds, dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,options_.posterior_sampler_options.HSsmc.nparticles);
-weights = ones(options_.posterior_sampler_options.HSsmc.nparticles,1)/options_.posterior_sampler_options.HSsmc.nparticles ;
-% The Herbst and Schorfheide sampler starts here
-for i=2:options_.posterior_sampler_options.HSsmc.nphi
-    % (a) Correction
-    % incremental weights
-    incwt = exp((phi(i)-phi(i-1))*loglik) ;
-    % update weights
-    weights = bsxfun(@times,weights,incwt) ;
-    sum_weights = sum(weights) ;
-    zhat = zhat + log(sum_weights) ;
-    % normalize weights
-    weights = weights/sum_weights ;
-    % (b) Selection
-    ESSsim(i) = 1/sum(weights.^2) ;
-    if (ESSsim(i) < options_.posterior_sampler_options.HSsmc.nparticles/2)
-        indx_resmpl = smc_resampling(weights,rand(1,1),options_.posterior_sampler_options.HSsmc.nparticles) ;
-        param = param(:,indx_resmpl) ;
-        loglik = loglik(indx_resmpl) ;
-        tlogpost_i = tlogpost_i(indx_resmpl) ;
-        weights = ones(options_.posterior_sampler_options.HSsmc.nparticles,1)/options_.posterior_sampler_options.HSsmc.nparticles  ;
-    end
-    % (c) Mutation
-    options_.posterior_sampler_options.HSsmc.c = options_.posterior_sampler_options.HSsmc.c*modified_logit(0.95,0.1,16.0,options_.posterior_sampler_options.HSsmc.acpt-options_.posterior_sampler_options.HSsmc.trgt) ;
-    % Calculate estimates of mean and variance
-    mu = param*weights ;
-    z = bsxfun(@minus,param,mu) ;
-    R = z*(bsxfun(@times,z',weights)) ;
-    Rchol = chol(R)' ;
-    % Mutation
-    if options_.posterior_sampler_options.HSsmc.option_mutation==1
-        [param,tlogpost_i,loglik,options_.posterior_sampler_options.HSsmc.acpt] = mutation_RW(TargetFun,param,tlogpost_i,loglik,phi,i,options_.posterior_sampler_options.HSsmc.c*Rchol,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_) ;
-    elseif options_.posterior_sampler_options.HSsmc.option_mutation==2
-        inv_R = inv(options_.posterior_sampler_options.HSsmc.c^2*R) ;
-        Rdiagchol = sqrt(diag(R)) ;
-        [param,tlogpost_i,loglik,options_.posterior_sampler_options.HSsmc.acpt] = mutation_Mixture(TargetFun,param,tlogpost_i,loglik,phi,i,options_.posterior_sampler_options.HSsmc.c*Rchol,options_.posterior_sampler_options.HSsmc.c*Rdiagchol,inv_R,mu,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_) ;
-    end
-    acptsim(i) = options_.posterior_sampler_options.HSsmc.acpt ;
-    csim(i) = options_.posterior_sampler_options.HSsmc.c ;
-    % print information
-    fprintf(' Iteration = %5.0f / %5.0f \n', i, options_.posterior_sampler_options.HSsmc.nphi);
-    fprintf(' phi = %5.4f \n', phi(i));
-    fprintf(' Neff = %5.4f \n', ESSsim(i));
-    fprintf(' %accept. = %5.4f \n', acptsim(i));
-end
-indx_resmpl = smc_resampling(weights,rand(1,1),options_.posterior_sampler_options.HSsmc.nparticles);
-distrib_param = param(:,indx_resmpl);
-fprintf(' Log_lik = %5.4f \n', zhat);
-
-mean_xparam = mean(distrib_param,2);
-npar = length(xparam1) ;
-%mat_var_cov = bsxfun(@minus,distrib_param,mean_xparam) ;
-%mat_var_cov = (mat_var_cov*mat_var_cov')/(options_.posterior_sampler_options.HSsmc.nparticles-1) ;
-%std_xparam = sqrt(diag(mat_var_cov)) ;
-lb95_xparam = zeros(npar,1) ;
-ub95_xparam = zeros(npar,1) ;
-for i=1:npar
-    temp = sortrows(distrib_param(i,:)') ;
-    lb95_xparam(i) = temp(0.025*options_.posterior_sampler_options.HSsmc.nparticles) ;
-    ub95_xparam(i) = temp(0.975*options_.posterior_sampler_options.HSsmc.nparticles) ;
-end
-
-TeX = options_.TeX;
-
-str = sprintf(' Param. \t Lower Bound (95%%) \t Mean \t Upper Bound (95%%)');
-for l=1:npar
-    [name,~] = get_the_name(l,TeX,M_,estim_params_,options_.varobs);
-    str = sprintf('%s\n %s \t\t %5.4f \t\t %7.5f \t\t %5.4f', str, name, lb95_xparam(l), mean_xparam(l), ub95_xparam(l));
-end
-disp([str])
-disp('')
-
-%% Plot parameters densities
-
-[nbplt,nr,nc,lr,lc,nstar] = pltorg(npar);
-
-if TeX
-    fidTeX = fopen([M_.fname '_param_density.tex'],'w');
-    fprintf(fidTeX,'%% TeX eps-loader file generated by DSMH.m (Dynare).\n');
-    fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
-    fprintf(fidTeX,' \n');
-end
-
-number_of_grid_points = 2^9;      % 2^9 = 512 !... Must be a power of two.
-bandwidth = 0;                    % Rule of thumb optimal bandwidth parameter.
-kernel_function = 'gaussian';     % Gaussian kernel for Fast Fourier Transform approximation.
-plt = 1 ;
-%for plt = 1:nbplt,
-if TeX
-    NAMES = [];
-    TeXNAMES = [];
-end
-hh_fig = dyn_figure(options_.nodisplay,'Name','Parameters Densities');
-for k=1:npar %min(nstar,npar-(plt-1)*nstar)
-    subplot(ceil(sqrt(npar)),floor(sqrt(npar)),k)
-    %kk = (plt-1)*nstar+k;
-    [name,texname] = get_the_name(k,TeX,M_,estim_params_,options_.varobs);
-    optimal_bandwidth = mh_optimal_bandwidth(distrib_param(k,:)',options_.posterior_sampler_options.HSsmc.nparticles,bandwidth,kernel_function);
-    [density(:,1),density(:,2)] = kernel_density_estimate(distrib_param(k,:)',number_of_grid_points,...
-        options_.posterior_sampler_options.HSsmc.nparticles,optimal_bandwidth,kernel_function);
-    plot(density(:,1),density(:,2));
-    hold on
-    if TeX
-        title(texname,'interpreter','latex')
-    else
-        title(name,'interpreter','none')
-    end
-    hold off
-    axis tight
-    drawnow
-end
-dyn_saveas(hh_fig,[ M_.fname '_param_density' int2str(plt) ],options_.nodisplay,options_.graph_format);
-if TeX && any(strcmp('eps',cellstr(options_.graph_format)))
-    % TeX eps loader file
-    fprintf(fidTeX,'\\begin{figure}[H]\n');
-    fprintf(fidTeX,'\\centering \n');
-    fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%_param_density%s}\n',min(k/floor(sqrt(npar)),1),M_.fname,int2str(plt));
-    fprintf(fidTeX,'\\caption{Parameter densities based on the Herbst/Schorfheide sampler.}');
-    fprintf(fidTeX,'\\label{Fig:ParametersDensities:%s}\n',int2str(plt));
-    fprintf(fidTeX,'\\end{figure}\n');
-    fprintf(fidTeX,' \n');
-end
-%end
-
-function [param,tlogpost_i,loglik,acpt] = mutation_RW(TargetFun,param,tlogpost_i,loglik,phi,i,cRchol,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_)
-acpt = 0.0 ;
-tlogpost_i = tlogpost_i + (phi(i)-phi(i-1))*loglik ;
-for j=1:options_.posterior_sampler_options.HSsmc.nparticles
-    validate= 0;
-    while validate==0
-        candidate = param(:,j) + cRchol*randn(size(param,1),1) ;
-        if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
-            [tlogpostx,loglikx] = tempered_likelihood(TargetFun,candidate,phi(i),dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
-            if isfinite(loglikx) % if returned log-density is not Inf or Nan (penalized value)
-                validate = 1;
-                if rand(1,1)<exp(tlogpostx-tlogpost_i(j)) % accept
-                    acpt = acpt + 1 ;
-                    param(:,j) = candidate;
-                    loglik(j) = loglikx;
-                    tlogpost_i(j) = tlogpostx;
-                end
-            end
-        end
-    end
-end
-acpt = acpt/options_.posterior_sampler_options.HSsmc.nparticles;
-
-function [param,tlogpost_i,loglik,acpt] = mutation_Mixture(TargetFun,param,tlogpost_i,loglik,phi,i,cRchol,cRdiagchol,invR,mu,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_)
-acpt = 0.0 ;
-tlogpost_i = tlogpost_i + (phi(i)-phi(i-1))*loglik ;
-for j=1:options_.posterior_sampler_options.HSsmc.nparticles
-    qx = 0 ;
-    q0 = 0 ;
-    alpind = rand(1,1) ;
-    validate= 0;
-    while validate==0
-        if alpind<options_.posterior_sampler_options.HSsmc.alp % RW, no need to modify qx and q0
-            candidate = param(:,j) + cRchol*randn(size(param,1),1);
-        elseif alpind<options_.posterior_sampler_options.HSsmc.alp + (1-options_.posterior_sampler_options.HSsmc.alp/2) % random walk with diagonal cov no need to modify qx and q0
-            candidate = param(:,j) + cRdiagchol*randn(size(param,1),1);
-        else % Proposal densities
-            candidate = mu + cRchol*randn(size(param,1),1);
-            qx = -.5*(candidate-mu)'*invR*(candidate-mu) ; % no need of the constants in the acceptation rule
-            q0 = -.5*(param(:,j)-mu)'*invR*(param(:,j)-mu) ;
-        end
-        if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
-            [tlogpostx,loglikx] = tempered_likelihood(TargetFun,candidate,phi(i),dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
-            if isfinite(loglikx) % if returned log-density is not Inf or Nan (penalized value)
-                validate = 1;
-                if rand(1,1)<exp((tlogpostx-qx)-(tlogpost_i(j)-q0)) % accept
-                    acpt = acpt + 1 ;
-                    param(:,j) = candidate;
-                    loglik(j) = loglikx;
-                    tlogpost_i(j) = tlogpostx;
-                end
-            end
-        end
-    end
-end
-acpt = acpt/options_.posterior_sampler_options.HSsmc.nparticles;
-
-function x = modified_logit(a,b,c,d)
-tmp = exp(c*d) ;
-x =  a + b*tmp/(1 + tmp) ;

matlab/PlotPosteriorDistributions.m

@@ -31,6 +31,7 @@ function oo_ = PlotPosteriorDistributions(estim_params_, M_, options_, bayestopt
 %
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
+
 latexDirectoryName = CheckPath('latex',M_.dname);
 graphDirectoryName = CheckPath('graphs',M_.dname);
 
@@ -72,7 +73,7 @@ for i=1:npar
         f1 = oo_.posterior_density.shocks_std.(name)(:,2);
         oo_.prior_density.shocks_std.(name)(:,1) = x2;
         oo_.prior_density.shocks_std.(name)(:,2) = f2;
-        if ~options_.mh_posterior_mode_estimation
+        if ~issmc(options_) && ~options_.mh_posterior_mode_estimation
             pmod = oo_.posterior_mode.shocks_std.(name);
         end
     elseif i <= nvx+nvn
@@ -81,7 +82,7 @@ for i=1:npar
         f1 = oo_.posterior_density.measurement_errors_std.(name)(:,2);
         oo_.prior_density.measurement_errors_std.(name)(:,1) = x2;
         oo_.prior_density.measurement_errors_std.(name)(:,2) = f2;
-        if ~options_.mh_posterior_mode_estimation
+        if ~issmc(options_) && ~options_.mh_posterior_mode_estimation
             pmod = oo_.posterior_mode.measurement_errors_std.(name);
         end
     elseif i <= nvx+nvn+ncx
@@ -93,7 +94,7 @@ for i=1:npar
         f1 = oo_.posterior_density.shocks_corr.(name)(:,2);
         oo_.prior_density.shocks_corr.(name)(:,1) = x2;
         oo_.prior_density.shocks_corr.(name)(:,2) = f2;
-        if ~options_.mh_posterior_mode_estimation
+        if ~issmc(options_) && ~options_.mh_posterior_mode_estimation
             pmod = oo_.posterior_mode.shocks_corr.(name);
         end
     elseif i <= nvx+nvn+ncx+ncn
@@ -105,7 +106,7 @@ for i=1:npar
         f1 = oo_.posterior_density.measurement_errors_corr.(name)(:,2);
         oo_.prior_density.measurement_errors_corr.(name)(:,1) = x2;
         oo_.prior_density.measurement_errors_corr.(name)(:,2) = f2;
-        if ~options_.mh_posterior_mode_estimation
+        if ~issmc(options_) && ~options_.mh_posterior_mode_estimation
             pmod = oo_.posterior_mode.measurement_errors_corr.(name);
         end
     else
@@ -115,7 +116,7 @@ for i=1:npar
         f1 = oo_.posterior_density.parameters.(name)(:,2);
         oo_.prior_density.parameters.(name)(:,1) = x2;
         oo_.prior_density.parameters.(name)(:,2) = f2;
-        if ~options_.mh_posterior_mode_estimation
+        if ~issmc(options_) && ~options_.mh_posterior_mode_estimation
             pmod = oo_.posterior_mode.parameters.(name);
         end
     end
@@ -130,7 +131,7 @@ for i=1:npar
     set(hh_plt, 'color', [0.7 0.7 0.7]);
     hold on;
     plot(x1, f1, '-k', 'linewidth', 2);
-    if ~options_.mh_posterior_mode_estimation
+    if ~issmc(options_) && ~options_.mh_posterior_mode_estimation
         plot([pmod pmod], [0.0 1.1*top0], '--g', 'linewidth', 2);
     end
     box on
@@ -160,4 +161,4 @@ for i=1:npar
         end
         subplotnum = 0;
     end
-end
+end

matlab/SMC_samplers_initialization.m

@@ -1,113 +0,0 @@
-function [ ix2, temperedlogpost, loglik, bayestopt_] = ...
-    SMC_samplers_initialization(TargetFun, xparam1, mh_bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_, NumberOfParticles)
-% function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npar, NumberOfParticles, bayestopt_] = ...
-%     SMC_samplers_initialization(TargetFun, xparam1, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,NumberOfParticles)
-% Draw in prior distribution to initialize samplers.
-%
-% INPUTS
-%   o TargetFun  [char]     string specifying the name of the objective
-%                           function (tempered posterior kernel and likelihood).
-%   o xparam1    [double]   (p*1) vector of parameters to be estimated (initial values).
-%   o mh_bounds  [double]   (p*2) matrix defining lower and upper bounds for the parameters.
-%   o dataset_              data structure
-%   o dataset_info          dataset info structure
-%   o options_              options structure
-%   o M_                    model structure
-%   o estim_params_         estimated parameters structure
-%   o bayestopt_            estimation options structure
-%   o oo_                   outputs structure
-%
-% OUTPUTS
-%   o ix2                   [double]    (NumberOfParticles*npar) vector of starting points for different chains
-%   o ilogpo2               [double]    (NumberOfParticles*1) vector of initial posterior values for different chains
-%   o iloglik2              [double]    (NumberOfParticles*1) vector of initial likelihood values for different chains
-%   o ModelName             [string]    name of the mod-file
-%   o MetropolisFolder      [string]    path to the Metropolis subfolder
-%   o bayestopt_            [structure] estimation options structure
-%
-% SPECIAL REQUIREMENTS
-%   None.
-
-% Copyright © 2006-2022 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 <https://www.gnu.org/licenses/>.
-
-%Initialize outputs
-ix2 = [];
-ilogpo2 = [];
-iloglik2 = [];
-ModelName = [];
-MetropolisFolder = [];
-
-ModelName = M_.fname;
-if ~isempty(M_.bvar)
-    ModelName = [ModelName '_bvar'];
-end
-
-MetropolisFolder = CheckPath('dsmh',M_.dname);
-BaseName = [MetropolisFolder filesep ModelName];
-
-npar  = length(xparam1);
-
-% Here we start a new DS Metropolis-Hastings, previous draws are discarded.
-disp('Estimation:: Initialization...')
-% Delete old dsmh files if any...
-files = dir([BaseName '_dsmh*_blck*.mat']);
-%if length(files)
-%    delete([BaseName '_dsmh*_blck*.mat']);
-%    disp('Estimation::smc: Old dsmh-files successfully erased!')
-%end
-% Delete old log file.
-file = dir([ MetropolisFolder '/dsmh.log']);
-%if length(file)
-%    delete([ MetropolisFolder '/dsmh.log']);
-%    disp('Estimation::dsmh: Old dsmh.log file successfully erased!')
-%    disp('Estimation::dsmh: Creation of a new dsmh.log file.')
-%end
-fidlog = fopen([MetropolisFolder '/dsmh.log'],'w');
-fprintf(fidlog,'%% DSMH log file (Dynare).\n');
-fprintf(fidlog,['%% ' datestr(now,0) '.\n']);
-fprintf(fidlog,' \n\n');
-fprintf(fidlog,'%% Session 1.\n');
-fprintf(fidlog,' \n');
-prior_draw(bayestopt_,options_.prior_trunc);
-% Find initial values for the NumberOfParticles chains...
-options_=set_dynare_seed_local_options(options_,'default');
-fprintf(fidlog,['  Initial values of the parameters:\n']);
-disp('Estimation::dsmh: Searching for initial values...');
-ix2 = zeros(npar,NumberOfParticles);
-temperedlogpost = zeros(NumberOfParticles,1);
-loglik = zeros(NumberOfParticles,1);
-%stderr = sqrt(bsxfun(@power,mh_bounds.ub-mh_bounds.lb,2)/12)/10;
-for j=1:NumberOfParticles
-    validate = 0;
-    while validate == 0
-    	candidate = prior_draw()';
-%        candidate = xparam1(:) + 0.001*randn(npar,1);%bsxfun(@times,stderr,randn(npar,1)) ;
-        if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
-            ix2(:,j) = candidate ;
-            [temperedlogpost(j),loglik(j)] = tempered_likelihood(TargetFun,candidate,0.0,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
-            if isfinite(loglik(j)) % if returned log-density is Inf or Nan (penalized value)
-                validate = 1;
-            end
-        end
-    end
-end
-fprintf(fidlog,' \n');
-disp('Estimation:: Initial values found!')
-skipline()
-
-

matlab/check_posterior_sampler_options.m

@@ -1,20 +1,17 @@
-function [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, fname, dname, options_, bounds, bayestopt_,outputFolderName)
-% function [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, fname, dname, options_, bounds, bayestopt_,outputFolderName)
-% initialization of posterior samplers
+function [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, fname, dname, options_, bounds, bayestopt_, outputFolderName)
+
+% Initialization of posterior samplers
 %
 % INPUTS
-%   posterior_sampler_options:       posterior sampler options
-%   fname:          name of the mod-file
-%   dname:          name of directory with metropolis folder
-%   options_:       structure storing the options
-%   bounds:         structure containing prior bounds
-%   bayestopt_:     structure storing information about priors
-
+% - posterior_sampler_options   [struct]       posterior sampler options
+% - options_                    [struct]       options
+% - bounds                      [struct]       prior bounds
+% - bayestopt_                  [struct]       information about priors
+%
 % OUTPUTS
-%   posterior_sampler_options:       checked posterior sampler options
-%   options_:       structure storing the options
-%   bayestopt_:     structure storing information about priors
-%   outputFolderName: string of folder to store mat files
+% - posterior_sampler_options   [struct]       checked posterior sampler options (updated)
+% - options_                    [struct]       options (updated)
+% - bayestopt_                  [struct]       information about priors (updated)
 %
 % SPECIAL REQUIREMENTS
 %   none
@@ -40,15 +37,17 @@ if nargin < 7
     outputFolderName = 'Output';
 end
 
-init=0;
+init = false;
 if isempty(posterior_sampler_options)
-    init=1;
+    init = true;
 end
 
 if init
     % set default options and user defined options
     posterior_sampler_options.posterior_sampling_method = options_.posterior_sampler_options.posterior_sampling_method;
-    posterior_sampler_options.bounds = bounds;
+    if ~issmc(options_)
+        posterior_sampler_options.bounds = bounds;
+    end
 
     switch posterior_sampler_options.posterior_sampling_method
 
@@ -227,7 +226,6 @@ if init
             end
         end
 
-
       case 'slice'
         posterior_sampler_options.parallel_bar_refresh_rate=1;
         posterior_sampler_options.serial_bar_refresh_rate=1;
@@ -342,50 +340,120 @@ if init
         end
 
         % moreover slice must be associated to:
-        %     options_.mh_posterior_mode_estimation = false;
-        % this is done below, but perhaps preprocessing should do this?
+            %     options_.mh_posterior_mode_estimation = false;
+            % this is done below, but perhaps preprocessing should do this?
 
