diff --git a/matlab/cli/dcontrib.m b/matlab/cli/dcontrib.m
new file mode 100644
index 0000000000000000000000000000000000000000..ab1363392773516733fe0f96a2abfcfd52aa5bbf
--- /dev/null
+++ b/matlab/cli/dcontrib.m
@@ -0,0 +1,347 @@
+function dcontrib(varargin)
+
+% Computes dynamic contributions to a subset of endogenous variables in a semi structural model.
+%
+% EXAMPLE
+%
+% >> dcontrib --model sandbox.mod --tags zpac eq:x1 --database ds --output results --range 2023Q1:2073Q1
+%
+% zpac and eq:x1 are the equation tags of the equations determining the endogenous variables for which we want to compute
+% the contributions of the other (exogenous) variables, sandbox.mod is the name of the file from which we exctract these
+% equations, ds is a dseries object containing the data, 2023Q1:2073Q1 is the time range over which we compute the
+% contributions, and results the name of the structure containing the contributions (as dseries objects) for each endogenous
+% variable.
+%
+% INPUTS
+% --model                name of a mod file (with extension)
+% --tags                 list of equations (equation tags assocated to the endogenous variables for which we want to compute the contributions)
+% --database             dseries object
+% --baseline             dseries object (path for the exogenous variables)
+% --range                followed by a dates range
+%
+% REMARKS
+% [1] --baseline and --range are not compatible.
+% [2] --variables is followed by a space separated list of names, it is assumed that each variable is associated with an equation tag.
+
+% 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/>.
+
+    global M_
+
+    if nargin==1 && strcmpi(varargin{1}, '--help')
+        skipline()
+        disp('--model     followed by the name of a mod file (with extension) [mandatory]')
+        disp('--tags      followed by a list of equation tags [mandatory]')
+        disp('--database  followed by dseries object [mandatory]')
+        disp('--baseline  followed by dseries object (path for the exogenous variables)')
+        disp('--range     followed by a dates range')
+        disp('--output    followed by a name for the structure holding the results [mandatory]')
+        skipline()
+        return
+    end
+
+    model = getmodel(varargin);
+
+    % First call to dynare to obtain the json verison of the model.
+    dynare(model(1:end-4), 'nopreprocessoroutput', 'notime', 'json=compute')
+    delete(sprintf('%s.log', model(1:end-4)))
+
+    eqtags = geteqtags(varargin);
+    variables = cell(length(eqtags), 1);
+    for i=1:length(eqtags)
+        variables(i) = get_variables_and_parameters_in_expression(get_lhs_and_rhs(eqtags{i}, M_, true));
+    end
+
+    % Cherry pick equations required for the decomposition.
+    cherrypickdir = sprintf('cherry-pick-%s', randomstring(10));
+    cherrypick(model(1:end-4), cherrypickdir, eqtags, false);
+    rmdir(model(1:end-4), 's')
+    rmdir(sprintf('+%s', model(1:end-4)), 's')
+    modfilename = sprintf('dcontrib_%s.mod', randomstring(10));
+    aggregate(modfilename, {}, '', cherrypickdir);
+    rmdir(cherrypickdir, 's')
+
+    % Second call to dynare (on the exctracted equations)
+    dynare(modfilename(1:end-4), 'nopreprocessoroutput', 'notime', 'json=compute')
+
+    % Get dataset
+    dname = getdatasetname(varargin);
+    ds = evalin('caller', dname);
+    if ~isdseries(ds)
+        error('dcontrib:getdataset: --dataset must be followed by a dseries object.')