-        if ~isempty(posterior_sampler_options.mode)
-            % multimodal case
-            posterior_sampler_options.rotated = 1;
-            posterior_sampler_options.WR=[];
-        end
-        %     posterior_sampler_options = set_default_option(posterior_sampler_options,'mode_files',[]);
+            if ~isempty(posterior_sampler_options.mode)
+                % multimodal case
+                posterior_sampler_options.rotated = 1;
+                posterior_sampler_options.WR=[];
+            end
+            %     posterior_sampler_options = set_default_option(posterior_sampler_options,'mode_files',[]);
 
 
-        posterior_sampler_options.W1=posterior_sampler_options.initial_step_size*(bounds.ub-bounds.lb);
-        if options_.load_mh_file
-            posterior_sampler_options.slice_initialize_with_mode = 0;
-        else
-            if ~posterior_sampler_options.slice_initialize_with_mode
-                posterior_sampler_options.invhess=[];
+            posterior_sampler_options.W1=posterior_sampler_options.initial_step_size*(bounds.ub-bounds.lb);
+            if options_.load_mh_file
+                posterior_sampler_options.slice_initialize_with_mode = 0;
+            else
+                if ~posterior_sampler_options.slice_initialize_with_mode
+                    posterior_sampler_options.invhess=[];
+                end
+            end
+
+            if ~isempty(posterior_sampler_options.mode_files) % multimodal case
+                modes = posterior_sampler_options.mode_files; % these can be also mean files from previous parallel slice chains
+                load(modes, 'xparams')
+                if size(xparams,2)<2
+                    error(['check_posterior_sampler_options:: Variable xparams loaded in file <' modes '> has size [' int2str(size(xparams,1)) 'x' int2str(size(xparams,2)) ']: it must contain at least two columns, to allow multi-modal sampling.'])
+                end
+                for j=1:size(xparams,2)
+                    mode(j).m=xparams(:,j);
+                end
+                posterior_sampler_options.mode = mode;
+                posterior_sampler_options.rotated = 1;
+                posterior_sampler_options.WR=[];
+            end
+
+          case 'hssmc'
+
+            % default options
+            posterior_sampler_options = add_fields_(posterior_sampler_options, options_.posterior_sampler_options.hssmc);
+
+            % user defined options
+            if ~isempty(options_.posterior_sampler_options.sampling_opt)
+                options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
+                for i=1:rows(options_list)
+                    switch options_list{i,1}
+                      case 'target'
+                        posterior_sampler_options.target = options_list{i,2};
+                      case 'steps'
+                        posterior_sampler_options.steps = options_list{i,2};
+                      case 'scale'
+                        posterior_sampler_options.scale = options_list{i,2};
+                      case 'particles'
+                        posterior_sampler_options.particles = options_list{i,2};
+                      case 'lambda'
+                        posterior_sampler_options.lambda = options_list{i,2};
+                      otherwise
+                        warning(['hssmc: Unknown option (' options_list{i,1} ')!'])
+                    end
+                end
+            end
+
+            options_.mode_compute = 0;
+            options_.cova_compute = 0;
+            options_.mh_replic = 0;
+            options_.mh_posterior_mode_estimation = false;
+
+      case 'dsmh'
+
+        % default options
+        posterior_sampler_options = add_fields_(posterior_sampler_options, options_.posterior_sampler_options.dsmh);
+
+        % user defined options
+        if ~isempty(options_.posterior_sampler_options.sampling_opt)
+            options_list = read_key_value_string(options_.posterior_sampler_options.sampling_opt);
+            for i=1:rows(options_list)
+                switch options_list{i,1}
+                  case 'proposal_distribution'
+                    if ~(strcmpi(options_list{i,2}, 'rand_multivariate_student') || ...
+                         strcmpi(options_list{i,2}, 'rand_multivariate_normal'))
+                        error(['initial_estimation_checks:: the proposal_distribution option to estimation takes either ' ...
+                               'rand_multivariate_student or rand_multivariate_normal as options']);
+                    else
+                        posterior_sampler_options.proposal_distribution=options_list{i,2};
+                    end
+                  case 'student_degrees_of_freedom'
+                    if options_list{i,2} <= 0
+                        error('initial_estimation_checks:: the student_degrees_of_freedom takes a positive integer argument');
+                    else
+                        posterior_sampler_options.student_degrees_of_freedom=options_list{i,2};
+                    end
+                  case 'save_tmp_file'
+                    posterior_sampler_options.save_tmp_file = options_list{i,2};
+                  case 'number_of_particles'
+                    posterior_sampler_options.particles = options_list{i,2};
+                  otherwise
+                    warning(['rwmh_sampler: Unknown option (' options_list{i,1} ')!'])
+                end
             end
         end
 
-        if ~isempty(posterior_sampler_options.mode_files) % multimodal case
-            modes = posterior_sampler_options.mode_files; % these can be also mean files from previous parallel slice chains
-            load(modes, 'xparams')
-            if size(xparams,2)<2
-                error(['check_posterior_sampler_options:: Variable xparams loaded in file <' modes '> has size [' int2str(size(xparams,1)) 'x' int2str(size(xparams,2)) ']: it must contain at least two columns, to allow multi-modal sampling.'])
-            end
-            for j=1:size(xparams,2)
-                mode(j).m=xparams(:,j);
-            end
-            posterior_sampler_options.mode = mode;
-            posterior_sampler_options.rotated = 1;
-            posterior_sampler_options.WR=[];
+        options_.mode_compute = 0;
+        options_.cova_compute = 0;
+        options_.mh_replic = 0;
+        options_.mh_posterior_mode_estimation = true;
+
+          otherwise
+            error('check_posterior_sampler_options:: Unknown posterior_sampling_method option %s ',posterior_sampler_options.posterior_sampling_method);
         end
 
-      otherwise
-        error('check_posterior_sampler_options:: Unknown posterior_sampling_method option %s ',posterior_sampler_options.posterior_sampling_method);
-    end
-
-    return
+        return
 end
 
 % here are all samplers requiring a proposal distribution
 if ~strcmp(posterior_sampler_options.posterior_sampling_method,'slice')
-    if ~options_.cova_compute && ~(options_.load_mh_file && posterior_sampler_options.use_mh_covariance_matrix) 
+    if ~options_.cova_compute && ~(options_.load_mh_file && posterior_sampler_options.use_mh_covariance_matrix)
         if strcmp('hessian',options_.MCMC_jumping_covariance)
         skipline()
         disp('check_posterior_sampler_options:: I cannot start the MCMC because the Hessian of the posterior kernel at the mode was not computed')

matlab/compute_mh_covariance_matrix.m

@@ -1,23 +1,19 @@
-function [posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at_the_mode] = compute_mh_covariance_matrix(bayestopt_,fname,dname,outputFolderName)
-% function [posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at_the_mode] = compute_mh_covariance_matrix(bayestopt_,fname,dname,outputFolderName)
+function [mean, covariance, mode, kernel_at_the_mode] = compute_mh_covariance_matrix(names, fname, dname, outputFolderName)
+
 % Estimation of the posterior covariance matrix, posterior mean, posterior mode and evaluation of the posterior kernel at the
-% estimated mode, using the draws from a metropolis-hastings. The estimated posterior mode and covariance matrix are saved in
-% a file <fname>_mh_mode.mat.
+% estimated mode, using posterior draws from a metropolis-hastings.
 %
 % INPUTS
-%   o  bayestopt_                    [struct]  characterizing priors
-%   o  fname                         [string]  name of model
-%   o  dname                         [string]  name of directory with metropolis folder
-%   o  outputFolderName              [string]  name of directory to store results
+% - names                    [cell]     n×1 cell array of row char arrays, names of the estimated parameters.
+% - fname                    [char]     name of the model
+% - dname                    [char]     name of subfolder with output files
+% - outputFolderName         [char]     name of directory to store results
 %
 % OUTPUTS
-%   o  posterior_mean                [double]  (n*1) vector, posterior expectation of the parameters.
-%   o  posterior_covariance          [double]  (n*n) matrix, posterior covariance of the parameters (computed from previous metropolis hastings).
-%   o  posterior_mode                [double]  (n*1) vector, posterior mode of the parameters.
-%   o  posterior_kernel_at_the_mode  [double]  scalar.
-%
-% SPECIAL REQUIREMENTS
-%   None.
+% - mean                     [double]   n×1 vector, posterior expectation of the parameters.
+% - covariance               [double]   n×n matrix, posterior covariance of the parameters.
+% - mode                     [double]   n×1 vector, posterior mode of the parameters.
+% - kernel_at_the_mode       [double]   scalar, value of the posterior kernel at the mode.
 
 % Copyright © 2006-2023 Dynare Team
 %
@@ -35,9 +31,7 @@ function [posterior_mean,posterior_covariance,posterior_mode,posterior_kernel_at
 %
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
-if nargin < 4
-    outputFolderName = 'Output';
-end
+
 MetropolisFolder = CheckPath('metropolis',dname);
 BaseName = [MetropolisFolder filesep fname];
 
@@ -49,29 +43,24 @@ TotalNumberOfMhFiles = sum(record.MhDraws(:,2));
 
 [nblck, n] = size(record.LastParameters);
 
-posterior_kernel_at_the_mode = -Inf;
-posterior_mean = zeros(n,1);
-posterior_mode = NaN(n,1);
-posterior_covariance = zeros(n,n);
+kernel_at_the_mode = -Inf;
+mean = zeros(n,1);
+mode = NaN(n,1);
+covariance = zeros(n,n);
 offset = 0;
 
 for b=1:nblck
     first_line = FirstLine;
     for n = FirstMhFile:TotalNumberOfMhFiles
         load([ BaseName '_mh' int2str(n) '_blck' int2str(b) '.mat'],'x2','logpo2');
-        [tmp,idx] = max(logpo2);
-        if tmp>posterior_kernel_at_the_mode
-            posterior_kernel_at_the_mode = tmp;
-            posterior_mode = x2(idx,:);
+        [tmp, idx] = max(logpo2);
+        if tmp>kernel_at_the_mode
+            kernel_at_the_mode = tmp;
+            mode = x2(idx,:);
         end
-        [posterior_mean,posterior_covariance,offset] = recursive_moments(posterior_mean,posterior_covariance,x2(first_line:end,:),offset);
+        [mean, covariance, offset] = recursive_moments(mean, covariance, x2(first_line:end,:), offset);
         first_line = 1;
     end
 end
 
-xparam1 = posterior_mode';
-hh = inv(posterior_covariance);
-fval = posterior_kernel_at_the_mode;
-parameter_names = bayestopt_.name;
-
-save([dname filesep outputFolderName filesep fname '_mh_mode.mat'],'xparam1','hh','fval','parameter_names');
+mode = transpose(mode);

matlab/compute_posterior_covariance_matrix.m

@@ -0,0 +1,65 @@
+function [mu, covariance, mode, kernel_at_the_mode] = compute_posterior_covariance_matrix(names, fname, dname, options_, outputFolderName)
+
+% Estimation of the posterior covariance matrix, posterior mean, posterior mode and evaluation of the posterior kernel at the
+% estimated mode, using posterior draws from a metropolis-hastings. The estimated posterior mode and covariance matrix are saved in
+% a file <fname>_mh_mode.mat, hssmc_mode.mat or dsmh__mode.mat under <dname>/<outputFolderName>/.
+%
+% INPUTS
+% - names                    [cell]     n×1 cell array of row char arrays, names of the estimated parameters.
+% - fname                    [char]     name of the model
+% - dname                    [char]     name of subfolder with output files
+% - outputFolderName         [char]     name of directory to store results
+%
+% OUTPUTS
+% - mean                     [double]   n×1 vector, posterior expectation of the parameters.
+% - covariance               [double]   n×n matrix, posterior covariance of the parameters.
+% - mode                     [double]   n×1 vector, posterior mode of the parameters.
+% - kernel_at_the_mode       [double]   scalar, value of the posterior kernel at the mode.
+
+% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
+
+
+if nargin<5
+    outputFolderName = 'Output';
+end
+
+if ishssmc(options_)
+    % Load draws from the posterior distribution
+    pfiles = dir(sprintf('%s/hssmc/particles-*.mat', dname));
+    posterior = load(sprintf('%s/hssmc/particles-%u-%u.mat', dname, length(pfiles), length(pfiles)));
+    % Get the posterior mode
+    [kernel_at_the_mode, id] = max(posterior.tlogpostkernel);
+    mode = posterior.particles(:,id);
+    % Compute the posterior mean
+    mu = sum(posterior.particles, 2)/length(posterior.tlogpostkernel);
+    % Compute the posterior covariance
+    covariance = (posterior.particles-mu)*(posterior.particles-mu)'/length(posterior.tlogpostkernel);
+else
+    [mu, covariance, mode, kernel_at_the_mode] = compute_mh_covariance_matrix(names, fname, dname, outputFolderName);
+end
+
+xparam1 = mode;
+hh = inv(covariance);
+fval = kernel_at_the_mode;
+parameter_names = names;
+
+if ishssmc(options_)
+    save(sprintf('%s/%s/hssmc_mode.mat', dname, outputFolderName), 'xparam1', 'hh', 'fval', 'parameter_names');
+else
+    save(sprintf('%s/%s/%s_mh_mode.mat', dname, outputFolderName, fname), 'xparam1', 'hh', 'fval', 'parameter_names');
+end

matlab/convergence_diagnostics/mcmc_diagnostics.m

@@ -79,7 +79,7 @@ for jj = 1:npar
         par_name_temp = get_the_name(jj, options_.TeX, M_, estim_params_, options_.varobs);
         param_name = vertcat(param_name, par_name_temp);
     end
-    Draws = GetAllPosteriorDraws(M_.dname, M_.fname, jj, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck);
+    Draws = GetAllPosteriorDraws(options_, M_.dname, M_.fname, jj, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck);
     Draws = reshape(Draws, [NumberOfDraws nblck]);
     Nc = min(1000, NumberOfDraws/2);
     for ll = 1:nblck

matlab/default_option_values.m

@@ -451,6 +451,7 @@ options_.nk = 1;
 options_.noconstant = false;
 options_.nodiagnostic = false;
 options_.mh_posterior_mode_estimation = false;
+options_.smc_posterior_mode_estimation = false;
 options_.prefilter = 0;
 options_.presample = 0;
 options_.prior_trunc = 1e-10;
@@ -463,7 +464,7 @@ options_.use_mh_covariance_matrix = false;
 options_.gradient_method = 2; %used by csminwel and newrat
 options_.gradient_epsilon = 1e-6; %used by csminwel and newrat
 options_.posterior_sampler_options.sampling_opt = []; %extended set of options for individual posterior samplers
-                                                      % Random Walk Metropolis-Hastings
+% Random Walk Metropolis-Hastings
 options_.posterior_sampler_options.posterior_sampling_method = 'random_walk_metropolis_hastings';
 options_.posterior_sampler_options.rwmh.proposal_distribution = 'rand_multivariate_normal';
 options_.posterior_sampler_options.rwmh.student_degrees_of_freedom = 3;
@@ -491,23 +492,19 @@ options_.posterior_sampler_options.imh.proposal_distribution = 'rand_multivariat
 options_.posterior_sampler_options.imh.use_mh_covariance_matrix=0;
 options_.posterior_sampler_options.imh.save_tmp_file=0;
 % Herbst and Schorfeide SMC Sampler
-%options_.posterior_sampler = 'Herbst_Schorfheide' ;
-options_.posterior_sampler_options.HSsmc.nphi= 25 ;
-options_.posterior_sampler_options.HSsmc.lambda = 2 ;
-options_.posterior_sampler_options.HSsmc.nparticles = 20000 ;
-options_.posterior_sampler_options.HSsmc.c = 0.5 ;
-options_.posterior_sampler_options.HSsmc.acpt = 0.25 ;
-options_.posterior_sampler_options.HSsmc.trgt = 0.25 ;
-options_.posterior_sampler_options.HSsmc.option_mutation = 1 ;
-options_.posterior_sampler_options.HSsmc.alp = .9 ;
+options_.posterior_sampler_options.hssmc.steps= 25;
+options_.posterior_sampler_options.hssmc.lambda = 2;
+options_.posterior_sampler_options.hssmc.particles = 20000;
+options_.posterior_sampler_options.hssmc.scale = 0.5;
+options_.posterior_sampler_options.hssmc.acpt = 1.00;
+options_.posterior_sampler_options.hssmc.target = 0.25;
 % DSMH: Dynamic Striated Metropolis-Hastings algorithm
-%options_.posterior_sampler = 'DSMH' ;
 options_.posterior_sampler_options.dsmh.H = 25 ;
 options_.posterior_sampler_options.dsmh.N = 20 ;
 options_.posterior_sampler_options.dsmh.G = 10 ;
 options_.posterior_sampler_options.dsmh.K = 50 ;
 options_.posterior_sampler_options.dsmh.lambda1 = 0.1 ;
-options_.posterior_sampler_options.dsmh.nparticles = 20000 ;
+options_.posterior_sampler_options.dsmh.particles = 20000 ;
 options_.posterior_sampler_options.dsmh.alpha0 = 0.2 ;
 options_.posterior_sampler_options.dsmh.alpha1 = 0.3 ;
 options_.posterior_sampler_options.dsmh.tau = 10 ;
@@ -664,12 +661,6 @@ options_.use_dll = false;
 % model evaluated using bytecode.dll
 options_.bytecode = false;
 
-% if true, use a fixed point method to solve Sylvester equation (for large scale models)
-options_.sylvester_fp = false;
-
-% convergence criteria to solve iteratively a sylvester equations
-options_.sylvester_fixed_point_tol = 1e-12;
-
 % if true, use a fixed point method to solve Lyapunov equation (for large scale models)
 options_.lyapunov_fp = false;
 % if true, use a doubling algorithm to solve Lyapunov equation (for large scale models)

matlab/dprintf.m

@@ -17,4 +17,4 @@ function dprintf(str, varargin)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
-disp(sprintf(str, varargin{:}));
+disp(sprintf(str, varargin{:}));

matlab/dyn_forecast.m

@@ -11,7 +11,7 @@ function forecast = dyn_forecast(var_list,M_,options_,oo_,task,dataset_info)
 %   task:         indicates how to initialize the forecast
 %                 either 'simul' or 'smoother'
 %   dataset_info:   Various informations about the dataset (descriptive statistics and missing observations).
-
+%
 % OUTPUTS
 %   forecast:   structure containing fields
 %                   Mean:       point estimate

matlab/dynare_config.m

@@ -51,6 +51,9 @@ p = {'/../contrib/ms-sbvar/TZcode/MatlabFiles/' ; ...
      '/discretionary_policy/' ; ...
      '/distributions/' ; ...
      '/ep/' ; ...
+     '/estimation/'; ...
+     '/estimation/smc/'; ...
+     '/estimation/resampler/'; ...
      '/gsa/' ; ...
      '/kalman/' ; ...
      '/kalman/likelihood' ; ...

matlab/dynare_estimation_1.m

@@ -31,6 +31,14 @@ function dynare_estimation_1(var_list_,dname)
 
 global M_ options_ oo_ estim_params_ bayestopt_ dataset_ dataset_info
 
+if issmc(options_)
+    options_.mode_compute = 0;
+    options_.mh_replic = 0;
+    options_.mh_recover = false;
+    options_.load_mh_file = false;
+    options_.load_results_after_load_mh = false;
+end
+
 dispString = 'Estimation::mcmc';
 
 if ~exist([M_.dname filesep 'Output'],'dir')
@@ -88,7 +96,9 @@ if options_.order > 1
     end
 end
 
-%% set objective function
+%
+% set objective function
+%
 if ~options_.dsge_var
     if options_.particle.status
         objective_function = str2func('non_linear_dsge_likelihood');
@@ -147,7 +157,10 @@ if ~isempty(estim_params_)
     M_ = set_all_parameters(xparam1,estim_params_,M_);
 end
 
-%% perform initial estimation checks;
+%
+% perform initial estimation checks;
+%
+
 try
     oo_ = initial_estimation_checks(objective_function,xparam1,dataset_,dataset_info,M_,estim_params_,options_,bayestopt_,bounds,oo_);
 catch % if check fails, provide info on using calibration if present
@@ -164,8 +177,11 @@ catch % if check fails, provide info on using calibration if present
     rethrow(e);
 end
 
-%% Run smoother if no estimation or mode-finding are requested
-if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && ~options_.mh_posterior_mode_estimation
+%
+% Run smoother if no estimation or mode-finding are requested
+%
+
+if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && ~options_.mh_posterior_mode_estimation && ~issmc(options_)
     if options_.order==1 && ~options_.particle.status
         if options_.smoother
             if options_.occbin.smoother.status && options_.occbin.smoother.inversion_filter
@@ -210,11 +226,11 @@ if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && ~options_.
     end
 end
 
-%% Estimation of the posterior mode or likelihood mode
+%
+% Estimation of the posterior mode or likelihood mode
+%
 
-
-
-if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
+if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation && ~issmc(options_)
     optimizer_vec = [options_.mode_compute;num2cell(options_.additional_optimizer_steps)];
     for optim_iter = 1:length(optimizer_vec)
         current_optimizer = optimizer_vec{optim_iter};
@@ -238,7 +254,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
                     ana_deriv_old = options_.analytic_derivation;
                     options_.analytic_derivation = 2;
                     [~,~,~,~,hh] = feval(objective_function,xparam1, ...
-                        dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
+                                         dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
                     options_.analytic_derivation = ana_deriv_old;
                 elseif ~isnumeric(current_optimizer) || ~(isequal(current_optimizer,5) && newratflag~=1 && strcmp(func2str(objective_function),'dsge_likelihood'))
                     % enter here if i) not mode_compute_5, ii) if mode_compute_5 and newratflag==1;
@@ -292,16 +308,19 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
     end
 end
 
-if ~options_.mh_posterior_mode_estimation && options_.cova_compute
+if ~options_.mh_posterior_mode_estimation && options_.cova_compute && ~issmc(options_)
     check_hessian_at_the_mode(hh, xparam1, M_, estim_params_, options_, bounds);
 end
 
-%% create mode_check_plots
-if options_.mode_check.status && ~options_.mh_posterior_mode_estimation
+%
+% create mode_check_plots
+%
+
+if options_.mode_check.status && ~options_.mh_posterior_mode_estimation && ~issmc(options_)
     ana_deriv_old = options_.analytic_derivation;
     options_.analytic_derivation = 0;
     mode_check(objective_function,xparam1,hh,options_,M_,estim_params_,bayestopt_,bounds,false,...
-        dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
+               dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
     options_.analytic_derivation = ana_deriv_old;
 end
 
@@ -309,43 +328,38 @@ oo_.posterior.optimization.mode = [];
 oo_.posterior.optimization.Variance = [];
 oo_.posterior.optimization.log_density=[];
 
-invhess=[];
-if ~options_.mh_posterior_mode_estimation
-    oo_.posterior.optimization.mode = xparam1;
-    if exist('fval','var')
-        oo_.posterior.optimization.log_density=-fval;
+invhess = [];
+
+if ~issmc(options_)
+    if ~options_.mh_posterior_mode_estimation
+        oo_.posterior.optimization.mode = xparam1;
+        if exist('fval','var')
+            oo_.posterior.optimization.log_density=-fval;
+        end
+        if options_.cova_compute
+            hsd = sqrt(diag(hh));
+            invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
+            stdh = sqrt(diag(invhess));
+            oo_.posterior.optimization.Variance = invhess;
+        end
+    else
+        variances = bayestopt_.p2.*bayestopt_.p2;
+        idInf = isinf(variances);
+        variances(idInf) = 1;
+        invhess = options_.mh_posterior_mode_estimation*diag(variances);
+        xparam1 = bayestopt_.p5;
+        idNaN = isnan(xparam1);
+        xparam1(idNaN) = bayestopt_.p1(idNaN);
+        outside_bound_pars=find(xparam1 < bounds.lb | xparam1 > bounds.ub);
+        xparam1(outside_bound_pars) = bayestopt_.p1(outside_bound_pars);
     end
-    if options_.cova_compute
-        hsd = sqrt(diag(hh));
-        invhess = inv(hh./(hsd*hsd'))./(hsd*hsd');
-        stdh = sqrt(diag(invhess));
-        oo_.posterior.optimization.Variance = invhess;
-    end
-else
-    variances = bayestopt_.p2.*bayestopt_.p2;
-    idInf = isinf(variances);
-    variances(idInf) = 1;
-    invhess = options_.mh_posterior_mode_estimation*diag(variances);
-    xparam1 = bayestopt_.p5;
-    idNaN = isnan(xparam1);
-    xparam1(idNaN) = bayestopt_.p1(idNaN);
-    outside_bound_pars=find(xparam1 < bounds.lb | xparam1 > bounds.ub);
-    xparam1(outside_bound_pars) = bayestopt_.p1(outside_bound_pars);
 end
 
 if ~options_.cova_compute
     stdh = NaN(length(xparam1),1);
 end
 
-if options_.particle.status && isfield(options_.particle,'posterior_sampler')
-    if strcmpi(options_.particle.posterior_sampler,'Herbst_Schorfheide')
-        Herbst_Schorfheide_sampler(objective_function,xparam1,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state)
-    elseif strcmpi(options_.particle.posterior_sampler,'DSMH')
-        DSMH_sampler(objective_function,xparam1,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state)
-    end
-end
-
-if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
+if ~issmc(options_) && any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
     % display results table and store parameter estimates and standard errors in results
     oo_ = display_estimation_results_table(xparam1, stdh, M_, options_, estim_params_, bayestopt_, oo_, prior_dist_names, 'Posterior', 'posterior');
     % Laplace approximation to the marginal log density:
@@ -366,56 +380,75 @@ if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
         [~,~,~,~,~,~,~,oo_.dsge_var.posterior_mode.PHI_tilde,oo_.dsge_var.posterior_mode.SIGMA_u_tilde,oo_.dsge_var.posterior_mode.iXX,oo_.dsge_var.posterior_mode.prior] =...
             feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
     end
-
-elseif ~any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
+elseif ~issmc(options_) && ~any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
     oo_=display_estimation_results_table(xparam1, stdh, M_, options_, estim_params_, bayestopt_, oo_, prior_dist_names, 'Maximum Likelihood', 'mle');
 end
 
-invhess = set_mcmc_jumping_covariance(invhess, nx, options_.MCMC_jumping_covariance, bayestopt_, 'dynare_estimation_1');
+if ~issmc(options_)
+    invhess = set_mcmc_jumping_covariance(invhess, nx, options_.MCMC_jumping_covariance, bayestopt_, 'dynare_estimation_1');
+end
 
-if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
-        (any(bayestopt_.pshape >0 ) && options_.load_mh_file)  %% not ML estimation
-    %reset bounds as lb and ub must only be operational during mode-finding
-    bounds = set_mcmc_prior_bounds(xparam1, bayestopt_, options_, 'dynare_estimation_1');
-    % Tunes the jumping distribution's scale parameter
-    if options_.mh_tune_jscale.status
-        if strcmp(options_.posterior_sampler_options.posterior_sampling_method, 'random_walk_metropolis_hastings')
-            options_.mh_jscale = tune_mcmc_mh_jscale_wrapper(invhess, options_, M_, objective_function, xparam1, bounds,...
-                dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds, oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
-            bayestopt_.jscale(:) = options_.mh_jscale;
-            fprintf('mh_jscale has been set equal to %s\n', num2str(options_.mh_jscale));
-        else
-            warning('mh_tune_jscale is only available with Random Walk Metropolis Hastings!')
+%
+% Run SMC sampler.
+%
+
+if ishssmc(options_)
+    [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options([], M_.fname, M_.dname, options_, bounds, bayestopt_);
+    options_.posterior_sampler_options.current_options = posterior_sampler_options;
+    oo_.MarginalDensity.hssmc = hssmc(objective_function, bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_);
+elseif isdsmh(options_)
+    dsmh(objective_function, xparam1, bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_)
+end
+
+%
+% Run MCMC and compute posterior statistics.
+%
+
+if issmc(options_) || (any(bayestopt_.pshape>0) && options_.mh_replic) ||  (any(bayestopt_.pshape>0) && options_.load_mh_file) % not ML estimation
+    if ~issmc(options_)
+        % Reset bounds as lb and ub must only be operational during mode-finding
+        bounds = set_mcmc_prior_bounds(xparam1, bayestopt_, options_, 'dynare_estimation_1');
+        % Tune the jumping distribution's scale parameter
+        if options_.mh_tune_jscale.status
+            if strcmp(options_.posterior_sampler_options.posterior_sampling_method, 'random_walk_metropolis_hastings')
+                options_.mh_jscale = tune_mcmc_mh_jscale_wrapper(invhess, options_, M_, objective_function, xparam1, bounds,...
+                                                                 dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, bounds, oo_.dr,oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
+                bayestopt_.jscale(:) = options_.mh_jscale;
+                fprintf('mh_jscale has been set equal to %s\n', num2str(options_.mh_jscale));
+            else
+                warning('mh_tune_jscale is only available with Random Walk Metropolis Hastings!')
+            end
         end
-    end
-    % runs MCMC
-    if options_.mh_replic || options_.load_mh_file
-        posterior_sampler_options = options_.posterior_sampler_options.current_options;
-        posterior_sampler_options.invhess = invhess;
-        [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, M_.fname, M_.dname, options_, bounds, bayestopt_);
-        % store current options in global
-        options_.posterior_sampler_options.current_options = posterior_sampler_options;
-        if options_.mh_replic
-            ana_deriv_old = options_.analytic_derivation;
-            options_.analytic_derivation = 0;
-            posterior_sampler(objective_function,posterior_sampler_options.proposal_distribution,xparam1,posterior_sampler_options,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,dispString);
-            options_.analytic_derivation = ana_deriv_old;
+        % Run MCMC
+        if options_.mh_replic || options_.load_mh_file
+            posterior_sampler_options = options_.posterior_sampler_options.current_options;
+            posterior_sampler_options.invhess = invhess;
+            [posterior_sampler_options, options_, bayestopt_] = check_posterior_sampler_options(posterior_sampler_options, M_.fname, M_.dname, options_, bounds, bayestopt_);
+            % store current options in global
+            options_.posterior_sampler_options.current_options = posterior_sampler_options;
+            if options_.mh_replic
+                ana_deriv_old = options_.analytic_derivation;
+                options_.analytic_derivation = 0;
+                posterior_sampler(objective_function,posterior_sampler_options.proposal_distribution,xparam1,posterior_sampler_options,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,dispString);
+                options_.analytic_derivation = ana_deriv_old;
+            end
         end
+        % Discard first mh_drop percent of the draws:
+        CutSample(M_, options_, dispString);
     end
-    %% Here I discard first mh_drop percent of the draws:
-    CutSample(M_, options_, dispString);
-    if options_.mh_posterior_mode_estimation
-        [~,~,posterior_mode,~] = compute_mh_covariance_matrix(bayestopt_,M_.fname,M_.dname);
-        oo_=fill_mh_mode(posterior_mode',NaN(length(posterior_mode),1),M_,options_,estim_params_,bayestopt_,oo_,'posterior');
+    if options_.mh_posterior_mode_estimation || (issmc(options_) && options_.smc_posterior_mode_estimation)
+        [~, covariance, posterior_mode, ~] = compute_posterior_covariance_matrix(bayestopt_.name, M_.fname, M_.dname, options_);
+        oo_ = fill_mh_mode(posterior_mode, sqrt(diag(covariance)), M_, options_, estim_params_, oo_, 'posterior');
         %reset qz_criterium
-        options_.qz_criterium=qz_criterium_old;
+        options_.qz_criterium = qz_criterium_old;
         return
     else
-        %get stored results if required
-        if options_.load_mh_file && options_.load_results_after_load_mh
-            oo_load_mh=load([M_.dname filesep 'Output' filesep M_.fname '_results'],'oo_');
+        % Get stored results if required
+        if ~issmc(options_) && options_.load_mh_file && options_.load_results_after_load_mh
+            oo_load_mh = load(sprintf('%s/%s/%s_results', M_.dname, 'Output', M_.fname), 'oo_');
         end
-        if ~options_.nodiagnostic
+        % Compute MCMC convergence diagnostics
+        if ~issmc(options_) && ~options_.nodiagnostic
             if (options_.mh_replic>0 || (options_.load_mh_file && ~options_.load_results_after_load_mh))
                 oo_= mcmc_diagnostics(options_, estim_params_, M_,oo_);
             elseif options_.load_mh_file && options_.load_results_after_load_mh
@@ -424,9 +457,11 @@ if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
                 end
             end
         end
-        %% Estimation of the marginal density from the Mh draws:
-        if options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
-            [~,oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_);
+        % Estimation of the marginal density from the Mh draws:
+        if ishssmc(options_) || options_.mh_replic || (options_.load_mh_file && ~options_.load_results_after_load_mh)
+            if ~issmc(options_)
+                [~, oo_] = marginal_density(M_, options_, estim_params_, oo_, bayestopt_);
+            end
             % Store posterior statistics by parameter name
             oo_ = GetPosteriorParametersStatistics(estim_params_, M_, options_, bayestopt_, oo_, prior_dist_names);
             if ~options_.nograph
@@ -435,13 +470,13 @@ if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
             % Store posterior mean in a vector and posterior variance in
             % a matrix
             [oo_.posterior.metropolis.mean,oo_.posterior.metropolis.Variance] ...
-                = GetPosteriorMeanVariance(M_,options_.mh_drop);
+                = GetPosteriorMeanVariance(options_, M_);
         elseif options_.load_mh_file && options_.load_results_after_load_mh
-            %% load fields from previous MCMC run stored in results-file
+            % load fields from previous MCMC run stored in results-file
             field_names={'posterior_mode','posterior_std_at_mode',...% fields set by marginal_density
-                'posterior_mean','posterior_hpdinf','posterior_hpdsup','posterior_median','posterior_variance','posterior_std','posterior_deciles','posterior_density',...% fields set by GetPosteriorParametersStatistics
-                'prior_density',...%fields set by PlotPosteriorDistributions
-                };
+                         'posterior_mean','posterior_hpdinf','posterior_hpdsup','posterior_median','posterior_variance','posterior_std','posterior_deciles','posterior_density',...% fields set by GetPosteriorParametersStatistics
+                         'prior_density',...%fields set by PlotPosteriorDistributions
+                        };
             for field_iter=1:size(field_names,2)
                 if isfield(oo_load_mh.oo_,field_names{1,field_iter})
                     oo_.(field_names{1,field_iter})=oo_load_mh.oo_.(field_names{1,field_iter});
@@ -456,7 +491,9 @@ if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
                 oo_.posterior.metropolis=oo_load_mh.oo_.posterior.metropolis;
             end
         end
-        [error_flag,~,options_]= metropolis_draw(1,options_,estim_params_,M_);
+        if ~issmc(options_)
+            [error_flag, ~, options_]= metropolis_draw(1, options_, estim_params_, M_);
+        end
         if ~(~isempty(options_.sub_draws) && options_.sub_draws==0)
             if options_.bayesian_irf
                 if error_flag
@@ -499,9 +536,9 @@ if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
                     error('%s: Particle Smoothers are not yet implemented.',dispString)
                 end
             end
-        else
-            fprintf('%s: sub_draws was set to 0. Skipping posterior computations.',dispString);
-        end
+            else
+                fprintf('%s: sub_draws was set to 0. Skipping posterior computations.',dispString);
+            end
         xparam1 = get_posterior_parameters('mean',M_,estim_params_,oo_,options_);
         M_ = set_all_parameters(xparam1,estim_params_,M_);
     end
@@ -517,7 +554,7 @@ end
 
 %Run and store classical smoother if needed
 if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.pshape> 0) && options_.load_mh_file)) ...
-        || ~options_.smoother ) && ~options_.partial_information  % to be fixed
+    || ~options_.smoother ) && ~options_.partial_information  % to be fixed
     %% ML estimation, or posterior mode without Metropolis-Hastings or Metropolis without Bayesian smoothed variables
     oo_=save_display_classical_smoother_results(xparam1,M_,oo_,options_,bayestopt_,dataset_,dataset_info,estim_params_);
 end

matlab/dynare_estimation_init.m

@@ -1,8 +1,6 @@
 function [dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_, bounds] = dynare_estimation_init(var_list_, dname, gsa_flag, M_, options_, oo_, estim_params_, bayestopt_)
 
-% function [dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_, bounds] = dynare_estimation_init(var_list_, dname, gsa_flag, M_, options_, oo_, estim_params_, bayestopt_)
-% performs initialization tasks before estimation or
-% global sensitivity analysis
+% Performs initialization tasks before estimation or global sensitivity analysis
 %
 % INPUTS
 %   var_list_:      selected endogenous variables vector
@@ -390,7 +388,7 @@ end
 %set options for old interface from the ones for new interface
 if ~isempty(dataset_)
     options_.nobs = dataset_.nobs;
-    if options_.endogenous_prior 
+    if options_.endogenous_prior
         if ~isnan(dataset_info.missing.number_of_observations) && ~(dataset_info.missing.number_of_observations==0) %missing observations present
             if dataset_info.missing.no_more_missing_observations<dataset_.nobs-10
                 fprintf('\ndynare_estimation_init: There are missing observations in the data.\n')
@@ -537,15 +535,15 @@ end
 
 if options_.occbin.smoother.status && options_.occbin.smoother.inversion_filter
     if ~isempty(options_.nk)
-        fprintf('dynare_estimation_init: the inversion filter does not support filter_step_ahead. Disabling the option.\n')        
+        fprintf('dynare_estimation_init: the inversion filter does not support filter_step_ahead. Disabling the option.\n')
         options_.nk=[];
     end
     if options_.filter_covariance
-        fprintf('dynare_estimation_init: the inversion filter does not support filter_covariance. Disabling the option.\n')        
+        fprintf('dynare_estimation_init: the inversion filter does not support filter_covariance. Disabling the option.\n')
         options_.filter_covariance=false;
     end
     if options_.smoothed_state_uncertainty
-        fprintf('dynare_estimation_init: the inversion filter does not support smoothed_state_uncertainty. Disabling the option.\n')        
+        fprintf('dynare_estimation_init: the inversion filter does not support smoothed_state_uncertainty. Disabling the option.\n')
         options_.smoothed_state_uncertainty=false;
     end
 end

matlab/estimation/resampler/kitagawa.m

@@ -1,6 +1,15 @@
-function x = bseastr(s1,s2)
+function indices = kitagawa(weights, noise)
 
-% Copyright © 2001-2017 Dynare Team
+% Return indices for resampling.
+%
+% INPUTS
+% - weights   [double]    n×1 vector of partcles' weights.
+% - noise     [double]    scalar, uniform random deviates in [0,1]
+%
+% OUTPUTS
+% - indices   [integer]   n×1 vector of indices in [1:n]
+
+% Copyright © 2022-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -17,31 +26,20 @@ function x = bseastr(s1,s2)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
-m = size(s1,1) ;
-x = zeros(m,1) ;
-s1=upper(deblank(s1));
-s2=upper(deblank(s2));
+n= length(weights);
 
-for im = 1:m
-    key = s1(im,:) ;
-    h = size(s2,1) ;
-    l = 1 ;
-    while l <= h
-        mid = round((h+l)/2) ;
-        temp = s2(mid,:) ;
-        if ~ strcmp(key,temp)
-            for i = 1:min(length(key),length(temp))
-                if temp(i) > key(i)
-                    h = mid - 1 ;
-                    break
-                else
-                    l = mid + 1 ;
-                    break
-                end
-            end
-        else
-            x(im) = mid ;
-            break
-        end
+if nargin<2, noise = rand; end
+
+indices = NaN(n, 1);
+
+cweights = cumsum(weights);
+
+wweights = (transpose(0:n-1)+noise)*(1.0/n);
+
+j = 1;
+for i=1:n
+    while wweights(i)>cweights(j)
+        j = j+1;
     end
+    indices(i) = j;
 end

matlab/estimation/smc/dsmh.m

@@ -1,5 +1,5 @@
-function DSMH_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
-% function DSMH_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
+function dsmh(TargetFun, xparam1, mh_bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_)
+
 % Dynamic Striated Metropolis-Hastings algorithm.
 %
 % INPUTS
@@ -33,7 +33,7 @@ function DSMH_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_
 % Then the comments write here can be used for all the other pairs of
 % parallel functions and also for management functions.
 