+    end
+
+    % Create a dseries object for the paths of the exogenous variables
+    xvariables = ds{M_.exo_names{:}};
+
+    % Get initial and terminal periods (if defined)
+    [firstperiod, lastperiod] = getperiods(varargin);
+    if firstperiod<=ds.dates(1)+M_.orig_maximum_lag
+        error('dcontrib:: Try increase firstperiod (>%s).', char(ds.dates(1)+M_.orig_maximum_lag))
+    end
+    if lastperiod>ds.dates(end)
+        error('dcontrib:: Try reduce lastperiod (<=%s).', char(ds.dates(end)))
+    end
+
+    % Load baseline (if it makes sense)
+    if isempty(firstperiod)
+        baselinename = getbaselinename(varargin);
+        baseline = evalin('caller', baselinename);
+        if ~isdseries(baseline)
+            error('dcontrib:getdataset: --baseline must be followed by a dseries object.')
+        end
+        firstperiod = baseline.dates(1);
+        lastperiod = baseline.dates(end);
+        baseline = baseline{M_.exo_names{:}};
+    else
+        % Set default baseline (exogenous variable levels in firstperiod-1)
+        baseline = xvariables(firstperiod-1);
+        baseline = repmat(baseline.data, lastperiod-firstperiod+1, 1);
+        baseline = dseries(baseline, firstperiod, M_.exo_names);
+    end
+
+    % Restrict the observations for the exogenous variables to the pertinent tim range
+    xvariables = xvariables(firstperiod:lastperiod);
+
+    % Set initial conditions for the simulation.
+    initialconditions = ds(ds.dates(1):firstperiod-1);
+
+    % Simulation on the baseline (track the effects of the initial state if the model is autoregressive)
+    S.baseline = simul_backward_model(initialconditions, lastperiod-firstperiod+1, baseline);
+
+    % contributions is a dseries object holding the marginal contribution of the baseline and
+    % each exogenous variable to endogenous variable z
+    % contributions.baseline = S.baseline(firstperiod:lastperiod);
+
+    % Add exogenous variables one by one and simulate the model (-> cumulated contributions)
+    for i=1:xvariables.vobs
+        name = xvariables.name{i};
+        baseline{name} = xvariables{name};
+        S.(name) = simul_backward_model(initialconditions, lastperiod-firstperiod+1, baseline);
+    end
+
+    % Compute marginal contributions
+    for j=1:length(variables)
+        cumulatedcontribs = S.baseline{variables{j}}(firstperiod:lastperiod).data;
+        contributions.(variables{j}) = dseries(cumulatedcontribs, firstperiod, 'baseline');
+        for i=1:xvariables.vobs
+            name = xvariables.name{i};
+            ts = S.(name);
+            data = ts{variables{j}}(firstperiod:lastperiod).data;
+            contributions.(variables{j}) = [contributions.(variables{j}), dseries(data-cumulatedcontribs, firstperiod, name)];
+            cumulatedcontribs = data;
+        end
+        contributions.(variables{j}) = contributions.(variables{j})(firstperiod:lastperiod);
+    end
+
+    % Save output in caller workspace
+    oname = getoutputname(varargin);
+    assignin('caller', oname, contributions)
+
+    % Cleanup
+    rmdir(modfilename(1:end-4), 's')
+    rmdir(sprintf('+%s', modfilename(1:end-4)), 's')
+    delete(sprintf('%s.mod', modfilename(1:end-4)))
+    delete(sprintf('%s.log', modfilename(1:end-4)))
+end
+
+
+function model = getmodel(cellarray)
+
+    % Return variables for which we want to compute the contributions.
+    %
+    % INPUTS
+    % - cellarray     [char]      1×n cell array of row char arrays.
+    %
+    % OUTPUTS
+    % - var           [char]      name of the model (with extension)
+
+    mpos = positions(cellarray);
+
+    model = cellarray{mpos+1};
+
+end
+
+
+function eqtags = geteqtags(cellarray)
+
+% Return equation tags for the equations we want to compute the contributions.
+%
+% INPUTS
+% - cellarray     [char]      1×n cell array of row char arrays.
+%
+% OUTPUTS
+% - eqtags        [char]      1×p cell array of row char arrays.
+
+    [~, vpos, ~, ~, ~, ~, indices] = positions(cellarray);
+
+    lastvalue = indices(find(indices==vpos)+1)-1;
+
+    eqtags = cellarray(vpos+1:lastvalue);
+
+end
+
+
+function dname = getdatasetname(cellarray)
+
+% Return the name of the dataset.