-% Copyright © 2006-2023 Dynare Team
+% Copyright © 2022-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -50,14 +50,15 @@ function DSMH_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
+opts = options_.posterior_sampler_options.dsmh;
 
 lambda = exp(bsxfun(@minus,options_.posterior_sampler_options.dsmh.H,1:1:options_.posterior_sampler_options.dsmh.H)/(options_.posterior_sampler_options.dsmh.H-1)*log(options_.posterior_sampler_options.dsmh.lambda1));
 c = 0.055 ;
 MM = int64(options_.posterior_sampler_options.dsmh.N*options_.posterior_sampler_options.dsmh.G/10) ;
 
 % Step 0: Initialization of the sampler
-[ param, tlogpost_iminus1, loglik, bayestopt_] = ...
-    SMC_samplers_initialization(TargetFun, xparam1, mh_bounds, dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,options_.posterior_sampler_options.dsmh.nparticles);
+[param, tlogpost_iminus1, loglik, bayestopt_] = ...
+    smc_samplers_initialization(TargetFun, 'dsmh', opts.particles, mh_bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_);
 
 ESS = zeros(options_.posterior_sampler_options.dsmh.H,1) ;
 zhat = 1 ;
@@ -78,20 +79,17 @@ end
 
 weights = exp(loglik*(lambda(end)-lambda(end-1)));
 weights = weights/sum(weights);
-indx_resmpl = smc_resampling(weights,rand(1,1),options_.posterior_sampler_options.dsmh.nparticles);
+indx_resmpl = smc_resampling(weights,rand(1,1),options_.posterior_sampler_options.dsmh.particles);
 distrib_param = param(:,indx_resmpl);
 
 mean_xparam = mean(distrib_param,2);
 npar  = length(xparam1);
-%mat_var_cov = bsxfun(@minus,distrib_param,mean_xparam) ;
-%mat_var_cov = (mat_var_cov*mat_var_cov')/(options_.HSsmc.nparticles-1) ;
-%std_xparam = sqrt(diag(mat_var_cov)) ;
 lb95_xparam = zeros(npar,1) ;
 ub95_xparam = zeros(npar,1) ;
 for i=1:npar
     temp = sortrows(distrib_param(i,:)') ;
-    lb95_xparam(i) = temp(0.025*options_.posterior_sampler_options.dsmh.nparticles) ;
-    ub95_xparam(i) = temp(0.975*options_.posterior_sampler_options.dsmh.nparticles) ;
+    lb95_xparam(i) = temp(0.025*options_.posterior_sampler_options.dsmh.particles) ;
+    ub95_xparam(i) = temp(0.975*options_.posterior_sampler_options.dsmh.particles) ;
 end
 
 TeX = options_.TeX;
@@ -130,9 +128,9 @@ for k=1:npar %min(nstar,npar-(plt-1)*nstar)
     subplot(ceil(sqrt(npar)),floor(sqrt(npar)),k)
     %kk = (plt-1)*nstar+k;
     [name,texname] = get_the_name(k,TeX,M_,estim_params_,options_.varobs);
-    optimal_bandwidth = mh_optimal_bandwidth(distrib_param(k,:)',options_.posterior_sampler_options.dsmh.nparticles,bandwidth,kernel_function);
+    optimal_bandwidth = mh_optimal_bandwidth(distrib_param(k,:)',options_.posterior_sampler_options.dsmh.particles,bandwidth,kernel_function);
     [density(:,1),density(:,2)] = kernel_density_estimate(distrib_param(k,:)',number_of_grid_points,...
-        options_.posterior_sampler_options.dsmh.nparticles,optimal_bandwidth,kernel_function);
+        options_.posterior_sampler_options.dsmh.particles,optimal_bandwidth,kernel_function);
     plot(density(:,1),density(:,2));
     hold on
     if TeX

matlab/estimation/smc/hssmc.m

@@ -0,0 +1,136 @@
+function mdd = hssmc(TargetFun, mh_bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_)
+
+% Sequential Monte-Carlo sampler, Herbst and Schorfheide (JAE, 2014).
+%
+% INPUTS
+% - TargetFun        [char]     string specifying the name of the objective function (posterior kernel).
+% - xparam1          [double]   p×1 vector of parameters to be estimated (initial values).
+% - mh_bounds        [double]   p×2 matrix defining lower and upper bounds for the parameters.
+% - dataset_         [dseries]  sample
+% - dataset_info     [struct]   informations about the dataset
+% - options_         [struct]   dynare's options
+% - M_               [struct]   model description
+% - estim_params_    [struct]   estimated parameters
+% - bayestopt_       [struct]   estimated parameters
+% - oo_              [struct]   outputs
+%
+% SPECIAL REQUIREMENTS
+% None.
+
+% Copyright © 2022-2023 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 <https://www.gnu.org/licenses/>.
+
+    smcopt = options_.posterior_sampler_options.current_options;
+
+    % Set location for the simulated particles.
+    SimulationFolder = CheckPath('hssmc', M_.dname);
+
+    % Define prior distribution
+    Prior = dprior(bayestopt_, options_.prior_trunc);
+
+    % Set function handle for the objective
+    eval(sprintf('%s = @(x) %s(x, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, mh_bounds, oo_.dr , oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state, []);', 'funobj', func2str(TargetFun)));
+
+    mlogit = @(x) .95 + .1/(1 + exp(-16*x)); % Update of the scale parameter
+
+    % Create the tempering schedule
+    phi = ((0:smcopt.steps-1)/(smcopt.steps-1)).^smcopt.lambda;
+
+    % Initialise the estimate of the marginal density of the data
+    mdd = .0;
+
+    % tuning for MH algorithms matrices
+    scl = zeros(smcopt.steps, 1);        % scale parameter
+    ESS = zeros(smcopt.steps, 1);        % ESS
+    acpt = zeros(smcopt.steps, 1);       % average acceptance rate
+
+    % Initialization of the sampler (draws from the prior distribution with finite logged likelihood)
+    t0 = tic;
+    [particles, tlogpostkernel, loglikelihood] = ...
+        smc_samplers_initialization(funobj, 'hssmc', smcopt.particles, Prior, SimulationFolder, smcopt.steps);
+    tt = toc(t0);
+
+    dprintf('#Iter.       lambda        ESS          Acceptance rate   scale   resample   seconds')
+    dprintf('%3u          %5.4f     %9.5E         %5.4f        %4.3f     %3s       %5.2f', 1, 0, 0, 0, 0, 'no', tt)
+
+    weights = ones(smcopt.particles, 1)/smcopt.particles;
+
+    resampled_particle_swarm = false;
+
+    for i=2:smcopt.steps % Loop over the weight on the liklihood (phi)
+        weights = weights.*exp((phi(i)-phi(i-1))*loglikelihood);
+        sweight = sum(weights);
+        weights = weights/sweight;
+        mdd = mdd + log(sweight);
+        ESS(i) = 1.0/sum(weights.^2);
+        if (2*ESS(i) < smcopt.particles) % Resampling
+            resampled_particle_swarm = true;
+            iresample = kitagawa(weights);
+            particles = particles(:,iresample);
+            loglikelihood = loglikelihood(iresample);
+            tlogpostkernel = tlogpostkernel(iresample);
+            weights = ones(smcopt.particles, 1)/smcopt.particles;
+        end
+        smcopt.scale = smcopt.scale*mlogit(smcopt.acpt-smcopt.target); % Adjust the scale parameter
+        scl(i) = smcopt.scale; % Scale parameter (for the jumping distribution in MH mutation step).
+        mu = particles*weights; % Weighted average of the particles.
+        z = particles-mu;
+        R = z*(z'.*weights); % Weighted covariance matrix of the particles.
+        t0 = tic;
+        acpt_ = false(smcopt.particles, 1);
+        tlogpostkernel = tlogpostkernel + (phi(i)-phi(i-1))*loglikelihood;
+        [acpt_, particles, loglikelihood, tlogpostkernel] = ...
+            randomwalk(funobj, chol(R, 'lower'), mu, scl(i), phi(i), acpt_, Prior, particles, loglikelihood, tlogpostkernel);
+        smcopt.acpt = sum(acpt_)/smcopt.particles; % Acceptance rate.
+        tt = toc(t0);
+        acpt(i) = smcopt.acpt;
+        if resampled_particle_swarm
+            dprintf('%3u          %5.4f     %9.5E         %5.4f        %4.3f     %3s       %5.2f', i, phi(i), ESS(i), acpt(i), scl(i), 'yes', tt)
+        else
+            dprintf('%3u          %5.4f     %9.5E         %5.4f        %4.3f     %3s       %5.2f', i, phi(i), ESS(i), acpt(i), scl(i), 'no', tt)
+        end
+        if i==smcopt.steps
+            iresample = kitagawa(weights);
+            particles = particles(:,iresample);
+        end
+        save(sprintf('%s%sparticles-%u-%u.mat', SimulationFolder, filesep(), i, smcopt.steps), 'particles', 'tlogpostkernel', 'loglikelihood')
+        resampled_particle_swarm = false;
+    end
+
+end
+
+function [accept, particles, loglikelihood, tlogpostkernel] = randomwalk(funobj, RR, mu, scale, phi, accept, Prior, particles, loglikelihood, tlogpostkernel)
+
+    parfor j=1:size(particles, 2)
+        notvalid= true;
+        while notvalid
+            candidate = particles(:,j) + scale*(RR*randn(size(mu)));
+            if Prior.admissible(candidate)
+                [tlogpost, loglik] = tempered_likelihood(funobj, candidate, phi, Prior);
+                if isfinite(loglik)
+                    notvalid = false;
+                    if rand<exp(tlogpost-tlogpostkernel(j))
+                        accept(j) = true ;
+                        particles(:,j) = candidate;
+                        loglikelihood(j) = loglik;
+                        tlogpostkernel(j) = tlogpost;
+                    end
+                end
+            end
+        end
+    end
+end

matlab/estimation/smc/isdsmh.m

@@ -1,7 +1,6 @@
-function indx = smc_resampling(weights,noise,number)
-% function indx = smc_resampling(weights,noise,number)
+function bool = isdsmh(options_)
 
-% Copyright © 2022 Dynare Team
+% Copyright © 2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -18,13 +17,9 @@ function indx = smc_resampling(weights,noise,number)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
-    indx = zeros(number,1);
-    cumweights = cumsum(weights);
-    randvec = (transpose(1:number)-1+noise(:))/number;
-    j = 1;
-    for i=1:number
-        while (randvec(i)>cumweights(j))
-            j = j+1;
-        end
-        indx(i) = j;
+bool = false;
+if isfield(options_, 'posterior_sampler_options')
+    if strcmp(options_.posterior_sampler_options.posterior_sampling_method, 'dsmh')
+        bool = true;
     end
+end

matlab/estimation/smc/ishssmc.m

@@ -0,0 +1,25 @@
+function bool = ishssmc(options_)
+
+% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
+
+bool = false;
+if isfield(options_, 'posterior_sampler_options')
+    if strcmp(options_.posterior_sampler_options.posterior_sampling_method, 'hssmc')
+        bool = true;
+    end
+end

matlab/estimation/smc/smc_samplers_initialization.m

@@ -0,0 +1,81 @@
+function [particles, tlogpostkernel, loglikelihood, SimulationFolder] = smc_samplers_initialization(funobj, sampler, n, Prior, SimulationFolder, nsteps)
+
+% Initialize SMC samplers by drawing initial particles in the prior distribution.
+%
+% INPUTS
+% - TargetFun        [char]     string specifying the name of the objective function (posterior kernel).
+% - sampler          [char]     name of the sampler.
+% - n                [integer]  scalar, number of particles.
+% - mh_bounds        [double]   p×2 matrix defining lower and upper bounds for the estimated parameters.
+% - dataset_         [dseries]  sample
+% - dataset_info     [struct]   informations about the dataset
+% - options_         [struct]   dynare's options
+% - M_               [struct]   model description
+% - estim_params_    [struct]   estimated parameters
+% - bayestopt_       [struct]   estimated parameters
+% - oo_              [struct]   outputs
+%
+% OUTPUTS
+% - ix2                   [double]    p×n matrix of particles
+% - ilogpo2               [double]    n×1 vector of posterior kernel values for the particles
+% - iloglik2              [double]    n×1 vector of likelihood values for the particles
+% - ModelName             [string]    name of the mod-file
+% - MetropolisFolder      [string]    path to the Metropolis subfolder
+% - bayestopt_            [structure] estimation options structure
+%
+% SPECIAL REQUIREMENTS
+%   None.
+
+% Copyright © 2022-2023 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 <https://www.gnu.org/licenses/>.
+
+dprintf('Estimation:%s: Initialization...', sampler)
+
+% Delete old mat files storign particles if any...
+matfiles = sprintf('%s%sparticles*.mat', SimulationFolder, filesep());
+files = dir(matfiles);
+if ~isempty(files)
+    delete(matfiles);
+    dprintf('Estimation:%s: Old %s-files successfully erased.', sampler, sampler)
+end
+
+% Simulate a pool of particles characterizing the prior distribution (with the additional constraint that the likelihood is finite)
+set_dynare_seed('default');
+dprintf('Estimation:%s: Searching for initial values...', sampler);
+particles = zeros(Prior.length(), n);
+tlogpostkernel = zeros(n, 1);
+loglikelihood = zeros(n, 1);
+
+t0 = tic;
+parfor j=1:n
+    notvalid = true;
+    while notvalid
+        candidate = Prior.draw();
+        if Prior.admissible(candidate)
+            particles(:,j) = candidate;
+            [tlogpostkernel(j), loglikelihood(j)] = tempered_likelihood(funobj, candidate, 0.0, Prior);
+            if isfinite(loglikelihood(j)) % if returned log-density is Inf or Nan (penalized value)
+                notvalid = false;
+            end
+        end
+    end
+end
+tt = toc(t0);
+
+save(sprintf('%s%sparticles-1-%u.mat', SimulationFolder, filesep(), nsteps), 'particles', 'tlogpostkernel', 'loglikelihood')
+dprintf('Estimation:%s: Initial values found (%.2fs)', sampler, tt)
+skipline()

matlab/estimation/smc/tempered_likelihood.m

@@ -0,0 +1,35 @@
+function [tlogpostkernel,loglikelihood] = tempered_likelihood(postkernelfun, xparam, lambda, Prior)
+
+% Evaluate tempered likelihood (posterior kernel)
+%
+% INPUTS
+% - postkernelfun       [handle]   Function handle for the opposite of the  posterior kernel.
+% - xparam              [double]   n×1 vector of parameters.
+% - lambda              [double]   scalar between 0 and 1, weight on the posterior kernel.
+% - Prior               [dprior]   Prior specification.
+%
+% OUTPUTS
+% - tlogpostkernel      [double]   scalar, value of the tempered posterior kernel.
+% - loglikelihood       [double]   scalar, value of the log likelihood.
+
+% Copyright © 2022-2023 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 <https://www.gnu.org/licenses/>.
+
+logpostkernel = -postkernelfun(xparam);
+logprior = Prior.density(xparam);
+loglikelihood = logpostkernel-logprior;
+tlogpostkernel = lambda*loglikelihood + logprior;

matlab/fill_mh_mode.m

@@ -1,22 +1,22 @@
-function oo_=fill_mh_mode(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_, field_name)
-%function oo_=fill_mh_mode(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_, field_name)
+function oo_ = fill_mh_mode(xparam1, stdh, M_, options_, estim_params_, oo_, field_name)
+
+% Fill oo_.<field_name>.mode and oo_.<field_name>.std_at_mode
 %
 % INPUTS
-%   o xparam1       [double]   (p*1) vector of estimate parameters.
-%   o stdh          [double]   (p*1) vector of estimate parameters.
-%   o M_                        Matlab's structure describing the Model (initialized by dynare, see @ref{M_}).
-%   o estim_params_             Matlab's structure describing the estimated_parameters (initialized by dynare, see @ref{estim_params_}).
-%   o options_                  Matlab's structure describing the options (initialized by dynare, see @ref{options_}).
-%   o bayestopt_                Matlab's structure describing the priors (initialized by dynare, see @ref{bayesopt_}).
-%   o oo_                       Matlab's structure gathering the results (initialized by dynare, see @ref{oo_}).
+% - xparam1         [double]  p×1 vector, estimated posterior mode.
+% - stdh            [double]  p×1 vector, estimated posterior standard deviation.
+% - M_              [struct]  Description of the model.
+% - estim_params_   [struct]  Description of the estimated parameters.
+% - options_        [struct]  Dynare's options.
+% - oo_             [struct]  Estimation and simulation results.
 %
 % OUTPUTS
-%   o oo_                       Matlab's structure gathering the results
+% - oo_                       Matlab's structure gathering the results
 %
 % SPECIAL REQUIREMENTS
 %   None.
 
-% Copyright © 2005-2021 Dynare Team
+% Copyright © 2005-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -42,7 +42,8 @@ np  = estim_params_.np ;  % Number of deep parameters.
 if np
     ip = nvx+nvn+ncx+ncn+1;
     for i=1:np
-        name = bayestopt_.name{ip};
+        k = estim_params_.param_vals(i,1);
+        name = M_.param_names{k};
         oo_.([field_name '_mode']).parameters.(name) = xparam1(ip);
         oo_.([field_name '_std_at_mode']).parameters.(name) = stdh(ip);
         ip = ip+1;
@@ -90,4 +91,4 @@ if ncn
         oo_.([field_name '_std_at_mode']).measurement_errors_corr.(name) = stdh(ip);
         ip = ip+1;
     end
-end
+end

matlab/ftest.m

@@ -1,84 +0,0 @@
-function ftest (s1,s2)
-
-% Copyright © 2001-2017 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 <https://www.gnu.org/licenses/>.
-
-global nvx nvy x y lag1
-
-if size(s1,1) ~= 2
-    error ('Spécifiez deux fichiers pour la comparaison.') ;
-end
-
-for i = 1:2
-    if ~ isempty(find(abs(s1(i,:)) == 46))
-        error ('Entrez les noms de fichiers sans extensions.') ;
-    end
-end
-
-s1 = [s1 [' ';' ']] ;
-file1 = [s1(1,1:min(find(abs(s1(1,:)) == 32))-1) '.BIN'] ;
-file2 = [s1(2,1:min(find(abs(s1(2,:)) == 32))-1) '.BIN'] ;
-
-fid=fopen(file1,'r') ;
-n1 = fread(fid,1,'int') ;
-n2 = fread(fid,1,'int') ;
-n3 = fread(fid,1,'int') ;
-lag1 = fread(fid,4,'int') ;
-nvx = fread(fid,[n1,n3],'int') ;
-x = fread(fid,[n1,n2],'float64') ;
-fclose(fid) ;
-nvx = char(nvx) ;
-
-fid=fopen(file2,'r') ;
-n1 = fread(fid,1,'int') ;
-n2 = fread(fid,1,'int') ;
-n3 = fread(fid,1,'int') ;
-lag2 = fread(fid,4,'int') ;
-nvy = fread(fid,[n1,n3],'int') ;
-y = fread(fid,[n1,n2],'float64') ;
-fclose(fid) ;
-nvy = char(nvy) ;
-
-if size(x,1) ~= size(y,1)
-    error ('FTEST: The two files don''t have the same number of variables.');
-end
-
-for i = 1:size(x,1)
-    if ~ strcmp(nvx(i,:),nvy(i,:))
-        error ('FTEST: The two files don''t have the same  variables.') ;
-    end
-end
-
-if nnz(lag1 - lag2) > 0
-    error ('FTEST: Leads and lags aren''t the same in both files.') ;
-end
-
-j = zeros(size(s2,1),1);
-for i=1:size(s2,1)
-    k = strmatch(s2(i,:),nvx,'exact') ;
-    if isempty(k)
-        t = ['FTEST: Variable ' s2(i) 'doesn''t exist'] ;
-        error (t) ;
-    else
-        j(i) =k;
-    end
-end
-
-y = y(j,:) ;
-x = x(j,:) ;
-
-%06/18/01 MJ replaced beastr by strmatch

matlab/gcompare.m

@@ -1,52 +0,0 @@
-function gcompare(s1,s2)
-% GCOMPARE :    GCOMPARE ( [ 'file1' ; 'file2' ] , [ 'var1' ; 'var2' ...] )
-%               This optional command plots the trajectories of a list of
-%               variables in two different simulations. One plot is drawn
-%               for each variable. The trajectories must have been previously
-%               saved by the instruction DYNASAVE. The simulation in file1
-%               is refered to as the base simulation.
-
-% Copyright © 2001-2017 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 <https://www.gnu.org/licenses/>.
-
-global options_ M_
-global nvx nvy x y lag1
-
-ftest(s1,s2) ;
-
-ix = [1-lag1(1):size(x,2)-lag1(1)]' ;
-i = [lag1(1):size(ix,1)-lag1(2)+1]' ;
-
-if options_.smpl == 0
-    i = [M_.maximum_lag:size(y,2)]' ;
-else
-    i = [options_.smpl(1):options_.smpl(2)]' ;
-end
-
-for k = 1:size(x,1)
-    figure ;
-    plot (ix(i),x(k,i),ix(i),y(k,i)) ;
-    xlabel (['Periods']) ;
-    title (['Variable ' s2(k,:)]) ;
-    l = min(i) + 1;
-    ll = max(i) - 1 ;
-    text (l,x(k,l),s1(1,:)) ;
-    text (ll,y(k,ll),s1(2,:)) ;
-end
-
-% 06/18/01 MJ corrected treatment of options_.smpl
-% 06/24/01 MJ removed color specification

matlab/get_Hessian.m

@@ -1,255 +0,0 @@
-function [Hess] = get_Hessian(T,R,Q,H,P,Y,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P,start,mf,kalman_tol,riccati_tol)
-% function [Hess] = get_Hessian(T,R,Q,H,P,Y,DT,DYss,DOm,DH,DP,D2T,D2Yss,D2Om,D2H,D2P,start,mf,kalman_tol,riccati_tol)
-%
-% computes the hessian matrix of the log-likelihood function of
-% a state space model (notation as in kalman_filter.m in DYNARE
-% Thanks to  Nikolai Iskrev
-%
-% NOTE: the derivative matrices (DT,DR ...) are 3-dim. arrays with last
-% dimension equal to the number of structural parameters
-% NOTE: the derivative matrices (D2T,D2Om ...) are 4-dim. arrays with last
-% two dimensions equal to the number of structural parameters
-
-% Copyright © 2011-2017 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 <https://www.gnu.org/licenses/>.
-
-
-k = size(DT,3);                                 % number of structural parameters
-smpl = size(Y,2);                               % Sample size.
-pp   = size(Y,1);                               % Maximum number of observed variables.
-mm   = size(T,2);                               % Number of state variables.
-a    = zeros(mm,1);                             % State vector.
-Om   = R*Q*transpose(R);                        % Variance of R times the vector of structural innovations.
-t    = 0;                                       % Initialization of the time index.
-oldK = 0;
-notsteady   = 1;                                % Steady state flag.
-F_singular  = 1;
-
-Hess  = zeros(k,k);                             % Initialization of the Hessian
-Da    = zeros(mm,k);                             % State vector.
-Dv = zeros(length(mf),k);
-D2a    = zeros(mm,k,k);                             % State vector.
-D2v = zeros(length(mf),k,k);
-
-C = zeros(length(mf),mm);
-for ii=1:length(mf); C(ii,mf(ii))=1;end         % SELECTION MATRIX IN MEASUREMENT EQ. (FOR WHEN IT IS NOT CONSTANT)
-dC = zeros(length(mf),mm,k);
-d2C = zeros(length(mf),mm,k,k);
-
-s   = zeros(pp,1);                      % CONSTANT TERM IN MEASUREMENT EQ. (FOR WHEN IT IS NOT CONSTANT)
-ds  = zeros(pp,1,k);
-d2s = zeros(pp,1,k,k);
-
-%     for ii = 1:k
-%         DOm = DR(:,:,ii)*Q*transpose(R) + R*DQ(:,:,ii)*transpose(R) + R*Q*transpose(DR(:,:,ii));
-%     end
-
-while notsteady & t<smpl
-    t  = t+1;
-    v  = Y(:,t)-a(mf);
-    F  = P(mf,mf) + H;
-    if rcond(F) < kalman_tol
-        if ~all(abs(F(:))<kalman_tol)
-            return
-        else
-            a = T*a;
-            P = T*P*transpose(T)+Om;
-        end
-    else
-        F_singular = 0;
-        iF     = inv(F);
-        K      = P(:,mf)*iF;
-
-        [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K);
-        [D2K,D2F,D2P1] = computeD2Kalman(T,DT,D2T,D2Om,P,DP,D2P,DH,mf,iF,K,DK);
-        tmp = (a+K*v);
-
-        for ii = 1:k
-            Dv(:,ii)   = -Da(mf,ii) - DYss(mf,ii);
-            %  dai = da(:,:,ii);
-            dKi  = DK(:,:,ii);
-            diFi = -iF*DF(:,:,ii)*iF;
-            dtmpi = Da(:,ii)+dKi*v+K*Dv(:,ii);
-
-
-            for jj = 1:ii
-                dFj    = DF(:,:,jj);
-                diFj   = -iF*DF(:,:,jj)*iF;
-                dKj  = DK(:,:,jj);
-                d2Kij  = D2K(:,:,jj,ii);
-                d2Fij  = D2F(:,:,jj,ii);
-                d2iFij = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
-                dtmpj = Da(:,jj)+dKj*v+K*Dv(:,jj);
-
-                d2vij  = -D2Yss(mf,jj,ii)  - D2a(mf,jj,ii);
-                d2tmpij = D2a(:,jj,ii) + d2Kij*v + dKj*Dv(:,ii) + dKi*Dv(:,jj) + K*d2vij;
-                D2a(:,jj,ii) = D2T(:,:,jj,ii)*tmp + DT(:,:,jj)*dtmpi + DT(:,:,ii)*dtmpj + T*d2tmpij;
-
-                Hesst(ii,jj) = getHesst_ij(v,Dv(:,ii),Dv(:,jj),d2vij,iF,diFi,diFj,d2iFij,dFj,d2Fij);
-            end
-            Da(:,ii)   = DT(:,:,ii)*tmp + T*dtmpi;
-        end
-        %                     vecDPmf = reshape(DP(mf,mf,:),[],k);
-        %                     iPmf = inv(P(mf,mf));
-        if t>=start
-            Hess = Hess + Hesst;
-        end
-        a      = T*(a+K*v);
-        P      = T*(P-K*P(mf,:))*transpose(T)+Om;
-        DP     = DP1;
-        D2P     = D2P1;
-    end
-    notsteady = max(max(abs(K-oldK))) > riccati_tol;
-    oldK = K;
-end
-
-if F_singular
-    error('The variance of the forecast error remains singular until the end of the sample')
-end
-
-
-if t < smpl
-    t0 = t+1;
-    while t < smpl
-        t = t+1;
-        v = Y(:,t)-a(mf);
-        tmp = (a+K*v);
-        for ii = 1:k
-            Dv(:,ii)   = -Da(mf,ii)-DYss(mf,ii);
-            dKi  = DK(:,:,ii);
-            diFi = -iF*DF(:,:,ii)*iF;
-            dtmpi = Da(:,ii)+dKi*v+K*Dv(:,ii);
-
-            for jj = 1:ii
-                dFj    = DF(:,:,jj);
-                diFj   = -iF*DF(:,:,jj)*iF;
-                dKj  = DK(:,:,jj);
-                d2Kij  = D2K(:,:,jj,ii);
-                d2Fij  = D2F(:,:,jj,ii);
-                d2iFij = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
-                dtmpj = Da(:,jj)+dKj*v+K*Dv(:,jj);
-
-                d2vij  = -D2Yss(mf,jj,ii)  - D2a(mf,jj,ii);
-                d2tmpij = D2a(:,jj,ii) + d2Kij*v + dKj*Dv(:,ii) + dKi*Dv(:,jj) + K*d2vij;
-                D2a(:,jj,ii) = D2T(:,:,jj,ii)*tmp + DT(:,:,jj)*dtmpi + DT(:,:,ii)*dtmpj + T*d2tmpij;
-
-                Hesst(ii,jj) = getHesst_ij(v,Dv(:,ii),Dv(:,jj),d2vij,iF,diFi,diFj,d2iFij,dFj,d2Fij);
-            end
-            Da(:,ii)   = DT(:,:,ii)*tmp + T*dtmpi;
-        end
-        if t>=start
-            Hess = Hess + Hesst;
-        end
-        a = T*(a+K*v);
-    end
-    %         Hess = Hess + .5*(smpl+t0-1)*(vecDPmf' * kron(iPmf,iPmf) * vecDPmf);
-    %         for ii = 1:k;
-    %             for jj = 1:ii
-    %              H(ii,jj) = trace(iPmf*(.5*DP(mf,mf,ii)*iPmf*DP(mf,mf,jj) + Dv(:,ii)*Dv(:,jj)'));
-    %             end
-    %         end
-end
-
-Hess = Hess + tril(Hess,-1)';
-
-Hess = -Hess/2;
-% end of main function
-
-function Hesst_ij = getHesst_ij(e,dei,dej,d2eij,iS,diSi,diSj,d2iSij,dSj,d2Sij);
-% computes (i,j) term in the Hessian
-
-Hesst_ij = trace(diSi*dSj + iS*d2Sij) + e'*d2iSij*e + 2*(dei'*diSj*e + dei'*iS*dej + e'*diSi*dej + e'*iS*d2eij);
-
-% end of getHesst_ij
-
-function [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K)
-
-k      = size(DT,3);
-tmp    = P-K*P(mf,:);
-
-for ii = 1:k
-    DF(:,:,ii)  = DP(mf,mf,ii) + DH(:,:,ii);
-    DiF(:,:,ii) = -iF*DF(:,:,ii)*iF;
-    DK(:,:,ii)  = DP(:,mf,ii)*iF + P(:,mf)*DiF(:,:,ii);
-    Dtmp        = DP(:,:,ii) - DK(:,:,ii)*P(mf,:) - K*DP(mf,:,ii);
-    DP1(:,:,ii) = DT(:,:,ii)*tmp*T' + T*Dtmp*T' + T*tmp*DT(:,:,ii)' + DOm(:,:,ii);
-end
-
-% end of computeDKalman
-
-function [d2K,d2S,d2P1] = computeD2Kalman(A,dA,d2A,d2Om,P0,dP0,d2P0,DH,mf,iF,K0,dK0)
-% computes the second derivatives of the Kalman matrices
-% note: A=T in main func.
-
-k      = size(dA,3);
-tmp    = P0-K0*P0(mf,:);
-[ns,no] = size(K0);
-
-% CPC = C*P0*C'; CPC = .5*(CPC+CPC');iF = inv(CPC);
-% APC = A*P0*C';
-% APA = A*P0*A';
-
-
-d2K  = zeros(ns,no,k,k);
-d2S  = zeros(no,no,k,k);
-d2P1 = zeros(ns,ns,k,k);
-
-for ii = 1:k
-    dAi = dA(:,:,ii);
-    dFi = dP0(mf,mf,ii);
-    d2Omi = d2Om(:,:,ii);
-    diFi = -iF*dFi*iF;
-    dKi = dK0(:,:,ii);
-    for jj = 1:k
-        dAj = dA(:,:,jj);
-        dFj = dP0(mf,mf,jj);
-        d2Omj = d2Om(:,:,jj);
-        dFj = dP0(mf,mf,jj);
-        diFj = -iF*dFj*iF;
-        dKj = dK0(:,:,jj);
-
-        d2Aij = d2A(:,:,jj,ii);
-        d2Pij = d2P0(:,:,jj,ii);
-        d2Omij = d2Om(:,:,jj,ii);
-
-        % second order
-
-        d2Fij = d2Pij(mf,mf) ;
-
-        %     d2APC = d2Aij*P0*C' + A*d2Pij*C' + A*P0*d2Cij' + dAi*dPj*C' + dAj*dPi*C' + A*dPj*dCi' + A*dPi*dCj' + dAi*P0*dCj' + dAj*P0*dCi';
-        d2APC = d2Pij(:,mf);
-
-        d2iF = -diFi*dFj*iF -iF*d2Fij*iF -iF*dFj*diFi;
-
-        d2Kij= d2Pij(:,mf)*iF + P0(:,mf)*d2iF + dP0(:,mf,jj)*diFi + dP0(:,mf,ii)*diFj;
-
-        d2KCP = d2Kij*P0(mf,:) + K0*d2Pij(mf,:) + dKi*dP0(mf,:,jj) + dKj*dP0(mf,:,ii) ;
-
-        dtmpi        = dP0(:,:,ii) - dK0(:,:,ii)*P0(mf,:) - K0*dP0(mf,:,ii);
-        dtmpj        = dP0(:,:,jj) - dK0(:,:,jj)*P0(mf,:) - K0*dP0(mf,:,jj);
-        d2tmp = d2Pij - d2KCP;
-
-        d2AtmpA = d2Aij*tmp*A' + A*d2tmp*A' + A*tmp*d2Aij' + dAi*dtmpj*A' + dAj*dtmpi*A' + A*dtmpj*dAi' + A*dtmpi*dAj' + dAi*tmp*dAj' + dAj*tmp*dAi';
-
-        d2K(:,:,ii,jj)  = d2Kij; %#ok<NASGU>
-        d2P1(:,:,ii,jj) = d2AtmpA  + d2Omij;  %#ok<*NASGU>
-        d2S(:,:,ii,jj)  = d2Fij;
-        %     d2iS(:,:,ii,jj) = d2iF;
-    end
-end
-
-% end of computeD2Kalman

matlab/gsa/smirnov.m

@@ -34,11 +34,16 @@ end
 % empirical cdfs.
 xmix= [x1;x2];
 bin = [-inf ; sort(xmix) ; inf];
-
-ncount1 = histc (x1 , bin);
-ncount1 = ncount1(:);
-ncount2 = histc (x2 , bin);
-ncount2 = ncount2(:);
+if isoctave
+    ncount1 = histc(x1 , bin);
+else
+    ncount1 = histcounts(x1 , bin);
+end
+if isoctave
+    ncount2 = histc(x2 , bin);
+else
+    ncount2 = histcounts(x2 , bin);
+end
 
 cum1  =  cumsum(ncount1)./sum(ncount1);
 cum1  =  cum1(1:end-1);

matlab/identification_checks.m

@@ -170,7 +170,7 @@ end
 jweak      = zeros(1,param_nbr);
 jweak_pair = zeros(param_nbr,param_nbr);
 
-if test_flag ~= 0 || test_flag ~= 0
+if test_flag ~= 0
     % these tests only apply to Jacobians, not to Gram matrices, i.e. Hessian-type or 'covariance' matrices
     Pco = NaN(param_nbr,param_nbr);
     for ii = 1:size(Xparnonzero,2)

matlab/issmc.m

@@ -1,9 +1,6 @@
-function S = shiftS(S,n)
-%function S = shiftS(S,n)
-%
-% Removes the first n elements of a one dimensional cell array.
+function bool = issmc(options_)
 
-% Copyright © 2013-2014 Dynare Team
+% Copyright © 2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -20,9 +17,4 @@ function S = shiftS(S,n)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
-if length(S) >= n+1
-    S = S(n+1:end);
-else
-    S = {};
-end
-end
+bool = ishssmc(options_) || isdsmh(options_);

matlab/load_last_mh_history_file.m

@@ -7,10 +7,8 @@ function record = load_last_mh_history_file(MetropolisFolder, ModelName)
 % Outputs:
 %   record              [struct]    structure storing the MH history
 %
-% Notes: The record structure is written to the caller workspace via an
-% assignin statement.
 
-% Copyright © 2013-2017 Dynare Team
+% Copyright © 2013-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -36,7 +34,7 @@ mh_history_files = dir([BaseName '_mh_history_*.mat']);
 
 % Consistency with older versions of Dynare.
 if isequal(length(mh_history_files),0)
-    if exist([BaseName '_mh_history.mat'])
+    if exist([BaseName '_mh_history.mat'],'file')
         format_mh_history_file = 1; % old Dynare format
     else
         error(['Estimation::load_mh_file: I cannot find any mh-history file in ' MetropolisFolder '!'])
@@ -46,7 +44,7 @@ else
 end
 
 if format_mh_history_file %needed to preserve backward compatibility
-    load([BaseName '_mh_history.mat']);
+    load([BaseName '_mh_history.mat'],'record');
     record.LastLogPost = record.LastLogLiK;
     record.InitialLogPost = record.InitialLogLiK;
     record.LastSeeds = record.Seeds;
@@ -60,7 +58,7 @@ if format_mh_history_file %needed to preserve backward compatibility
     record = rmfield(record,'AcceptationRates');
     save([BaseName '_mh_history_0.mat'],'record');
 else
-    load([BaseName '_mh_history_' num2str(length(mh_history_files)-1) '.mat']);
+    load([BaseName '_mh_history_' num2str(length(mh_history_files)-1) '.mat'],'record');
     % add fields that have later been introduced
     if ~isfield(record,'MCMCConcludedSuccessfully')
         record.MCMCConcludedSuccessfully = NaN; % This information is forever lost...

matlab/marginal_density.m

@@ -60,7 +60,7 @@ xparam1 = posterior_mean;
 hh = inv(SIGMA);
 fprintf(' Done!\n');
 if ~isfield(oo_,'posterior_mode') || (options_.mh_replic && isequal(options_.posterior_sampler_options.posterior_sampling_method,'slice'))
-    oo_=fill_mh_mode(posterior_mode',NaN(npar,1),M_,options_,estim_params_,bayestopt_,oo_,'posterior');
+    oo_=fill_mh_mode(posterior_mode',NaN(npar,1),M_,options_,estim_params_,oo_,'posterior');
 end
 
 % save the posterior mean and the inverse of the covariance matrix

matlab/maximize_prior_density.m

@@ -1,9 +1,18 @@
-function [xparams,lpd,hessian_mat] = ...
-    maximize_prior_density(iparams, prior_shape, prior_hyperparameter_1, prior_hyperparameter_2, prior_inf_bound, prior_sup_bound,options_,M_,bayestopt_,estim_params_,oo_)
+function [xparams, lpd, hessian_mat] = ...
+    maximize_prior_density(iparams, names, options_, M_, Prior, estim_params_, oo_)
+
 % Maximizes the logged prior density using Chris Sims' optimization routine.
 %
 % INPUTS
-%   iparams                        [double]   vector of initial parameters.
+% - iparams                        [double]   vector of initial parameters.
+% - Prior              [dprior]   vector specifying prior densities shapes.
+% - DynareOptions      [struct]   Options, AKA options_
+% - DynareModel        [struct]   Model description, AKA M_
+% - EstimatedParams    [struct]   Info about estimated parameters, AKA estimated_params_
+% - DynareResults      [struct]   Results, AKA oo_
+
+%
+%
 %   prior_shape                    [integer]  vector specifying prior densities shapes.
 %   prior_hyperparameter_1         [double]   vector, first hyperparameter.
 %   prior_hyperparameter_2         [double]   vector, second hyperparameter.
@@ -37,10 +46,18 @@ function [xparams,lpd,hessian_mat] = ...
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
-[xparams, lpd, exitflag, hessian_mat]=dynare_minimize_objective('minus_logged_prior_density', ...
-                                                  iparams, options_.mode_compute, options_, [prior_inf_bound, prior_sup_bound], ...
-                                                  bayestopt_.name, bayestopt_, [], ...
-                                                  prior_shape, prior_hyperparameter_1, prior_hyperparameter_2, prior_inf_bound, prior_sup_bound, ...
-                                                  options_,M_,estim_params_,oo_);
+[xparams, lpd, exitflag, hessian_mat] = dynare_minimize_objective('minus_logged_prior_density', ...
+                                                                  iparams, ...
+                                                                  options_.mode_compute, ...
+                                                                  options_, ...
+                                                                  [Prior.p3, Prior.p4], ...
+                                                                  names, ...
+                                                                  [], ...
+                                                                  [], ...
+                                                                  Prior,
+                                                                  options_, ...
+                                                                  M_, ...
+                                                                  estim_params_, ...
+                                                                  oo_);
 
 lpd = -lpd;

matlab/mcpath_function.m

@@ -1,8 +0,0 @@
-function [res,jac,domerr] = mcpath_function(func,z,jacflag,varargin)
-domerr = 0;
-
-if jacflag
-    [res,jac] = func(z,varargin{:});
-else
-    res = func(z,varargin{:});
-end

matlab/metropolis_run_analysis.m

@@ -1,55 +0,0 @@
-function metropolis_run_analysis(M_,basetopt,j)
-%function metropolis_run_analysis(M_,basetopt,j
-% analizes Metropolis runs
-%
-% INPUTS
-%   M_:        (struct)  Model structure
-%   basetopt:  (struct)  Estimated parameters structure
-%   j:         (int)     Index of estimated paramter
-%
-% OUTPUTS
-%   none
-%
-% SPECIAL REQUIREMENTS
-%   none
-
-% Copyright © 2003-2017 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 <https://www.gnu.org/licenses/>.
-
-load([M_.fname '/metropolis/' M_.fname '_mh_history'])
-nblck = record.Nblck;
-ndraws = sum(record.MhDraws(:,1));
-
-logPost = [];
-params = [];
-blck = 1;
-for i=1:record.LastFileNumber
-    fname = [M_.fname '/metropolis/' M_.fname '_mh' int2str(i) '_blck' ...
-             int2str(blck) '.mat'];
-    if exist(fname,'file')
-        o=load(fname);
-        logPost = [logPost; o.logpo2];
-        params  = [params; o.x2];
-    end
-end
-
-figure;
-subplot(2,1,1)
-plot(logPost)
-subplot(2,1,2)
-plot(params(:,j))
-title(['parameter ' int2str(j)])

matlab/mh_autocorrelation_function.m

@@ -59,7 +59,7 @@ nblck = size(record.LastParameters,1);
 clear record;
 
 % Get all the posterior draws:
-PosteriorDraws = GetAllPosteriorDraws(M_.dname, M_.fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck, blck);
+PosteriorDraws = GetAllPosteriorDraws(options_, M_.dname, M_.fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck, blck);
 