+%
+% INPUTS
+% - cellarray     [char]      1×n cell array of row char arrays.
+%
+% OUTPUTS
+% - dname         [char]      dataset name for endogenous and exogenous variables
+
+    [~, ~, dpos] = positions(cellarray);
+
+    dname = cellarray{dpos+1};
+
+end
+
+
+function [firstperiod, lastperiod] = getperiods(cellarray)
+
+% Return variables for which we want to compute the contributions.
+%
+% INPUTS
+% - cellarray     [char]      1×n cell array of row char arrays.
+%
+% OUTPUTS
+% - ds            [dseries]   dataset for endogenous and exogenous variables
+
+    [~, ~, ~, rpos] = positions(cellarray);
+
+    firstperiod = dates();
+    lastperiod = dates();
+
+    if ~isempty(rpos)
+        try
+            tmp = strsplit(cellarray{rpos+1},':');
+            firstperiod = dates(tmp{1});
+            lastperiod = dates(tmp{2});
+        catch
+            error('dcontrib:getperiods: Cannot convert the --range argument to dates objects.')
+        end
+        if lastperiod<=firstperiod
+            error('dcontrib:getperiods: In --range A:B we must have B>A.')
+        end
+    end
+
+end
+
+
+function dname = getbaselinename(cellarray)
+
+% Return the name of the dataset.
+%
+% INPUTS
+% - cellarray     [char]      1×n cell array of row char arrays.
+%
+% OUTPUTS
+% - dname         [char]      baseline name for endogenous and exogenous variables
+
+    [~, ~, ~, ~, bpos] = positions(cellarray);
+
+    dname = cellarray{bpos+1};
+
+end
+
+
+function oname = getoutputname(cellarray)
+
+% Return the name of the output.
+%
+% INPUTS
+% - cellarray     [char]      1×n cell array of row char arrays.
+%
+% OUTPUTS
+% - dname         [char]      baseline name for endogenous and exogenous variables
+
+    [~, ~, ~, ~, ~, opos] = positions(cellarray);
+
+    oname = cellarray{opos+1};
+
+end
+
+
+function [mpos, vpos, dpos, rpos, bpos, opos, indices] = positions(cellarray)
+
+    % Return  positions of the arguments.
+    %
+    % INPUTS
+    % - cellarray     [char]      1×n cell array of row char arrays.
+    %
+    % OUTPUTS
+    % - mpos          [integer]   scalar, index for the --model argument.
+    % - vpos          [integer]   scalar, index for the --tags arguments.
+    % - dpos          [integer]   scalar, index for the --database argument.
+    % - rpos          [integer]   scalar, index for the --range argument.
+    % - bpos          [integer]   scalar. index for the --baseline argument.
+    % - opos          [integer]   scalar, index for the --output argument.
+
+    % Index for --model argument
+    mpos = find(strcmp('--model', cellarray));
+    if isempty(mpos)
+        error('dcontrib::positions: --model argument is mandatory.')
+    elseif length(mpos)>1
+        error('dplot::positions: Only one --model argument is allowed.')
+    end
+
+    % Index for --tags argument
+    vpos = find(strcmp('--tags', cellarray));
+    if isempty(vpos)
+        error('dplot::positions: --tags argument is mandatory.')
+    elseif length(vpos)>1
+        error('dplot::positions: Only one --tags argument is allowed.')
+    end
+
+    % Index for the --initialconditions argument
+     dpos = find(strcmp('--database', cellarray));
+     if isempty(dpos)
+        error('dplot::positions: --database argument is mandatory.')
+    elseif length(dpos)>1
+        error('dplot::positions: Only one --database argument is allowed.')
+    end
+
+    % Index for the --range argument
+    rpos = find(strcmp('--range', cellarray));
+    if length(rpos)>1
+        error('dplot::positions: Only one --range argument is allowed.')