 % Compute the autocorrelation function:
 [~,autocor] = sample_autocovariance(PosteriorDraws,options_.mh_autocorrelation_function_size);

matlab/minus_logged_prior_density.m

@@ -1,24 +1,19 @@
-function [fval,info,exit_flag,fake_1,fake_2] = minus_logged_prior_density(xparams,pshape,p6,p7,p3,p4,options_,M_,estim_params_,oo_)
-% [fval,info,exit_flag,fake_1,fake_2] = minus_logged_prior_density(xparams,pshape,p6,p7,p3,p4,options_,M_,estim_params_,oo_)
+function [fval, info, exitflag, ~, ~] = minus_logged_prior_density(xparams, Prior, options_, M_, estim_params_, oo_)
+
 % Evaluates minus the logged prior density.
-%
+ %
 % INPUTS
-%   xparams             [double]   vector of parameters.
-%   pshape              [integer]  vector specifying prior densities shapes.
-%   p6                  [double]   vector, first hyperparameter.
-%   p7                  [double]   vector, second hyperparameter.
-%   p3                  [double]   vector, prior's lower bound.
-%   p4                  [double]   vector, prior's upper bound.
-%   prior_sup_bound     [double]   vector, prior's upper bound.
-%   options_            [structure] describing the options
-%   M_                  [structure] describing the model
-%   estim_params_       [structure] characterizing parameters to be estimated
-%   oo_                 [structure] storing the results
+% - xparams            [double]   vector of parameters.
+% - Prior              [dprior]   vector specifying prior densities shapes.
+% - DynareOptions      [struct]   Options, AKA options_
+% - DynareModel        [struct]   Model description, AKA M_
+% - EstimatedParams    [struct]   Info about estimated parameters, AKA estimated_params_
+% - DynareResults      [struct]   Results, AKA oo_
 %
 % OUTPUTS
-%   f          [double]  value of minus the logged prior density.
-%   info       [double]  vector: second entry stores penalty, first entry the error code.
-%
+% - fval               [double]  value of minus the logged prior density.
+% - info               [double]  4×1 vector, second entry stores penalty, first entry the error code, last entry a penalty (used for optimization).
+
 % Copyright © 2009-2023 Dynare Team
 %
 % This file is part of Dynare.
@@ -36,10 +31,7 @@ function [fval,info,exit_flag,fake_1,fake_2] = minus_logged_prior_density(xparam
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
-fake_1 = [];
-fake_2 = [];
-
-exit_flag = 1;
+exitflag = true;
 info = zeros(4,1);
 
 %------------------------------------------------------------------------------
@@ -47,74 +39,75 @@ info = zeros(4,1);
 %------------------------------------------------------------------------------
 
 % Return, with endogenous penalty, if some parameters are smaller than the lower bound of the prior domain.
-if ~isequal(options_.mode_compute,1) && any(xparams<p3)
-    k = find(xparams<p3);
+if ~isequal(options_.mode_compute, 1) && any(xparams<Prior.p3)
+    k = find(xparams<Prior.p3);
     fval = Inf;
-    exit_flag = 0;
+    exitflag = false;
     info(1) = 41;
-    info(4) = sum((p3(k)-xparams(k)).^2);
+    info(4) = sum((Prior.p3(k)-xparams(k)).^2);
     return
 end
 
 % Return, with endogenous penalty, if some parameters are greater than the upper bound of the prior domain.
-if ~isequal(options_.mode_compute,1) && any(xparams>p4)
-    k = find(xparams>p4);
+if ~isequal(options_.mode_compute, 1) && any(xparams>Prior.p4)
+    k = find(xparams>Prior.p4);
     fval = Inf;
-    exit_flag = 0;
+    exitflag = false;
     info(1) = 42;
-    info(4) = sum((xparams(k)-p4(k)).^2);
+    info(4) = sum((xparams(k)-Prior.p4(k)).^2);
     return
 end
 
 % Get the diagonal elements of the covariance matrices for the structural innovations (Q) and the measurement error (H).
-M_ = set_all_parameters(xparams,estim_params_,M_);
+M_ = set_all_parameters(xparams, estim_params_, M_);
 
 Q = M_.Sigma_e;
 H = M_.H;
 
 % Test if Q is positive definite.
-if ~issquare(Q) || estim_params_.ncx || isfield(estim_params_,'calibrated_covariances')
+if ~issquare(Q) || estim_params_.ncx || isfield(estim_params_, 'calibrated_covariances')
     % Try to compute the cholesky decomposition of Q (possible iff Q is positive definite)
     [Q_is_positive_definite, penalty] = ispd(Q);
     if ~Q_is_positive_definite
-        % The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
+        % The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the
+        % eigenvalues of this matrix in order to build the endogenous penalty.
         fval = Inf;
-        exit_flag = 0;
+        exitflag = false;
         info(1) = 43;
         info(4) = penalty;
         return
     end
-    if isfield(estim_params_,'calibrated_covariances')
-        correct_flag=check_consistency_covariances(Q);
+    if isfield(estim_params_, 'calibrated_covariances')
+        correct_flag = check_consistency_covariances(Q);
         if ~correct_flag
             penalty = sum(Q(estim_params_.calibrated_covariances.position).^2);
             fval = Inf;
-            exit_flag = 0;
+            exitflag = false;
             info(1) = 71;
             info(4) = penalty;
             return4
         end
     end
-
 end
 
 % Test if H is positive definite.
-if ~issquare(H) || estim_params_.ncn || isfield(estim_params_,'calibrated_covariances_ME')
+if ~issquare(H) || estim_params_.ncn || isfield(estim_params_, 'calibrated_covariances_ME')
     [H_is_positive_definite, penalty] = ispd(H);
     if ~H_is_positive_definite
-        % The variance-covariance matrix of the measurement errors is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
+        % The variance-covariance matrix of the measurement errors is not definite positive. We have to compute the eigenvalues
+        % of this matrix in order to build the endogenous penalty.
         fval = Inf;
-        exit_flag = 0;
+        exitflag = false;
         info(1) = 44;
         info(4) = penalty;
         return
     end
-    if isfield(estim_params_,'calibrated_covariances_ME')
-        correct_flag=check_consistency_covariances(H);
+    if isfield(estim_params_, 'calibrated_covariances_ME')
+        correct_flag = check_consistency_covariances(H);
         if ~correct_flag
             penalty = sum(H(estim_params_.calibrated_covariances_ME.position).^2);
             fval = Inf;
-            exit_flag = 0;
+            exitflag = false;
             info(1) = 72;
             info(4) = penalty;
             return
@@ -127,7 +120,7 @@ end
 % 2. Check BK and steady state
 %-----------------------------
 
-[~,info] = resol(0,M_,options_,oo_.dr,oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
+[~, info] = resol(0, M_, options_, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
 
 % Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
 if info(1)
@@ -137,14 +130,14 @@ if info(1)
         %meaningful second entry of output that can be used
         fval = Inf;
         info(4) = info(2);
-        exit_flag = 0;
+        exitflag = false;
         return
     else
         fval = Inf;
         info(4) = 0.1;
-        exit_flag = 0;
+        exitflag = false;
         return
     end
 end
 
-fval = - priordens(xparams,pshape,p6,p7,p3,p4);
+fval = - Prior.density(xparams);

matlab/modules/dseries

@@ -1 +1 @@
-Subproject commit 80446e7cdcf392167e91b428b61d77e028e47674
+Subproject commit a3874403fe5b403f74d727006cb4ccf198cf63c8

matlab/optimize_prior.m

@@ -1,5 +1,5 @@
-function optimize_prior(options_, M_, oo_, bayestopt_, estim_params_)
-% optimize_prior(options_, M_, oo_, bayestopt_, estim_params_)
+function optimize_prior(options_, M_, oo_, Prior, estim_params_, pnames)
+
 % This routine computes the mode of the prior density using an optimization algorithm.
 %
 % INPUTS
@@ -26,24 +26,25 @@ function optimize_prior(options_, M_, oo_, bayestopt_, estim_params_)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
+oo_.dr = set_state_space(oo_.dr, M_, options_);
+
 % Initialize to the prior mean
-oo_.dr = set_state_space(oo_.dr,M_);
-xparam1 = bayestopt_.p1;
+xparam1 = Prior.p1;
 
 % Pertubation of the initial condition.
-look_for_admissible_initial_condition = 1; scale = 1.0; iter  = 0;
+look_for_admissible_initial_condition = true; scale = 1.0; iter  = 0;
 while look_for_admissible_initial_condition
     xinit = xparam1+scale*randn(size(xparam1));
-    if all(xinit(:)>bayestopt_.p3) && all(xinit(:)<bayestopt_.p4)
-        M_ = set_all_parameters(xinit,estim_params_,M_);
-        [oo_.dr,INFO,M_.params] = resol(0,M_,options_,oo_.dr,oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
+    if all(xinit>Prior.p3) && all(xinit<Prior.p4)
+        M_ = set_all_parameters(xinit, estim_params_, M_);
+        [dr, INFO, M_, oo_] = resol(0, M_, options_, oo_);
         if ~INFO(1)
-            look_for_admissible_initial_condition = 0;
+            look_for_admissible_initial_condition = false;
         end
     else
-        if iter == 2000
+        if iter==2000
             scale = scale/1.1;
-            iter  = 0;
+            iter = 0;
         else
             iter = iter+1;
         end
@@ -52,23 +53,19 @@ end
 
 % Maximization of the prior density
 [xparams, lpd, hessian_mat] = ...
-    maximize_prior_density(xinit, bayestopt_.pshape, ...
-                           bayestopt_.p6, ...
-                           bayestopt_.p7, ...
-                           bayestopt_.p3, ...
-                           bayestopt_.p4,options_,M_,bayestopt_,estim_params_,oo_);
+    maximize_prior_density(xinit, pnames, options_, M_, Prior, estim_params_, oo_);
 
-% Display the results.
+% Display results.
 skipline(2)
 disp('------------------')
 disp('PRIOR OPTIMIZATION')
 disp('------------------')
 skipline()
 for i = 1:length(xparams)
-    disp(['deep parameter ' int2str(i) ': ' get_the_name(i,0,M_,estim_params_,options_.varobs) '.'])
-    disp(['  Initial condition ....... ' num2str(xinit(i)) '.'])
-    disp(['  Prior mode .............. ' num2str(bayestopt_.p5(i)) '.'])
-    disp(['  Optimized prior mode .... ' num2str(xparams(i)) '.'])
+    dprintf('deep parameter %u: %s.', i, get_the_name(i, 0, M_, estim_params_, options_.varobs))
+    dprintf('  Initial condition ........ %s.', num2str(xinit(i)))
+    dprintf('  Prior mode ............... %s.', num2str(Prior.p5(i)))
+    dprintf('  Optimized prior mode ..... %s.', num2str(xparams(i)))
     skipline()
 end
-skipline()
+skipline()

matlab/plot_shock_decomposition.m

@@ -536,6 +536,11 @@ b = size(z,3);
 if ~isempty(options_.plot_shock_decomp.plot_init_date)
     my_initial_date = max(initial_date,options_.plot_shock_decomp.plot_init_date);
     a = find((initial_date:initial_date+b-1)==options_.plot_shock_decomp.plot_init_date);
+    if isempty(a)
+        warning(['You set plot_init_date larger than the last observation. The last observation is %s,\n' ...
+            'while you requested as the first observation %s.Exiting because there is nothing to do.'],initial_date+b,options_.plot_shock_decomp.plot_init_date);
+        return;
+    end
 end
 if ~isempty(options_.plot_shock_decomp.plot_end_date)
     if options_.plot_shock_decomp.plot_end_date<=(max(initial_date:initial_date+b-1))

matlab/posterior_sampler_initialization.m

@@ -1,8 +1,7 @@
 function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npar, NumberOfBlocks, nruns, NewFile, MAX_nruns, d, bayestopt_] = ...
-    posterior_sampler_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_, dispString)
-% function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npar, NumberOfBlocks, nruns, NewFile, MAX_nruns, d, bayestopt_] = ...
-%     metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_, dispString)
-% Metropolis-Hastings initialization.
+    posterior_sampler_initialization(TargetFun, xparam1, vv, mh_bounds, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_, dispString)
+
+% Posterior sampler initialization.
 %
 % INPUTS
 %   o TargetFun  [char]     string specifying the name of the objective
@@ -257,7 +256,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
         if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
             ix2 = candidate;
             ilogpo2 = - feval(TargetFun,ix2',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_.dr, oo_.steady_state,oo_.exo_steady_state,oo_.exo_det_steady_state);
-            fprintf('%s: Initialization at the posterior mode.\n\n',dispString);            
+            fprintf('%s: Initialization at the posterior mode.\n\n',dispString);
             fprintf(fidlog,['    Blck ' int2str(1) 'params:\n']);
             for i=1:length(ix2(1,:))
                 fprintf(fidlog,['      ' int2str(i)  ':' num2str(ix2(1,i)) '\n']);

matlab/prior_bounds.m

@@ -1,51 +1,13 @@
-function bounds = prior_bounds(bayestopt, prior_trunc)
+function bounds = prior_bounds(bayestopt_, priortrunc)
 
-%@info:
-%! @deftypefn {Function File} {@var{bounds} =} prior_bounds (@var{bayesopt},@var{option})
-%! @anchor{prior_bounds}
-%! @sp 1
-%! Returns bounds for the prior densities. For each estimated parameter the lower and upper bounds
-%! are such that the defined intervals contains a probability mass equal to 1-2*@var{option}.prior_trunc. The
-%! default value for @var{option}.prior_trunc is 1e-10 (set in @ref{global_initialization}).
-%! @sp 2
-%! @strong{Inputs}
-%! @sp 1
-%! @table @ @var
-%! @item bayestopt
-%! Matlab's structure describing the prior distribution (initialized by @code{dynare}).
-%! @item option
-%! Matlab's structure describing the options (initialized by @code{dynare}).
-%! @end table
-%! @sp 2
-%! @strong{Outputs}
-%! @sp 1
-%! @table @ @var
-%! @item bounds
-%! A structure with two fields lb and up (p*1 vectors of doubles, where p is the number of estimated parameters) for the lower and upper bounds.
-%! @end table
-%! @sp 2
-%! @strong{This function is called by:}
-%! @sp 1
-%! @ref{get_prior_info}, @ref{dynare_estimation_1}, @ref{dynare_estimation_init}
-%! @sp 2
-%! @strong{This function calls:}
-%! @sp 1
-%! None.
-%! @end deftypefn
-%@eod:
-
-
-% function bounds = prior_bounds(bayestopt)
 % computes bounds for prior density.
 %
 % INPUTS
-%    bayestopt  [structure]  characterizing priors (shape, mean, p1..p4)
+% - bayestopt   [struct]  characterizing priors (shape, mean, p1..p4)
+% - priortrunc  [double]  scalar, probability mass in the tails to be removed
 %
 % OUTPUTS
-%    bounds     [double]      structure specifying prior bounds (lb and ub fields)
-%
-% SPECIAL REQUIREMENTS
-%    none
+% - bounds     [struct]  prior bounds (lb, lower bounds, and ub, upper bounds, fields are n×1 vectors)
 
 % Copyright © 2003-2023 Dynare Team
 %
@@ -64,74 +26,78 @@ function bounds = prior_bounds(bayestopt, prior_trunc)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
-pshape = bayestopt.pshape;
-p3 = bayestopt.p3;
-p4 = bayestopt.p4;
-p6 = bayestopt.p6;
-p7 = bayestopt.p7;
+if nargin<2, priortrunc = 0.0; end
 
-bounds.lb = zeros(length(p6),1);
-bounds.ub = zeros(length(p6),1);
+assert(priortrunc>=0 && priortrunc<=1, 'Second input argument must be non negative and not larger than one.')
+
+pshape = bayestopt_.pshape;
+p3 = bayestopt_.p3;
+p4 = bayestopt_.p4;
+p6 = bayestopt_.p6;
+p7 = bayestopt_.p7;
+
+bounds.lb = zeros(size(p6));
+bounds.ub = zeros(size(p6));
 
 for i=1:length(p6)
     switch pshape(i)
       case 1
-        if prior_trunc == 0
+        if priortrunc==0
             bounds.lb(i) = p3(i);
             bounds.ub(i) = p4(i);
         else
-            bounds.lb(i) = betainv(prior_trunc,p6(i),p7(i))*(p4(i)-p3(i))+p3(i);
-            bounds.ub(i) = betainv(1-prior_trunc,p6(i),p7(i))*(p4(i)-p3(i))+p3(i);
+            bounds.lb(i) = betainv(priortrunc, p6(i), p7(i))*(p4(i)-p3(i))+p3(i);
+            bounds.ub(i) = betainv(1.0-priortrunc, p6(i), p7(i))*(p4(i)-p3(i))+p3(i);
         end
       case 2
-        if prior_trunc == 0
+        if priortrunc==0
             bounds.lb(i) = p3(i);
             bounds.ub(i) = Inf;
         else
-            bounds.lb(i) = gaminv(prior_trunc,p6(i),p7(i))+p3(i);
-            bounds.ub(i) = gaminv(1-prior_trunc,p6(i),p7(i))+p3(i);
+            bounds.lb(i) = gaminv(priortrunc, p6(i), p7(i))+p3(i);
+            bounds.ub(i) = gaminv(1.0-priortrunc, p6(i), p7(i))+p3(i);
         end
       case 3
-        if prior_trunc == 0
-            bounds.lb(i) = max(-Inf,p3(i));
-            bounds.ub(i) = min(Inf,p4(i));
+        if priortrunc == 0
+            bounds.lb(i) = max(-Inf, p3(i));
+            bounds.ub(i) = min(Inf, p4(i));
         else
-            bounds.lb(i) = max(norminv(prior_trunc,p6(i),p7(i)),p3(i));
-            bounds.ub(i) = min(norminv(1-prior_trunc,p6(i),p7(i)),p4(i));
+            bounds.lb(i) = max(norminv(priortrunc, p6(i), p7(i)), p3(i));
+            bounds.ub(i) = min(norminv(1-priortrunc, p6(i), p7(i)), p4(i));
         end
       case 4
-        if prior_trunc == 0
+        if priortrunc==0
             bounds.lb(i) = p3(i);
             bounds.ub(i) = Inf;
         else
-            bounds.lb(i) = 1/sqrt(gaminv(1-prior_trunc, p7(i)/2, 2/p6(i)))+p3(i);
-            bounds.ub(i) = 1/sqrt(gaminv(prior_trunc, p7(i)/2, 2/p6(i)))+p3(i);
+            bounds.lb(i) = 1.0/sqrt(gaminv(1.0-priortrunc, p7(i)/2.0, 2.0/p6(i)))+p3(i);
+            bounds.ub(i) = 1.0/sqrt(gaminv(priortrunc, p7(i)/2.0, 2.0/p6(i)))+p3(i);
         end
       case 5
-        if prior_trunc == 0
+        if priortrunc == 0
             bounds.lb(i) = p6(i);
             bounds.ub(i) = p7(i);
         else
-            bounds.lb(i) = p6(i)+(p7(i)-p6(i))*prior_trunc;
-            bounds.ub(i) = p7(i)-(p7(i)-p6(i))*prior_trunc;
+            bounds.lb(i) = p6(i)+(p7(i)-p6(i))*priortrunc;
+            bounds.ub(i) = p7(i)-(p7(i)-p6(i))*priortrunc;
         end
       case 6
-        if prior_trunc == 0
+        if priortrunc == 0
             bounds.lb(i) = p3(i);
             bounds.ub(i) = Inf;
         else
-            bounds.lb(i) = 1/gaminv(1-prior_trunc, p7(i)/2, 2/p6(i))+p3(i);
-            bounds.ub(i) = 1/gaminv(prior_trunc, p7(i)/2, 2/p6(i))+ p3(i);
+            bounds.lb(i) = 1.0/gaminv(1.0-priortrunc, p7(i)/2.0, 2.0/p6(i))+p3(i);
+            bounds.ub(i) = 1.0/gaminv(priortrunc, p7(i)/2.0, 2.0/p6(i))+ p3(i);
         end
       case 8
-        if prior_trunc == 0
+        if priortrunc == 0
             bounds.lb(i) = p3(i);
             bounds.ub(i) = Inf;
         else
-            bounds.lb(i) = p3(i)+wblinv(prior_trunc,p6(i),p7(i));
-            bounds.ub(i) = p3(i)+wblinv(1-prior_trunc,p6(i),p7(i));
+            bounds.lb(i) = p3(i)+wblinv(priortrunc, p6(i), p7(i));
+            bounds.ub(i) = p3(i)+wblinv(1.0-priortrunc, p6(i), p7(i));
         end
       otherwise
-        error(sprintf('prior_bounds: unknown distribution shape (index %d, type %d)', i, pshape(i)));
+        error('prior_bounds: unknown distribution shape (index %d, type %d)', i, pshape(i));
     end
-end
+end