+    end
+
+    % Index for the --baseline argument
+    bpos = find(strcmp('--baseline', cellarray));
+    if length(bpos)>1
+        error('dplot::positions: Only one --baseline argument is allowed.')
+    end
+
+    if ~isempty(rpos) && ~isempty(bpos)
+        error('dplot::positions: --baseline and --range arguments are not allowed simultaneously.')
+    end
+
+    % Index for the --output argument.
+    opos = find(strcmp('--output', cellarray));
+    if isempty(opos)
+        error('dplot::positions: --output argument is mandatory.')
+    elseif length(opos)>1
+        error('dplot::positions: Only one --periods argument is allowed.')
+    end
+
+    % Sorted vector of indices
+    indices = sort([mpos; vpos; dpos; rpos; bpos; opos]);
+
+end
diff --git a/tests/Makefile.am b/tests/Makefile.am
index 2d974779e82130a906b3c0880a8d579924afe02e..2817e9886270087226921d9cf4fc9f379bcfdd83 100644
--- a/tests/Makefile.am
+++ b/tests/Makefile.am
@@ -1269,7 +1269,8 @@ M_TRS_FILES += 	run_block_byte_tests_matlab.m.trs \
 		nonlinearsolvers.m.trs \
 		cyclereduction.m.trs \
 		logarithmicreduction.m.trs \
-		riccatiupdate.m.trs
+		riccatiupdate.m.trs \
+		contribs.m.trs
 
 M_XFAIL_TRS_FILES = $(patsubst %.mod, %.m.trs, $(XFAIL_MODFILES))
 
@@ -1284,7 +1285,8 @@ O_TRS_FILES += 	run_block_byte_tests_octave.o.trs \
 		nonlinearsolvers.o.trs \
 		cyclereduction.o.trs \
 		logarithmicreduction.o.trs \
-		riccatiupdate.o.trs
+		riccatiupdate.o.trs \
+		contribs.o.trs
 
 O_XFAIL_TRS_FILES = $(patsubst %.mod, %.o.trs, $(XFAIL_MODFILES))
 
@@ -1491,7 +1493,10 @@ EXTRA_DIST = \
 	solve_algo_12_14/simul_backward_common.inc \
 	solve_algo_12_14/purely_backward_common.inc \
 	solve_algo_12_14/purely_static_common.inc \
-	solve_algo_12_14/purely_forward_common.inc
+	solve_algo_12_14/purely_forward_common.inc \
+	sandbox.mod \
+	simulateddata.m
+
 
 if ENABLE_MATLAB
 check-local: check-matlab
diff --git a/tests/contribs.m b/tests/contribs.m
new file mode 100644
index 0000000000000000000000000000000000000000..94efddf9605a9390e71e395b1e1dc90ae730b755
--- /dev/null
+++ b/tests/contribs.m
@@ -0,0 +1,82 @@
+debug = false;
+
+if debug
+    [top_test_dir, ~, ~] = fileparts(mfilename('fullpath'));
+else
+    top_test_dir = getenv('TOP_TEST_DIR');
+end
+
+addpath(sprintf('%s/matlab', top_test_dir(1:end-6)))
+
+if ~debug
+    % Test Dynare Version
+    if ~strcmp(dynare_version(), getenv('DYNARE_VERSION'))
+        error('Incorrect version of Dynare is being tested')
+    end
+end
+
+dynare_config;
+
+NumberOfTests = 0;
+testFailed = 0;
+
+if ~debug
+    skipline()
+    disp('***  TESTING: nonlinearsolvers.m ***');
+end
+
+%
+% TEST
+%
+
+t0 = clock;
+
+NumberOfTests = NumberOfTests+1;
+
+try
+    dataset = dseries('simulateddata.m');
+
+    dcontrib --model sandbox.mod --tags zpac eq:x1 --database dataset --output results --range 2023Q1:2073Q1
+
+    if max(abs(sum(results.z.data, 2)-dataset.z(dates('2023Q1'):dates('2073Q1')).data))>1e-5
+
+        error('Computation of dynamic contributions failed.')