matlab/prior_posterior_statistics.m

@@ -212,9 +212,9 @@ if strcmpi(type,'posterior')
     mh_nblck = options_.mh_nblck;
     if B==NumberOfDraws*mh_nblck
         % we load all retained MH runs !
-        logpost=GetAllPosteriorDraws(M_.dname, M_.fname, 0, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck);
+        logpost=GetAllPosteriorDraws(options_, M_.dname, M_.fname, 0, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck);
         for column=1:npar
-            x(:,column) = GetAllPosteriorDraws(M_.dname, M_.fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck);
+            x(:,column) = GetAllPosteriorDraws(options_, M_.dname, M_.fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, NumberOfDraws, nblck);
         end
     else
         logpost=NaN(B,1);
@@ -390,4 +390,4 @@ if ~isnumeric(options_.parallel)
     if leaveSlaveOpen == 0
         closeSlave(options_.parallel,options_.parallel_info.RemoteTmpFolder),
     end
-end
+end

matlab/score.m

@@ -1,123 +0,0 @@
-function [DLIK] = score(T,R,Q,H,P,Y,DT,DYss,DOm,DH,DP,start,mf,kalman_tol,riccati_tol)
-% function [DLIK] = score(T,R,Q,H,P,Y,DT,DYss,DOm,DH,DP,start,mf,kalman_tol,riccati_tol)
-%
-% computes the derivative of the log-likelihood function of
-% a state space model (notation as in kalman_filter.m in DYNARE
-% thanks to Nikolai Iskrev
-%
-% NOTE: the derivative matrices (DT,DR ...) are 3-dim. arrays with last
-% dimension equal to the number of structural parameters
-
-% Copyright © 2009-2017 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/licen
-
-k = size(DT,3);                                 % number of structural parameters
-smpl = size(Y,2);                               % Sample size.
-mm   = size(T,2);                               % Number of state variables.
-a    = zeros(mm,1);                             % State vector.
-Om   = R*Q*transpose(R);                        % Variance of R times the vector of structural innovations.
-t    = 0;                                       % Initialization of the time index.
-oldK = 0;
-notsteady   = 1;                                % Steady state flag.
-F_singular  = 1;
-
-DLIK  = zeros(k,1);                             % Initialization of the score.
-Da    = zeros(mm,k);                            % State vector.
-Dv    = zeros(length(mf),k);                    % observation vector.
-
-%     for ii = 1:k
-%         DOm = DR(:,:,ii)*Q*transpose(R) + R*DQ(:,:,ii)*transpose(R) + R*Q*transpose(DR(:,:,ii));
-%     end
-
-while notsteady & t<smpl
-    t  = t+1;
-    v  = Y(:,t)-a(mf);
-    F  = P(mf,mf) + H;
-    if rcond(F) < kalman_tol
-        if ~all(abs(F(:))<kalman_tol)
-            return
-        else
-            a = T*a;
-            P = T*P*transpose(T)+Om;
-        end
-    else
-        F_singular = 0;
-        iF     = inv(F);
-        K      = P(:,mf)*iF;
-
-        [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K);
-        for ii = 1:k
-            Dv(:,ii)   = -Da(mf,ii)-DYss(mf,ii);
-            Da(:,ii)   = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
-            if t>=start
-                DLIK(ii,1)  = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
-            end
-        end
-        a      = T*(a+K*v);
-        P      = T*(P-K*P(mf,:))*transpose(T)+Om;
-        DP     = DP1;
-    end
-    notsteady = max(max(abs(K-oldK))) > riccati_tol;
-    oldK = K;
-end
-
-if F_singular
-    error('The variance of the forecast error remains singular until the end of the sample')
-end
-
-for ii = 1:k
-    tmp0(:,:,ii) = iF*DF(:,:,ii)*iF;
-end
-
-if t < smpl
-    t0 = t+1;
-    while t < smpl
-        t = t+1;
-        v = Y(:,t)-a(mf);
-        for ii = 1:k
-            Dv(:,ii)   = -Da(mf,ii)-DYss(mf,ii);
-            Da(:,ii)   = DT(:,:,ii)*(a+K*v) + T*(Da(:,ii)+DK(:,:,ii)*v + K*Dv(:,ii));
-            if t>=start
-                DLIK(ii,1)  = DLIK(ii,1) + trace( iF*DF(:,:,ii) ) + 2*Dv(:,ii)'*iF*v - v'*(iF*DF(:,:,ii)*iF)*v;
-            end
-        end
-        a = T*(a+K*v);
-    end
-    for ii = 1:k
-        %             DLIK(ii,1)  = DLIK(ii,1) + (smpl-t0+1)*trace( iF*DF(:,:,ii) );
-    end
-
-end
-
-DLIK = DLIK/2;
-
-% end of main function
-
-function [DK,DF,DP1] = computeDKalman(T,DT,DOm,P,DP,DH,mf,iF,K)
-
-k      = size(DT,3);
-tmp    = P-K*P(mf,:);
-
-for ii = 1:k
-    DF(:,:,ii)  = DP(mf,mf,ii) + DH(:,:,ii);
-    DiF(:,:,ii) = -iF*DF(:,:,ii)*iF;
-    DK(:,:,ii)  = DP(:,mf,ii)*iF + P(:,mf)*DiF(:,:,ii);
-    Dtmp        = DP(:,:,ii) - DK(:,:,ii)*P(mf,:) - K*DP(mf,:,ii);
-    DP1(:,:,ii) = DT(:,:,ii)*tmp*T' + T*Dtmp*T' + T*tmp*DT(:,:,ii)' + DOm(:,:,ii);
-end
-
-% end of computeDKalman

matlab/substitute.m

@@ -0,0 +1,59 @@
+function v = substitute(v, i, x)
+
+% Substitute a scalar in a vector.
+%
+% INPUTS
+% - v     [double]      m×1 vector
+% - i     [integer]     scalar, index for the scalar to be replaced
+% - x     [double]      scalar or 1×n vector.
+%
+% OUTPUTS
+% - v     [double]      m×1 vector or m×n matrix (with substituted value(s))
+%
+% REMARKS
+% If x is a vector with n elements, then n substitutions are performed (returning n updated vectors in a matrix with n columns)
+%
+% EXAMPLES
+% >> v = ones(2,1);
+% >> substitude(v, 1, 0)
+%
+% ans =                                                                                                                                                                        %
+%
+%    0
+%
+%    1
+%
+% >> substitute(v, 1, [3 4])
+%
+% ans =
+%
+%    3     4
+%    1     1
+
+% Copyright © 2023 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 <https://www.gnu.org/licenses/>.
+
+assert(isvector(v), 'First input argument must be a vector.')
+assert(isvector(x), 'Last input argument must be a scalar or a vector.')
+assert(isscalar(i) && isint(i) && i>0 && i<=length(v), 'Second input argument must be a scalar integer')
+
+if length(x)==1
+    v(i) = x;
+else
+    v = repmat(v, 1, length(x));
+    v(i,:) = x;
+end

matlab/trace_plot.m

@@ -67,7 +67,7 @@ n_nblocks_to_plot=length(blck);
 
 if n_nblocks_to_plot==1
 % Get all the posterior draws:
-PosteriorDraws = GetAllPosteriorDraws(M_.dname,M_.fname,column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, TotalNumberOfMhDraws, mh_nblck, blck);
+    PosteriorDraws = GetAllPosteriorDraws(options_, M_.dname,M_.fname,column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, TotalNumberOfMhDraws, mh_nblck, blck);
 else
     PosteriorDraws=NaN(TotalNumberOfMhDraws,n_nblocks_to_plot);
     save_string='';
@@ -75,7 +75,7 @@ else
         title_string_tex='';
     end
     for block_iter=1:n_nblocks_to_plot
-        PosteriorDraws(:,block_iter) = GetAllPosteriorDraws(M_.dname, M_.fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, TotalNumberOfMhDraws, mh_nblck, blck(block_iter));
+        PosteriorDraws(:,block_iter) = GetAllPosteriorDraws(options_, M_.dname, M_.fname, column, FirstMhFile, FirstLine, TotalNumberOfMhFiles, TotalNumberOfMhDraws, mh_nblck, blck(block_iter));
         save_string=[save_string,'_',num2str(blck(block_iter))];
         if options_.TeX
             title_string_tex=[title_string_tex, ', ' num2str(blck(block_iter))];

matlab/var_sample_moments.m

@@ -1,4 +1,5 @@
-function dataset_info=var_sample_moments(nlag, var_trend_order, dataset_, dataset_info)%datafile,varobs,xls_sheet,xls_range)
+function dataset_info=var_sample_moments(nlag, var_trend_order, dataset_, dataset_info)
+% dataset_info=var_sample_moments(nlag, var_trend_order, dataset_, dataset_info)
 % Computes the sample moments of a VAR model.
 %
 % The VAR(p) model is defined by:
@@ -51,7 +52,7 @@ function dataset_info=var_sample_moments(nlag, var_trend_order, dataset_, datase
 % SPECIAL REQUIREMENTS
 %   None.
 
-% Copyright © 2007-2021 Dynare Team
+% Copyright © 2007-2023 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -80,14 +81,11 @@ if isequal(var_trend_order,-1)
 elseif isequal(var_trend_order,0)
     % Constant and no linear trend case.
     X = ones(NumberOfObservations,NumberOfVariables*nlag+1);
-    indx = NumberOfVariables*nlag+1;
 elseif isequal(var_trend_order,1)
     % Constant and linear trend case.
     X = ones(NumberOfObservations,NumberOfVariables*nlag+2);
-    indx = NumberOfVariables*nlag+1:NumberOfVariables*nlag+2;
 else
     error('Estimation::var_sample_moments: trend must be equal to -1,0 or 1!')
-    return
 end
 
 % I build matrices Y and X
@@ -108,10 +106,4 @@ dataset_info.mYX=Y'*X;
 dataset_info.mXY=X'*Y;
 dataset_info.mXX=X'*X;
 dataset_info.Ydata=Y;
-dataset_info.Xdata=X;
-% assignin('base', 'mYY', Y'*Y);
-% assignin('base', 'mYX', Y'*X);
-% assignin('base', 'mXY', X'*Y);
-% assignin('base', 'mXX', X'*X);
-% assignin('base', 'Ydata', Y);
-% assignin('base', 'Xdata', X);
+dataset_info.Xdata=X;

meson.build

@@ -457,7 +457,8 @@ shared_module('k_order_welfare', k_order_welfare_src, kwargs : korder_mex_kwargs
 
 # Unit tests
 
-k_order_test_kwargs = { 'include_directories' : [ mex_incdir, korder_incdir ],
+k_order_test_kwargs = { 'include_directories' : mex_kwargs['include_directories'] + korder_incdir,
+                        'cpp_args' : mex_kwargs['cpp_args'],
                         'link_args' : exe_link_args,
                         'build_rpath' : exe_rpath,
                         'link_with' : korder_lib }
@@ -804,6 +805,8 @@ mod_and_m_tests = [
                 'estimation/fsdat_simul.m' ] },
   { 'test' : [ 'estimation/fs2000.mod' ],
     'extra' : [ 'estimation/fsdat_simul.m' ] },
+  { 'test' : [ 'estimation/hssmc/fs2000.mod' ],
+    'extra' : [ 'estimation/fsdat_simul.m' ] },
   { 'test' : [ 'gsa/ls2003a.mod',
                'gsa/ls2003.mod',
                'gsa/ls2003scr.mod',

mex/sources/libkorder/k_ord_dynare.cc

@@ -1,5 +1,5 @@
 /*
- * Copyright © 2008-2022 Dynare Team
+ * Copyright © 2008-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -248,7 +248,7 @@ DynareStateNameList::DynareStateNameList(const KordpDynare& dynare, const Dynare
                                          const DynareNameList& denl)
 {
   for (int i = 0; i < dynare.nYs; i++)
-    names.emplace_back(dnl.getName(i + dynare.nstat()));
-  for (int i = 0; i < dynare.nexog(); i++)
+    names.emplace_back(dnl.getName(i + dynare.nStat));
+  for (int i = 0; i < dynare.nExog; i++)
     names.emplace_back(denl.getName(i));
 }

mex/sources/libkorder/sylv/GeneralMatrix.hh

@@ -90,12 +90,10 @@ public:
   ConstGeneralMatrix(const GeneralMatrix& m, int i, int j, int nrows, int ncols);
   // Create submatrix (with data sharing)
   ConstGeneralMatrix(const ConstGeneralMatrix& m, int i, int j, int nrows, int ncols);
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
   explicit ConstGeneralMatrix(const mxArray* p) :
       data(p), rows {static_cast<int>(mxGetM(p))}, cols {static_cast<int>(mxGetN(p))}, ld {rows}
   {
   }
-#endif
   virtual ~ConstGeneralMatrix() = default;
 
   ConstGeneralMatrix& operator=(const ConstGeneralMatrix& v) = delete;
@@ -234,12 +232,10 @@ public:
   // Create submatrix (with data sharing)
   GeneralMatrix(GeneralMatrix& m, int i, int j, int nrows, int ncols);
 
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
   explicit GeneralMatrix(mxArray* p) :
       data(p), rows {static_cast<int>(mxGetM(p))}, cols {static_cast<int>(mxGetN(p))}, ld {rows}
   {
   }
-#endif
 
   virtual ~GeneralMatrix() = default;
   GeneralMatrix& operator=(const GeneralMatrix& m) = default;

mex/sources/libkorder/sylv/SylvParams.cc

@@ -125,7 +125,6 @@ SylvParams::getArrayNames() const
   return names;
 }
 
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
 mxArray*
 SylvParams::DoubleParamItem::createMatlabArray() const
 {
@@ -136,11 +135,11 @@ mxArray*
 SylvParams::IntParamItem::createMatlabArray() const
 {
   mxArray* res = mxCreateNumericMatrix(1, 1, mxINT32_CLASS, mxREAL);
-# if MX_HAS_INTERLEAVED_COMPLEX
+#if MX_HAS_INTERLEAVED_COMPLEX
   *mxGetInt32s(res) = value;
-# else
+#else
   *static_cast<int*>(mxGetData(res)) = value;
-# endif
+#endif
   return res;
 }
 
@@ -225,4 +224,3 @@ SylvParams::createStructArray() const
 
   return res;
 }
-#endif

mex/sources/libkorder/sylv/SylvParams.hh

@@ -25,9 +25,7 @@
 #include <string>
 #include <vector>
 
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
-# include <dynmex.h>
-#endif
+#include <dynmex.h>
 
 enum class status
 {
@@ -111,9 +109,7 @@ protected:
       ParamItem<double>::operator=(val);
       return *this;
     }
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
     [[nodiscard]] mxArray* createMatlabArray() const;
-#endif
   };
 
   class IntParamItem : public ParamItem<int>
@@ -132,9 +128,7 @@ protected:
       ParamItem<int>::operator=(val);
       return *this;
     }
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
     [[nodiscard]] mxArray* createMatlabArray() const;
-#endif
   };
 
   class BoolParamItem : public ParamItem<bool>
@@ -153,9 +147,7 @@ protected:
       ParamItem<bool>::operator=(val);
       return *this;
     }
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
     [[nodiscard]] mxArray* createMatlabArray() const;
-#endif
   };
 
   class MethodParamItem : public ParamItem<solve_method>
@@ -174,9 +166,7 @@ protected:
       ParamItem<solve_method>::operator=(val);
       return *this;
     }
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
     [[nodiscard]] mxArray* createMatlabArray() const;
-#endif
   };
 
 public:
@@ -224,9 +214,8 @@ public:
   void print(const std::string& prefix) const;
   void print(std::ostream& fdesc, const std::string& prefix) const;
   [[nodiscard]] std::vector<const char*> getArrayNames() const;
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
   [[nodiscard]] mxArray* createStructArray() const;
-#endif
+
 private:
   void copy(const SylvParams& p);
 };

mex/sources/libkorder/sylv/Vector.cc

@@ -107,14 +107,12 @@ Vector::Vector(const Vector& v, int off_arg, int skip, int l) : len(l), data {ne
   copy(v.data + off_arg * v.s, v.s * skip);
 }
 
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
 Vector::Vector(mxArray* p) :
     len {static_cast<int>(mxGetNumberOfElements(p))}, data {mxGetPr(p)}, destroy {false}
 {
   if (!mxIsDouble(p) || mxIsComplex(p) || mxIsSparse(p))
     throw SYLV_MES_EXCEPTION("This is not a dense array of real doubles.");
 }
-#endif
 
 bool
 Vector::operator==(const Vector& y) const
@@ -305,14 +303,12 @@ ConstVector::ConstVector(const double* d, int skip, int l) : len {l}, s {skip},
 {
 }
 
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
 ConstVector::ConstVector(const mxArray* p) :
     len {static_cast<int>(mxGetNumberOfElements(p))}, data {mxGetPr(p)}
 {
   if (!mxIsDouble(p))
     throw SYLV_MES_EXCEPTION("This is not a MATLAB array of doubles.");
 }
-#endif
 
 bool
 ConstVector::operator==(const ConstVector& y) const

mex/sources/libkorder/sylv/Vector.hh

@@ -28,9 +28,7 @@
 #include <complex>
 #include <utility>
 
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
-# include <dynmex.h>
-#endif
+#include <dynmex.h>
 
 class GeneralMatrix;
 class ConstVector;
@@ -72,9 +70,7 @@ public:
   Vector(const Vector& v, int off_arg, int l);
   Vector(Vector& v, int off_arg, int skip, int l);
   Vector(const Vector& v, int off_arg, int skip, int l);
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
   explicit Vector(mxArray* p);
-#endif
   Vector& operator=(const Vector& v);
   /* The move-assignment operator is not implemented, because moving pointers
      across class instances would break the “reference semantics” that the
@@ -198,9 +194,7 @@ public:
   ConstVector(const ConstVector& v, int off_arg, int l);
   ConstVector(const ConstVector& v, int off_arg, int skip, int l);
   ConstVector(const double* d, int skip, int l);
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
   explicit ConstVector(const mxArray* p);
-#endif
   virtual ~ConstVector() = default;
   ConstVector& operator=(const ConstVector& v) = delete;
   ConstVector& operator=(ConstVector&& v) = delete;

mex/sources/libkorder/tl/t_polynomial.hh

@@ -216,7 +216,7 @@ public:
                   auto ten_smart = std::make_unique<_Ttype>(nrows(), nvars(), j);
                   ten_smart->zeros();
                   ten = ten_smart.get();
-                  insert(std::move(ten_smart));
+                  _Tparent::insert(std::move(ten_smart));
                 }
 
               Symmetry sym {i, j};
@@ -245,7 +245,7 @@ public:
               auto ten_smart = std::make_unique<_Ttype>(nrows(), nvars(), j);
               ten_smart->zeros();
               ten = ten_smart.get();
-              insert(std::move(ten_smart));
+              _Tparent::insert(std::move(ten_smart));
             }
 
           Symmetry sym {0, j};

mex/sources/libkorder/tl/twod_matrix.hh

@@ -1,6 +1,6 @@
 /*
  * Copyright © 2004-2011 Ondra Kamenik
- * Copyright © 2019 Dynare Team
+ * Copyright © 2019-2023 Dynare Team
  *
  * This file is part of Dynare.
  *
@@ -63,11 +63,9 @@ public:
       ConstGeneralMatrix(m, first_row, first_col, rows, cols)
   {
   }
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
   explicit ConstTwoDMatrix(const mxArray* p) : ConstGeneralMatrix(p)
   {
   }
-#endif
   ~ConstTwoDMatrix() override = default;
 
   ConstTwoDMatrix& operator=(const ConstTwoDMatrix& v) = delete;
@@ -125,11 +123,9 @@ public:
   {
   }
 
-#if defined(MATLAB_MEX_FILE) || defined(OCTAVE_MEX_FILE)
   explicit TwoDMatrix(mxArray* p) : GeneralMatrix(p)
   {
   }
-#endif
   ~TwoDMatrix() override = default;
 
   TwoDMatrix& operator=(const TwoDMatrix& m) = default;

preprocessor

@@ -1 +1 @@
-Subproject commit 378d00fc3a50a11c3dce615ea337ad55989c938e
+Subproject commit cd86b1895d97d1b195849072a8d26fd372ca5d8c

scripts/dynare.el

@@ -55,39 +55,40 @@
 ;; Also include "end" in this list
 (defvar dynare-statements
   '("var" "varexo" "varexo_det" "trend_var" "log_trend_var"
-    "predetermined_variables" "parameters" "model_local_variable"
-    "model_info" "estimation" "set_time" "data" "varobs"
-    "varexobs" "unit_root_vars" "rplot" "osr_params" "osr" "dynatype"
-    "dynasave" "model_comparison" "change_type" "load_params_and_steady_state"
-    "save_params_and_steady_state" "write_latex_dynamic_model"
-    "write_latex_static_model" "write_latex_original_model"
-    "write_latex_steady_state_model" "steady" "check" "simul" "stoch_simul"
-    "var_model" "trend_component_model" "var_expectation_model" "pac_model"
-    "dsample" "planner_objective" "ramsey_model" "ramsey_policy"
-    "evaluate_planner_objective" "occbin_setup" "occbin_solver"
-    "occbin_write_regimes" "occbin_graph"
+    "predetermined_variables" "parameters" "model_local_variable" "model_info"
+    "estimation" "set_time" "data" "varobs" "varexobs" "unit_root_vars" "rplot"
+    "osr_params" "osr" "dynatype" "dynasave" "model_comparison" "change_type"
+    "load_params_and_steady_state" "save_params_and_steady_state"
+    "write_latex_dynamic_model" "write_latex_static_model"
+    "write_latex_original_model" "write_latex_steady_state_model" "steady"
+    "check" "simul" "stoch_simul" "var_model" "trend_component_model"
+    "var_expectation_model" "pac_model" "dsample" "planner_objective"
+    "ramsey_model" "ramsey_policy" "evaluate_planner_objective" "occbin_setup"
+    "occbin_solver" "occbin_write_regimes" "occbin_graph"
     "discretionary_policy" "identification" "bvar_density" "bvar_forecast"
-    "dynare_sensitivity" "initval_file" "histval_file" "forecast"
+    "bvar_irf" "dynare_sensitivity" "initval_file" "histval_file" "forecast"
     "shock_decomposition" "realtime_shock_decomposition"
     "plot_shock_decomposition" "initial_condition_decomposition"
-    "squeeze_shock_decomposition" "sbvar"
-    "ms_estimation" "ms_simulation" "ms_compute_mdd" "ms_compute_probabilities"
-    "ms_forecast" "ms_irf" "ms_variance_decomposition" "conditional_forecast"
-    "plot_conditional_forecast" "method_of_moments"
-    "markov_switching" "svar" "svar_global_identification_check"
-    "external_function" "calib_smoother" "model_diagnostics" "extended_path"
-    "smoother2histval" "perfect_foresight_setup" "perfect_foresight_solver"
+    "squeeze_shock_decomposition" "sbvar" "ms_estimation" "ms_simulation"
+    "ms_compute_mdd" "ms_compute_probabilities" "ms_forecast" "ms_irf"
+    "ms_variance_decomposition" "conditional_forecast"
+    "plot_conditional_forecast" "method_of_moments" "markov_switching" "svar"
+    "svar_global_identification_check" "external_function" "calib_smoother"
+    "model_diagnostics" "extended_path" "smoother2histval"
+    "perfect_foresight_setup" "perfect_foresight_solver"
     "perfect_foresight_with_expectation_errors_setup"
-    "perfect_foresight_with_expectation_errors_solver"
-    "compilation_setup" "model_remove" "model_options" "var_remove" "resid"
-    "std" "corr" "prior_function" "posterior_function" "end")
+    "perfect_foresight_with_expectation_errors_solver" "compilation_setup"
+    "model_remove" "model_options" "var_remove" "resid" "std" "corr"
+    "prior_function" "posterior_function" "end")
   "Dynare statement keywords.")
 