+    end
+
+catch
+
+    testFailed = testFailed+1;
+
+end
+
+t1 = clock;
+
+
+if ~debug
+    cd(getenv('TOP_TEST_DIR'));
+else
+    dprintf('FAILED tests: %i', testFailed)
+end
+
+if  isoctave
+    fid = fopen('contribs.o.trs', 'w+');
+else
+    fid = fopen('contribs.m.trs', 'w+');
+end
+if testFailed
+    fprintf(fid,':test-result: FAIL\n');
+    fprintf(fid,':list-of-failed-tests: nonlinearsolvers.m\n');
+else
+    fprintf(fid,':test-result: PASS\n');
+end
+fprintf(fid,':number-tests: %i\n', NumberOfTests);
+fprintf(fid,':number-failed-tests: %i\n', testFailed);
+fprintf(fid,':elapsed-time: %f\n', etime(t1, t0));
+fclose(fid);
+
+if ~debug
+    exit;
+end
+%
+% END OF TEST
+%
diff --git a/tests/sandbox.mod b/tests/sandbox.mod
new file mode 100644
index 0000000000000000000000000000000000000000..6b4a3556a9963628371ff9552a70d8f47e875b0a
--- /dev/null
+++ b/tests/sandbox.mod
@@ -0,0 +1,134 @@
+// --+ options: json=compute, stochastic +--
+
+@#define simulate = false
+
+var x1 x2 x1bar x2bar z y x u v s azertyuiopiop z1 z2 z3;
+
+varexo ex1
+       ex2
+       ex1bar
+       ex2bar
+       ez
+       ey
+       ex
+       eu
+       ev
+       es;
+
+parameters
+       rho_1 rho_2 rho_3 rho_4
+       a_x1_0 a_x1_1 a_x1_2 a_x1_x2_1 a_x1_x2_2
+	   a_x2_0 a_x2_1 a_x2_2 a_x2_x1_1 a_x2_x1_2
+	   e_c_m c_z_1 c_z_2 c_z_dx2 c_z_u c_z_dv c_z_s cx cy beta
+       lambda;
+
+rho_1 =  .9;
+rho_2 = -.2;
+rho_3 =  .4;
+rho_4 = -.3;
+
+
+a_x1_0 =  -.9;
+a_x1_1 =  .4;
+a_x1_2 =  .3;
+a_x1_x2_1 = .1;
+a_x1_x2_2 = .2;
+
+a_x2_0 =  -.9;
+a_x2_1 =   .2;
+a_x2_2 =  -.1;
+a_x2_x1_1 = -.1;
+a_x2_x1_2 = .2;
+
+beta  =  .2;
+e_c_m =  .5;
+c_z_1 =  .2;
+c_z_2 = -.1;
+c_z_dx2 = .3;
+c_z_u = .3;
+c_z_dv = .4;
+c_z_s  = -.2;
+cx = 1.0;
+cy = 1.0;
+
+
+lambda = 0.5; // Share of optimizing agents.
+
+trend_component_model(model_name=toto, eqtags=['eq:x1', 'eq:x2', 'eq:x1bar', 'eq:x2bar'], targets=['eq:x1bar', 'eq:x2bar']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman, auxname=rototo);
+
+model;
+
+[name='eq:u']
+s = .3*s(-1) - .1*s(-2) + es;
+
+[name='eq:diff(v)']
+diff(v) = .5*diff(v(-1)) + ev;
+
+[name='eq:u']
+u = .5*u(-1) - .2*u(-2) + eu;
+
+[name='eq:y']
+y = rho_1*y(-1) + rho_2*y(-2) + ey;
+
+[name='eq:x']
+x = rho_3*x(-1) + rho_4*x(-2) + ex;
+
+[name='eq:azertyuiopiop', rename='azertyuiopiop->qsdfghjklm']
+azertyuiopiop = x + y;
+
+[name='eq:x1']
+diff(x1) = a_x1_0*(x1(-1)-x1bar(-1)) + a_x1_1*diff(x1(-1)) + a_x1_2*diff(x1(-2)) + a_x1_x2_1*diff(x2(-1)) + a_x1_x2_2*diff(x2(-2)) + ex1;
+
+[name='eq:x2']
+diff(x2) = a_x2_0*(x2(-1)-x2bar(-1)) + a_x2_1*diff(x1(-1)) + a_x2_2*diff(x1(-2)) + a_x2_x1_1*diff(x2(-1)) + a_x2_x1_2*diff(x2(-2)) + ex2;
+
+[name='eq:x1bar']
+x1bar = x1bar(-1) + ex1bar;
+
+[name='eq:x2bar']
+x2bar = x2bar(-1) + ex2bar;
+
+[name='zpac']
+diff(z) = lambda*(e_c_m*(x1(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + c_z_s*s + c_z_dv*diff(v) ) + (1-lambda)*( cy*y + cx*x) + c_z_u*u + c_z_dx2*diff(x2) + ez;
+
+[name='z1']
+z1 = z+y-x+u;
+
+[name='z2']
+z2 = z-y+x-u;
+
+[name='z3']
+z3 = u-diff(v);
+
+end;
+
+shocks;
+  var ex1 = 1.0;
+  var ex2 = 1.0;
+  var ex1bar = 1.0;
+  var ex2bar = 1.0;
+  var ez = 1.0;
+  var ey = 0.1;
+  var ex = 0.1;
+  var eu = 0.05;
+  var ev = 0.05;
+  var es = 0.07;
+end;
+
+// Initialize the PAC model (build the Companion VAR representation for the auxiliary model).
+pac.initialize('pacman');
+
+// Update the parameters of the PAC expectation model (h0 and h1 vectors).
+pac.update.expectation('pacman');
+
+@#if simulate
+verbatim;
+// Simulate the model to create an artificial sample.
+initialconditions = dseries(zeros(10, M_.endo_nbr+M_.exo_nbr), 2000Q1, vertcat(M_.endo_names,M_.exo_names));
+TrueData = simul_backward_model(initialconditions, 300);
+TrueData.save('simulateddata', 'm')
+end;
+
+@#endif
diff --git a/tests/simulateddata.m b/tests/simulateddata.m
new file mode 100644
index 0000000000000000000000000000000000000000..5f3456fe26b47c97bfe208aec2157f96d95a849f
--- /dev/null
+++ b/tests/simulateddata.m
@@ -0,0 +1,7497 @@
+% File created on 05-Jul-2023 14:55:50.
+
+FREQ__ = 4;
+INIT__ = '2000Q1';
+
+NAMES__ = {'x1'; 'x2'; 'x1bar'; 'x2bar'; 'z'; 'y'; 'x'; 'u'; 'v'; 's'; 'azertyuiopiop'; 'z1'; 'z2'; 'z3'; 'ex1'; 'ex2'; 'ex1bar'; 'ex2bar'; 'ez'; 'ey'; 'ex'; 'eu'; 'ev'; 'es'};
+TEX__ = {'x1'; 'x2'; 'x1bar'; 'x2bar'; 'z'; 'y'; 'x'; 'u'; 'v'; 's'; 'azertyuiopiop'; 'z1'; 'z2'; 'z3'; 'ex1'; 'ex2'; 'ex1bar'; 'ex2bar'; 'ez'; 'ey'; 'ex'; 'eu'; 'ev'; 'es'};
+OPS__ = {[];[];[];[];[];[];[];[];[];[];[];[];[];[];[];[];[];[];[];[];[];[];[];[]};
+
+x1 = [
+              0
+              0
+              0
+              0
+              0
+              0
+              0
+              0
+              0
+              0
+     0.53766714
+      2.5389865
+     0.61933601
+      2.4875444
+      2.5744943
+      2.8895704
+      3.0657897
+      3.1778697
+      7.6027825
+      8.6889742
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