 ;; Keywords that may appear in blocks, and that begin a statement which will be
 ;; closed by a semicolon
 (defvar dynare-statements-like
-  '("stderr" "values" "periods" "scales" "restriction" "exclusion" "upper_cholesky" "lower_cholesky"
-    "bind" "relax" "error_bind" "error_relax" "add" "multiply")
+  '("stderr" "values" "periods" "scales" "restriction" "exclusion"
+    "upper_cholesky" "lower_cholesky" "equation" "bind" "relax" "error_bind"
+    "error_relax" "add" "multiply" "target" "auxname_target_nonstationary"
+    "component" "growth" "auxname" "kind")
   "Dynare statements-like keywords.")
 
 ;; Those keywords that makes the lexer enter the DYNARE_BLOCK start condition
@@ -96,13 +97,15 @@
 ;; referenced in another eval-when-compile statement in dynare-calculate-indentation
 (eval-when-compile
   (defvar dynare-blocks
-    '("model" "steady_state_model" "initval" "endval" "histval" "shocks" "heteroskedastic_shocks"
-      "shock_groups" "init2shocks" "mshocks" "estimated_params" "epilogue" "priors"
-      "estimated_params_init" "estimated_params_bounds" "estimated_params_remove"
-      "osr_params_bounds" "observation_trends" "deterministic_trends" "optim_weights"
-      "homotopy_setup" "conditional_forecast_paths" "svar_identification"
-      "moment_calibration" "irf_calibration" "ramsey_constraints" "generate_irfs"
-      "matched_moments" "occbin_constraints" "model_replace" "verbatim" "pac_target_info")
+    '("model" "steady_state_model" "initval" "endval" "histval"
+      "filter_initial_state" "shocks" "heteroskedastic_shocks" "shock_groups"
+      "init2shocks" "mshocks" "estimated_params" "epilogue" "priors"
+      "estimated_params_init" "estimated_params_bounds"
+      "estimated_params_remove" "osr_params_bounds" "observation_trends"
+      "deterministic_trends" "optim_weights" "homotopy_setup"
+      "conditional_forecast_paths" "svar_identification" "moment_calibration"
+      "irf_calibration" "ramsey_constraints" "generate_irfs" "matched_moments"
+      "occbin_constraints" "model_replace" "pac_target_info" "verbatim")
     "Dynare block keywords."))
 
 ;; Mathematical functions and operators used in model equations (see "hand_side" in Bison file)

tests/estimation/dsmc/fs2000.mod

@@ -0,0 +1,85 @@
+// See fs2000.mod in the examples/ directory for details on the model
+
+var m P c e W R k d n l gy_obs gp_obs y dA;
+varexo e_a e_m;
+
+parameters alp bet gam mst rho psi del;
+
+alp = 0.33;
+bet = 0.99;
+gam = 0.003;
+mst = 1.011;
+rho = 0.7;
+psi = 0.787;
+del = 0.02;
+
+model;
+dA = exp(gam+e_a);
+log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
+W = l/n;
+-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
+c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+P*c = m;
+m-1+d = l;
+e = exp(e_a);
+y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+gy_obs = dA*y/y(-1);
+gp_obs = (P/P(-1))*m(-1)/dA;
+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;
+
+
+shocks;
+var e_a; stderr 0.014;
+var e_m; stderr 0.005;
+end;
+
+steady;
+
+check;
+
+estimated_params;
+alp, beta_pdf, 0.356, 0.02;
+bet, beta_pdf, 0.993, 0.002;
+gam, normal_pdf, 0.0085, 0.003;
+mst, normal_pdf, 1.0002, 0.007;
+rho, beta_pdf, 0.129, 0.223;
+psi, beta_pdf, 0.65, 0.05;
+del, beta_pdf, 0.01, 0.005;
+stderr e_a, inv_gamma_pdf, 0.035449, inf;
+stderr e_m, inv_gamma_pdf, 0.008862, inf;
+end;
+
+varobs gp_obs gy_obs;
+
+options_.solve_tolf = 1e-12;
+
+estimation(order=1,datafile='../fsdat_simul.m',nobs=192,loglinear,posterior_sampling_method='dsmh');

tests/estimation/hssmc/fs2000.mod

@@ -0,0 +1,91 @@
+// See fs2000.mod in the examples/ directory for details on the model
+
+var m P c e W R k d n l gy_obs gp_obs y dA;
+varexo e_a e_m;
+
+parameters alp bet gam mst rho psi del;
+
+alp = 0.33;
+bet = 0.99;
+gam = 0.003;
+mst = 1.011;
+rho = 0.7;
+psi = 0.787;
+del = 0.02;
+
+model;
+dA = exp(gam+e_a);
+log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
+W = l/n;
+-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
+c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+P*c = m;
+m-1+d = l;
+e = exp(e_a);
+y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+gy_obs = dA*y/y(-1);
+gp_obs = (P/P(-1))*m(-1)/dA;
+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;
+
+
+shocks;
+var e_a; stderr 0.014;
+var e_m; stderr 0.005;
+end;
+
+steady;
+
+check;
+
+estimated_params;
+alp, beta_pdf, 0.356, 0.02;
+bet, beta_pdf, 0.993, 0.002;
+gam, normal_pdf, 0.0085, 0.003;
+mst, normal_pdf, 1.0002, 0.007;
+rho, beta_pdf, 0.129, 0.223;
+psi, beta_pdf, 0.65, 0.05;
+del, beta_pdf, 0.01, 0.005;
+stderr e_a, inv_gamma_pdf, 0.035449, inf;
+stderr e_m, inv_gamma_pdf, 0.008862, inf;
+end;
+
+varobs gp_obs gy_obs;
+
+options_.solve_tolf = 1e-12;
+
+estimation(order=1, datafile='../fsdat_simul.m', nobs=192, loglinear,
+           posterior_sampling_method='hssmc',
+           posterior_sampler_options=('steps',10,
+                                      'lambda',2,
+                                      'particles', 20000,
+                                      'scale',.5,
+                                      'target', .25));

tests/estimation/method_of_moments/AnScho/AnScho_MoM_common.inc

@@ -269,8 +269,6 @@ method_of_moments(
     % , lyapunov_complex_threshold = 1e-15     % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
     % , lyapunov_fixed_point_tol = 1e-10       % convergence criterion used in the fixed point Lyapunov solver
     % , lyapunov_doubling_tol = 1e-16          % convergence criterion used in the doubling algorithm
-    % , sylvester = default                    % algorithm to solve Sylvester equation; possible values are DEFAULT, FIXED_POINT
-    % , sylvester_fixed_point_tol = 1e-12      % convergence criterion used in the fixed point Sylvester solver
     % , qz_criterium = 0.999999                % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems
     % , qz_zero_threshold = 1e-6               % value used to test if a generalized eigenvalue is 0/0 in the generalized Schur decomposition
     % , schur_vec_tol=1e-11                    % tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix

tests/estimation/method_of_moments/RBC/RBC_MoM_Andreasen.mod

@@ -218,8 +218,6 @@ method_of_moments(
         % , lyapunov_complex_threshold = 1e-15     % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
         % , lyapunov_fixed_point_tol = 1e-10       % convergence criterion used in the fixed point Lyapunov solver
         % , lyapunov_doubling_tol = 1e-16          % convergence criterion used in the doubling algorithm
-        % , sylvester = default                    % algorithm to solve Sylvester equation; possible values are DEFAULT, FIXED_POINT
-        % , sylvester_fixed_point_tol = 1e-12      % convergence criterion used in the fixed point Sylvester solver
         % , qz_criterium = 0.999999                % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems
         % , qz_zero_threshold = 1e-6               % value used to test if a generalized eigenvalue is 0/0 in the generalized Schur decomposition
         % , schur_vec_tol=1e-11                    % tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix

tests/estimation/method_of_moments/RBC/RBC_MoM_SMM_ME.mod

@@ -205,8 +205,6 @@ end
         % , lyapunov_complex_threshold = 1e-15     % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
         % , lyapunov_fixed_point_tol = 1e-10       % convergence criterion used in the fixed point Lyapunov solver
         % , lyapunov_doubling_tol = 1e-16          % convergence criterion used in the doubling algorithm
-        % , sylvester = default                    % algorithm to solve Sylvester equation; possible values are DEFAULT, FIXED_POINT
-        % , sylvester_fixed_point_tol = 1e-12      % convergence criterion used in the fixed point Sylvester solver
         % , qz_criterium = 0.999999                % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems
         % , qz_zero_threshold = 1e-6               % value used to test if a generalized eigenvalue is 0/0 in the generalized Schur decomposition
         % , schur_vec_tol=1e-11                    % tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix

tests/estimation/method_of_moments/RBC/RBC_MoM_prefilter.mod

@@ -177,8 +177,6 @@ save('test_matrix.mat','weighting_matrix')
         % , lyapunov_complex_threshold = 1e-15     % complex block threshold for the upper triangular matrix in symmetric Lyapunov equation solver
         % , lyapunov_fixed_point_tol = 1e-10       % convergence criterion used in the fixed point Lyapunov solver
         % , lyapunov_doubling_tol = 1e-16          % convergence criterion used in the doubling algorithm
-        % , sylvester = default                    % algorithm to solve Sylvester equation; possible values are DEFAULT, FIXED_POINT
-        % , sylvester_fixed_point_tol = 1e-12      % convergence criterion used in the fixed point Sylvester solver
         % , qz_criterium = 0.999999                % value used to split stable from unstable eigenvalues in reordering the Generalized Schur decomposition used for solving first order problems
         % , qz_zero_threshold = 1e-6               % value used to test if a generalized eigenvalue is 0/0 in the generalized Schur decomposition
         % , schur_vec_tol=1e-11                    % tolerance level used to find nonstationary variables in Schur decomposition of the transition matrix

tests/estimation/slice/fs2000_slice.mod

@@ -1,94 +1,94 @@
-// See fs2000.mod in the examples/ directory for details on the model
-
-var m P c e W R k d n l gy_obs gp_obs y dA;
-varexo e_a e_m;
-
-parameters alp bet gam mst rho psi del;
-
-alp = 0.33;
-bet = 0.99;
-gam = 0.003;
-mst = 1.011;
-rho = 0.7;
-psi = 0.787;
-del = 0.02;
-
-model;
-dA = exp(gam+e_a);
-log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
--P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
-W = l/n;
--(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
-R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
-1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
-c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
-P*c = m;
-m-1+d = l;
-e = exp(e_a);
-y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
-gy_obs = dA*y/y(-1);
-gp_obs = (P/P(-1))*m(-1)/dA;
-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;
-
-shocks;
-var e_a; stderr 0.014;
-var e_m; stderr 0.005;
-end;
-
-steady;
-
-check;
-
-estimated_params;
-alp, beta_pdf, 0.356, 0.02;
-bet, beta_pdf, 0.993, 0.002;
-gam, normal_pdf, 0.0085, 0.003;
-mst, normal_pdf, 1.0002, 0.007;
-rho, beta_pdf, 0.129, 0.05;
-psi, beta_pdf, 0.65, 0.05;
-del, beta_pdf, 0.01, 0.005;
-stderr e_a, inv_gamma_pdf, 0.035449, inf;
-stderr e_m, inv_gamma_pdf, 0.008862, inf;
-end;
-
-varobs gp_obs gy_obs;
-
-//options_.posterior_sampling_method = 'slice';
-estimation(order=1,datafile='../fsdat_simul',nobs=192,silent_optimizer,loglinear,mh_replic=50,mh_nblocks=2,mh_drop=0.2, //mode_compute=0,cova_compute=0,
-posterior_sampling_method='slice'
-);
-// continue with rotated slice
-estimation(order=1,datafile='../fsdat_simul',silent_optimizer,nobs=192,loglinear,mh_replic=100,mh_nblocks=2,mh_drop=0.5,load_mh_file,//mode_compute=0,
-posterior_sampling_method='slice',
-posterior_sampler_options=('rotated',1,'use_mh_covariance_matrix',1)
-);
-
-options_.TeX=1;
-generate_trace_plots(1:2);
-options_.TeX=1;
+// See fs2000.mod in the examples/ directory for details on the model
+
+var m P c e W R k d n l gy_obs gp_obs y dA;
+varexo e_a e_m;
+
+parameters alp bet gam mst rho psi del;
+
+alp = 0.33;
+bet = 0.99;
+gam = 0.003;
+mst = 1.011;
+rho = 0.7;
+psi = 0.787;
+del = 0.02;
+
+model;
+dA = exp(gam+e_a);
+log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
+W = l/n;
+-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
+c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+P*c = m;
+m-1+d = l;
+e = exp(e_a);
+y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+gy_obs = dA*y/y(-1);
+gp_obs = (P/P(-1))*m(-1)/dA;
+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;
+
+shocks;
+var e_a; stderr 0.014;
+var e_m; stderr 0.005;
+end;
+
+steady;
+
+check;
+
+estimated_params;
+alp, beta_pdf, 0.356, 0.02;
+bet, beta_pdf, 0.993, 0.002;
+gam, normal_pdf, 0.0085, 0.003;
+mst, normal_pdf, 1.0002, 0.007;
+rho, beta_pdf, 0.129, 0.05;
+psi, beta_pdf, 0.65, 0.05;
+del, beta_pdf, 0.01, 0.005;
+stderr e_a, inv_gamma_pdf, 0.035449, inf;
+stderr e_m, inv_gamma_pdf, 0.008862, inf;
+end;
+
+varobs gp_obs gy_obs;
+
+//options_.posterior_sampling_method = 'slice';
+estimation(order=1,datafile='../fsdat_simul',nobs=192,silent_optimizer,loglinear,mh_replic=50,mh_nblocks=2,mh_drop=0.2, //mode_compute=0,cova_compute=0,
+posterior_sampling_method='slice'
+);
+// continue with rotated slice
+estimation(order=1,datafile='../fsdat_simul',silent_optimizer,nobs=192,loglinear,mh_replic=100,mh_nblocks=2,mh_drop=0.5,load_mh_file,//mode_compute=0,
+posterior_sampling_method='slice',
+posterior_sampler_options=('rotated',1,'use_mh_covariance_matrix',1)
+);
+
+options_.TeX=1;
+generate_trace_plots(1:2);
+options_.TeX=1;

windows/build.sh

@@ -53,15 +53,15 @@ ln -s "$ROOT_DIRECTORY"/deps/mkoctfile64 /tmp/windeps/
 # Go to source root directory
 cd ..
 
-common_meson_opts=(-Dbuildtype=release --cross-file scripts/windows-cross.ini)
+common_meson_opts=(-Dbuildtype=release --cross-file windows/mingw-cross.ini)
 
 # Create Windows 64-bit DLL binaries for MATLAB ≥ R2018b
-meson setup --cross-file scripts/windows-cross-matlab.ini -Dmatlab_path=/tmp/windeps/matlab64/R2018b \
+meson setup --cross-file windows/mingw-cross-matlab.ini -Dmatlab_path=/tmp/windeps/matlab64/R2018b \
       "${common_meson_opts[@]}" build-win-matlab
 meson compile -v -C build-win-matlab
 
 # Create Windows DLL binaries for Octave/MinGW (64bit)
-meson setup --cross-file scripts/windows-cross-octave.ini \
+meson setup --cross-file windows/mingw-cross-octave.ini \
       "${common_meson_opts[@]}" build-win-octave
 meson compile -v -C build-win-octave
 

windows/deps/versions.mk

@@ -18,7 +18,7 @@ MATLAB64_VERSION = 20231122
 ## Build dependencies
 
 # pacman -Ss mingw-w64-x86_64-boost
-MINGW64_BOOST_VERSION = 1.83.0-1
+MINGW64_BOOST_VERSION = 1.83.0-2
 
 # pacman -Ss mingw-w64-x86_64-gsl
 MINGW64_GSL_VERSION = 2.7.1-2
@@ -40,11 +40,11 @@ MINGW64_LIBAEC_VERSION = 1.0.6-2
 
 # Dependency of HDF5
 # pacman -Ss mingw-w64-x86_64-openssl
-MINGW64_OPENSSL_VERSION = 3.1.4-1
+MINGW64_OPENSSL_VERSION = 3.2.0-1
 
 # Dependency of HDF5
 # pacman -Ss mingw-w64-x86_64-curl
-MINGW64_CURL_VERSION = 8.4.0-1
+MINGW64_CURL_VERSION = 8.5.0-1
 
 # Dependency of curl (and of the MinGW compiler)
 # pacman -Ss mingw-w64-x86_64-zstd
@@ -81,22 +81,22 @@ MINGW64_LIBUNISTRING_VERSION = 1.1-1
 ## MinGW packages for the embedded compiler
 
 # pacman -Ss mingw-w64-x86_64-gcc$
-MINGW64_GCC_VERSION = 13.2.0-2
+MINGW64_GCC_VERSION = 13.2.0-3
 
 # pacman -Ss mingw-w64-x86_64-gmp
 MINGW64_GMP_VERSION = 6.3.0-2
 
 # pacman -Ss mingw-w64-x86_64-binutils
-MINGW64_BINUTILS_VERSION = 2.41-2
+MINGW64_BINUTILS_VERSION = 2.41-3
 
 # pacman -Ss mingw-w64-x86_64-headers-git
-MINGW64_HEADERS_VERSION = 11.0.0.r404.g3a137bd87-1
+MINGW64_HEADERS_VERSION = 11.0.0.r442.ga27e7b27e-1
 
 # pacman -Ss mingw-w64-x86_64-crt-git
-MINGW64_CRT_VERSION = 11.0.0.r404.g3a137bd87-1
+MINGW64_CRT_VERSION = 11.0.0.r442.ga27e7b27e-1
 
 # pacman -Ss mingw-w64-x86_64-winpthreads-git
-MINGW64_WINPTHREADS_VERSION = 11.0.0.r404.g3a137bd87-1
+MINGW64_WINPTHREADS_VERSION = 11.0.0.r442.ga27e7b27e-1
 
 # pacman -Ss mingw-w64-x86_64-isl
 MINGW64_ISL_VERSION = 0.26-1

windows/mingw-cross.ini

@@ -1,5 +1,5 @@
 # Meson common cross file for targeting Windows from Linux
-# To be included after windows-cross-{matlab,octave}.ini
+# To be included after mingw-cross-{matlab,octave}.ini
 
 [constants]
 # For the architectural baseline, we follow MSYS2: