diff --git a/matlab/+pac/initialize.m b/matlab/+pac/initialize.m
index bc0e989c8..6d1b73753 100644
--- a/matlab/+pac/initialize.m
+++ b/matlab/+pac/initialize.m
@@ -39,4 +39,4 @@ end
 
 M_.pac.(pacmodel).auxiliary_model_type = auxiliary_model_type;
 
-get_companion_matrix(auxiliary_model_name, auxiliary_model_type);
\ No newline at end of file
+get_companion_matrix_legacy(auxiliary_model_name, auxiliary_model_type);
\ No newline at end of file
diff --git a/matlab/+var_expectation/+update/parameters.m b/matlab/+var_expectation/+update/parameters.m
index 8981686cb..b3582e294 100644
--- a/matlab/+var_expectation/+update/parameters.m
+++ b/matlab/+var_expectation/+update/parameters.m
@@ -59,7 +59,7 @@ auxcalib = DynareOutput.(varexpectationmodel.auxiliary_model_type).(varexpectati
 
 if ~isfield(auxcalib, 'CompanionMatrix') || any(isnan(auxcalib.CompanionMatrix(:)))
     message = sprintf('Auxiliary model %s has to be estimated first.', varexpectationmodel.auxiliary_model_name);
-    message = sprintf('%s\nPlease use get_companion_matrix command first.', message);
+    message = sprintf('%s\nPlease use *get_companion_matrix command first.', message);
     error(message);
 end
 
diff --git a/matlab/+var_expectation/initialize.m b/matlab/+var_expectation/initialize.m
index 06f40eab3..65b6ae6ea 100644
--- a/matlab/+var_expectation/initialize.m
+++ b/matlab/+var_expectation/initialize.m
@@ -42,4 +42,4 @@ end
 
 M_.var_expectation.(varexpectationmodel).auxiliary_model_type = auxiliary_model_type;
 
-get_companion_matrix(auxiliary_model_name, auxiliary_model_type);
\ No newline at end of file
+get_companion_matrix_legacy(auxiliary_model_name, auxiliary_model_type);
\ No newline at end of file
diff --git a/matlab/get_companion_matrix.m b/matlab/get_companion_matrix.m
index 825dc08b5..8e9929d87 100644
--- a/matlab/get_companion_matrix.m
+++ b/matlab/get_companion_matrix.m
@@ -31,7 +31,7 @@ function [A0, AR, B] = get_companion_matrix(auxiliary_model_name, auxiliary_mode
 
 global oo_ M_
 
-if nargin<2
+if nargin < 2
     if isfield(M_, 'var') && isfield(M_.var, auxiliary_model_name)
         auxiliary_model_type = 'var';
     elseif isfield(M_, 'trend_component') && isfield(M_.trend_component, auxiliary_model_name)
@@ -41,39 +41,47 @@ if nargin<2
     end
 end
 
-if nargout
-    A0 = [];
-    AR = [];
-    B = [];
+if strcmp(auxiliary_model_type, 'var')
+    AR = evalin('base', [M_.fname '.var_ar(''' auxiliary_model_name ''', M_.params)']);
+elseif strcmp(auxiliary_model_type, 'trend_component')
+    [AR, A0] = evalin('base', [M_.fname '.trend_component_ar_ec(''' auxiliary_model_name ''', M_.params)']);
+else
+    error('Unknown type of auxiliary model.')
 end
 
-get_ar_ec_matrices(auxiliary_model_name, auxiliary_model_type);
-
 % Get the number of lags
-p = size(oo_.(auxiliary_model_type).(auxiliary_model_name).ar, 3);
+p = size(AR, 3);
 
 % Get the number of variables
-n = length(oo_.(auxiliary_model_type).(auxiliary_model_name).ar(:,:,1));
+n = length(M_.(auxiliary_model_type).(auxiliary_model_name).lhs);
 
 switch auxiliary_model_type
   case 'var'
     oo_.var.(auxiliary_model_name).CompanionMatrix = zeros(n*p);
-    oo_.var.(auxiliary_model_name).CompanionMatrix(1:n,1:n) = oo_.var.(auxiliary_model_name).ar(:,:,1);
-    for i=2:p
-        oo_.var.(auxiliary_model_name).CompanionMatrix(1:n,(i-1)*n+(1:n)) = oo_.var.(auxiliary_model_name).ar(:,:,i);
+    oo_.var.(auxiliary_model_name).CompanionMatrix(1:n,1:n) = AR(:,:,1);
+    for i = 2:p
+        oo_.var.(auxiliary_model_name).CompanionMatrix(1:n,(i-1)*n+(1:n)) = AR(:,:,i);
         oo_.var.(auxiliary_model_name).CompanionMatrix((i-1)*n+(1:n),(i-2)*n+(1:n)) = eye(n);
     end
     M_.var.(auxiliary_model_name).list_of_variables_in_companion_var = M_.endo_names(M_.var.(auxiliary_model_name).lhs);
+    if nargout
+        A0 = [];
+        B  = [];
+    end
   case 'trend_component'
     % Get number of trends.
     q = sum(M_.trend_component.(auxiliary_model_name).trends);
+
     % Get the number of equations with error correction.
-    m  = n-q;
+    m  = n - q;
+    
     % Get the indices of trend and EC equations in the auxiliary model.
     trend_eqnums_in_auxiliary_model = find(M_.trend_component.(auxiliary_model_name).trends);
     ecm_eqnums_in_auxiliary_model = find(~M_.trend_component.(auxiliary_model_name).trends);
+    
     % Get the indices of trend equations in model.
     trend_eqnums = M_.trend_component.(auxiliary_model_name).trend_eqn;
+    
     % REMARK It is assumed that the non trend equations are the error correction
     %        equations. We assume that the model can be cast in the following form:
     %
@@ -99,68 +107,71 @@ switch auxiliary_model_type
     %
     %        where the dimensions of I and 0 matrices can easily be
     %        deduced from the number of EC and trend equations.
+
     % Check that the lhs of candidate ecm equations are at least first differences.
-    for i=1:m
+    for i = 1:m
         if ~get_difference_order(M_.trend_component.(auxiliary_model_name).lhs(ecm_eqnums_in_auxiliary_model(i)))
-            error('Model %s is not a Trend component  model! LHS variables should be in difference', auxiliary_model_name)
+            error([auxiliary_model_name ' is not a trend component model. The LHS variables should be in differences'])
         end
     end
+    
     % Get the trend variables indices (lhs variables in trend equations).
     [~, id_trend_in_var, ~] = intersect(M_.trend_component.(auxiliary_model_name).eqn, trend_eqnums);
     trend_variables = reshape(M_.trend_component.(auxiliary_model_name).lhs(id_trend_in_var), q, 1);
+    
     % Get the rhs variables in trend equations.
-    trend_autoregressive_variables = zeros(q, 1);
-    for i=1:q
+    for i = 1:q
         % Check that there is only one variable on the rhs and update trend_autoregressive_variables.
         v = M_.trend_component.(auxiliary_model_name).rhs.vars_at_eq{id_trend_in_var(i)}.var;
-        if length(v)~=1
+        if length(v) ~= 1
             error('A trend equation (%s) must have only one variable on the RHS!', M_.trend_component.(auxiliary_model_name).eqtags{trend_eqnums(i)})
         end
-        trend_autoregressive_variables(i) = v;
+        
         % Check that the variables on lhs and rhs have the same difference orders.
-        if get_difference_order(trend_variables(i))~=get_difference_order(trend_autoregressive_variables(i))
+        if get_difference_order(trend_variables(i)) ~= get_difference_order(v)
             error('In a trend equation (%s) LHS and RHS variables must have the same difference orders!', M_.trend_component.(auxiliary_model_name).eqtags{trend_eqnums(i)})
         end
+        
         % Check that the trend equation is autoregressive.
         if isdiff(v)
-            if ~M_.aux_vars(get_aux_variable_id(v)).type==9
+            if ~M_.aux_vars(get_aux_variable_id(v)).type == 9
                 error('In a trend equation (%s) RHS variable must be lagged LHS variable!', M_.trend_component.(auxiliary_model_name).eqtags{trend_eqnums(i)})
             else
-                if M_.aux_vars(get_aux_variable_id(v)).orig_index~=trend_variables(i)
+                if M_.aux_vars(get_aux_variable_id(v)).orig_index ~= trend_variables(i)
                     error('In a trend equation (%s) RHS variable must be lagged LHS variable!', M_.trend_component.(auxiliary_model_name).eqtags{trend_eqnums(i)})
                 end
             end
         else
-            if get_aux_variable_id(v) && M_.aux_vars(get_aux_variable_id(v)).endo_index~=trend_variables(i)
+            if get_aux_variable_id(v) && M_.aux_vars(get_aux_variable_id(v)).endo_index ~= trend_variables(i)
                 error('In a trend equation (%s) RHS variable must be lagged LHS variable!', M_.trend_component.(auxiliary_model_name).eqtags{trend_eqnums(i)})
             end
         end
     end
+    
     % Reorder trend_eqnums_in_auxiliary_model to ensure that the order of
     % the trend variables matches the order of the error correction
     % variables.
     [~,reorder] = ismember(M_.trend_component.toto.lhs(trend_eqnums_in_auxiliary_model), ...
-                           M_.trend_component.toto.trend_vars(find(M_.trend_component.toto.trend_vars>0)));
+                           M_.trend_component.toto.trend_vars(M_.trend_component.toto.trend_vars > 0));
     trend_eqnums_in_auxiliary_model = trend_eqnums_in_auxiliary_model(reorder);
+    
     % Get the EC matrix (the EC term is assumend to be in t-1).
     %
     % TODO: Check that the EC term is the difference between the
     %       endogenous variable and the trend variable.
     %
-    A0 = oo_.trend_component.(auxiliary_model_name).ec(ecm_eqnums_in_auxiliary_model,:,1);
-    % Get the AR matrices.
-    AR = oo_.trend_component.(auxiliary_model_name).ar(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,:);
     % Build B matrices (VAR in levels)
-    B(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,1) = eye(m)+A0+AR(:,:,1);
-    B(ecm_eqnums_in_auxiliary_model,trend_eqnums_in_auxiliary_model) = -A0;
-    B(trend_eqnums_in_auxiliary_model,trend_eqnums_in_auxiliary_model) = eye(q);
-    for i=2:p
-        B(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,i) = AR(:,:,i)-AR(:,:,i-1);
+    B(ecm_eqnums_in_auxiliary_model, ecm_eqnums_in_auxiliary_model, 1) = eye(m) + A0 + AR(:,:,1);
+    B(ecm_eqnums_in_auxiliary_model, trend_eqnums_in_auxiliary_model) = -A0;
+    B(trend_eqnums_in_auxiliary_model, trend_eqnums_in_auxiliary_model) = eye(q);
+    for i = 2:p
+        B(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,i) = AR(:,:,i) - AR(:,:,i-1);
     end
     B(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,p+1) = -AR(:,:,p);
+    
     % Write Companion matrix
     oo_.trend_component.(auxiliary_model_name).CompanionMatrix = zeros(size(B, 1)*size(B, 3));
-    for i=1:p
+    for i = 1:p
         oo_.trend_component.(auxiliary_model_name).CompanionMatrix(1:n, (i-1)*n+(1:n)) = B(:,:,i);
         oo_.trend_component.(auxiliary_model_name).CompanionMatrix(i*n+(1:n),(i-1)*n+(1:n)) = eye(n);
     end
diff --git a/matlab/get_companion_matrix_preprocessor.m b/matlab/get_companion_matrix_legacy.m
similarity index 81%
rename from matlab/get_companion_matrix_preprocessor.m
rename to matlab/get_companion_matrix_legacy.m
index e7835b473..a6a06b198 100644
--- a/matlab/get_companion_matrix_preprocessor.m
+++ b/matlab/get_companion_matrix_legacy.m
@@ -1,6 +1,5 @@
-function [A0, AR, B] = get_companion_matrix_preprocessor(auxiliary_model_name, auxiliary_model_type)
-%function [A0, AR, B] = get_companion_matrix_preprocessor(auxiliary_model_name, auxiliary_model_type)
-%
+function [A0, AR, B] = get_companion_matrix_legacy(auxiliary_model_name, auxiliary_model_type)
+
 % Gets the companion VAR representation of a PAC auxiliary model.
 % Depending on the nature of this auxiliary model the output is
 % saved in oo_.{var,trend_component}.(auxiliary_model_name).CompanionMatrix
@@ -32,7 +31,7 @@ function [A0, AR, B] = get_companion_matrix_preprocessor(auxiliary_model_name, a
 
 global oo_ M_
 
-if nargin < 2
+if nargin<2
     if isfield(M_, 'var') && isfield(M_.var, auxiliary_model_name)
         auxiliary_model_type = 'var';
     elseif isfield(M_, 'trend_component') && isfield(M_.trend_component, auxiliary_model_name)
@@ -42,47 +41,39 @@ if nargin < 2
     end
 end
 
-if strcmp(auxiliary_model_type, 'var')
-    AR = evalin('base', [M_.fname '.var_ar(''' auxiliary_model_name ''', M_.params)']);
-elseif strcmp(auxiliary_model_type, 'trend_component')
-    [AR, A0] = evalin('base', [M_.fname '.trend_component_ar_ec(''' auxiliary_model_name ''', M_.params)']);
-else
-    error('Unknown type of auxiliary model.')
+if nargout
+    A0 = [];
+    AR = [];
+    B = [];
 end
 
+get_ar_ec_matrices(auxiliary_model_name, auxiliary_model_type);
+
 % Get the number of lags
-p = size(AR, 3);
+p = size(oo_.(auxiliary_model_type).(auxiliary_model_name).ar, 3);
 
 % Get the number of variables
-n = length(M_.(auxiliary_model_type).(auxiliary_model_name).lhs);
+n = length(oo_.(auxiliary_model_type).(auxiliary_model_name).ar(:,:,1));
 
 switch auxiliary_model_type
   case 'var'
     oo_.var.(auxiliary_model_name).CompanionMatrix = zeros(n*p);
-    oo_.var.(auxiliary_model_name).CompanionMatrix(1:n,1:n) = AR(:,:,1);
-    for i = 2:p
-        oo_.var.(auxiliary_model_name).CompanionMatrix(1:n,(i-1)*n+(1:n)) = AR(:,:,i);
+    oo_.var.(auxiliary_model_name).CompanionMatrix(1:n,1:n) = oo_.var.(auxiliary_model_name).ar(:,:,1);
+    for i=2:p
+        oo_.var.(auxiliary_model_name).CompanionMatrix(1:n,(i-1)*n+(1:n)) = oo_.var.(auxiliary_model_name).ar(:,:,i);
         oo_.var.(auxiliary_model_name).CompanionMatrix((i-1)*n+(1:n),(i-2)*n+(1:n)) = eye(n);
     end
     M_.var.(auxiliary_model_name).list_of_variables_in_companion_var = M_.endo_names(M_.var.(auxiliary_model_name).lhs);
-    if nargout
-        A0 = [];
-        B  = [];
-    end
   case 'trend_component'
     % Get number of trends.
     q = sum(M_.trend_component.(auxiliary_model_name).trends);
-
     % Get the number of equations with error correction.
-    m  = n - q;
-    
+    m  = n-q;
     % Get the indices of trend and EC equations in the auxiliary model.
     trend_eqnums_in_auxiliary_model = find(M_.trend_component.(auxiliary_model_name).trends);
     ecm_eqnums_in_auxiliary_model = find(~M_.trend_component.(auxiliary_model_name).trends);
-    
     % Get the indices of trend equations in model.
     trend_eqnums = M_.trend_component.(auxiliary_model_name).trend_eqn;
-    
     % REMARK It is assumed that the non trend equations are the error correction
     %        equations. We assume that the model can be cast in the following form:
     %
@@ -108,71 +99,68 @@ switch auxiliary_model_type
     %
     %        where the dimensions of I and 0 matrices can easily be
     %        deduced from the number of EC and trend equations.
-
     % Check that the lhs of candidate ecm equations are at least first differences.
-    for i = 1:m
+    for i=1:m
         if ~get_difference_order(M_.trend_component.(auxiliary_model_name).lhs(ecm_eqnums_in_auxiliary_model(i)))
-            error([auxiliary_model_name ' is not a trend component model. The LHS variables should be in differences'])
+            error('Model %s is not a Trend component  model! LHS variables should be in difference', auxiliary_model_name)
         end
     end
-    
     % Get the trend variables indices (lhs variables in trend equations).
     [~, id_trend_in_var, ~] = intersect(M_.trend_component.(auxiliary_model_name).eqn, trend_eqnums);
     trend_variables = reshape(M_.trend_component.(auxiliary_model_name).lhs(id_trend_in_var), q, 1);
-    
     % Get the rhs variables in trend equations.
-    for i = 1:q
+    trend_autoregressive_variables = zeros(q, 1);
+    for i=1:q
         % Check that there is only one variable on the rhs and update trend_autoregressive_variables.
         v = M_.trend_component.(auxiliary_model_name).rhs.vars_at_eq{id_trend_in_var(i)}.var;
-        if length(v) ~= 1
+        if length(v)~=1
             error('A trend equation (%s) must have only one variable on the RHS!', M_.trend_component.(auxiliary_model_name).eqtags{trend_eqnums(i)})
         end
-        
+        trend_autoregressive_variables(i) = v;
         % Check that the variables on lhs and rhs have the same difference orders.
-        if get_difference_order(trend_variables(i)) ~= get_difference_order(v)
+        if get_difference_order(trend_variables(i))~=get_difference_order(trend_autoregressive_variables(i))
             error('In a trend equation (%s) LHS and RHS variables must have the same difference orders!', M_.trend_component.(auxiliary_model_name).eqtags{trend_eqnums(i)})
         end
-        
         % Check that the trend equation is autoregressive.
         if isdiff(v)
-            if ~M_.aux_vars(get_aux_variable_id(v)).type == 9
+            if ~M_.aux_vars(get_aux_variable_id(v)).type==9
                 error('In a trend equation (%s) RHS variable must be lagged LHS variable!', M_.trend_component.(auxiliary_model_name).eqtags{trend_eqnums(i)})
             else
-                if M_.aux_vars(get_aux_variable_id(v)).orig_index ~= trend_variables(i)
+                if M_.aux_vars(get_aux_variable_id(v)).orig_index~=trend_variables(i)
                     error('In a trend equation (%s) RHS variable must be lagged LHS variable!', M_.trend_component.(auxiliary_model_name).eqtags{trend_eqnums(i)})
                 end
             end
         else
-            if get_aux_variable_id(v) && M_.aux_vars(get_aux_variable_id(v)).endo_index ~= trend_variables(i)
+            if get_aux_variable_id(v) && M_.aux_vars(get_aux_variable_id(v)).endo_index~=trend_variables(i)
                 error('In a trend equation (%s) RHS variable must be lagged LHS variable!', M_.trend_component.(auxiliary_model_name).eqtags{trend_eqnums(i)})
             end
         end
     end
-    
     % Reorder trend_eqnums_in_auxiliary_model to ensure that the order of
     % the trend variables matches the order of the error correction
     % variables.
     [~,reorder] = ismember(M_.trend_component.toto.lhs(trend_eqnums_in_auxiliary_model), ...
-                           M_.trend_component.toto.trend_vars(M_.trend_component.toto.trend_vars > 0));
+                           M_.trend_component.toto.trend_vars(find(M_.trend_component.toto.trend_vars>0)));
     trend_eqnums_in_auxiliary_model = trend_eqnums_in_auxiliary_model(reorder);
-    
     % Get the EC matrix (the EC term is assumend to be in t-1).
     %
     % TODO: Check that the EC term is the difference between the
     %       endogenous variable and the trend variable.
     %
+    A0 = oo_.trend_component.(auxiliary_model_name).ec(ecm_eqnums_in_auxiliary_model,:,1);
+    % Get the AR matrices.
+    AR = oo_.trend_component.(auxiliary_model_name).ar(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,:);
     % Build B matrices (VAR in levels)
-    B(ecm_eqnums_in_auxiliary_model, ecm_eqnums_in_auxiliary_model, 1) = eye(m) + A0 + AR(:,:,1);
-    B(ecm_eqnums_in_auxiliary_model, trend_eqnums_in_auxiliary_model) = -A0;
-    B(trend_eqnums_in_auxiliary_model, trend_eqnums_in_auxiliary_model) = eye(q);
-    for i = 2:p
-        B(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,i) = AR(:,:,i) - AR(:,:,i-1);
+    B(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,1) = eye(m)+A0+AR(:,:,1);
+    B(ecm_eqnums_in_auxiliary_model,trend_eqnums_in_auxiliary_model) = -A0;
+    B(trend_eqnums_in_auxiliary_model,trend_eqnums_in_auxiliary_model) = eye(q);
+    for i=2:p
+        B(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,i) = AR(:,:,i)-AR(:,:,i-1);
     end
     B(ecm_eqnums_in_auxiliary_model,ecm_eqnums_in_auxiliary_model,p+1) = -AR(:,:,p);
-    
     % Write Companion matrix
     oo_.trend_component.(auxiliary_model_name).CompanionMatrix = zeros(size(B, 1)*size(B, 3));
-    for i = 1:p
+    for i=1:p
         oo_.trend_component.(auxiliary_model_name).CompanionMatrix(1:n, (i-1)*n+(1:n)) = B(:,:,i);
         oo_.trend_component.(auxiliary_model_name).CompanionMatrix(i*n+(1:n),(i-1)*n+(1:n)) = eye(n);
     end
diff --git a/tests/Makefile.am b/tests/Makefile.am
index ed28d6349..8c378f995 100644
--- a/tests/Makefile.am
+++ b/tests/Makefile.am
@@ -361,20 +361,21 @@ MODFILES = \
 	trend-component-and-var-models/vm1.mod \
 	trend-component-and-var-models/vm2.mod \
 	trend-component-and-var-models/vm3.mod \
+	trend-component-and-var-models/vm4.mod \
 	trend-component-and-var-models/tcm1.mod \
 	trend-component-and-var-models/tcm2.mod \
 	trend-component-and-var-models/tcm3.mod \
 	trend-component-and-var-models/tcm4.mod \
 	trend-component-and-var-models/tcm5.mod \
 	trend-component-and-var-models/tcm6.mod \
-	trend-component-and-var-models/vm1p.mod \
-	trend-component-and-var-models/vm2p.mod \
-	trend-component-and-var-models/vm3p.mod \
-	trend-component-and-var-models/tcm1p.mod \
-	trend-component-and-var-models/tcm2p.mod \
-	trend-component-and-var-models/tcm3p.mod \
-	trend-component-and-var-models/tcm4p.mod \
-	trend-component-and-var-models/tcm5p.mod \
+	trend-component-and-var-models/legacy/vm1.mod \
+	trend-component-and-var-models/legacy/vm2.mod \
+	trend-component-and-var-models/legacy/vm3.mod \
+	trend-component-and-var-models/legacy/tcm1.mod \
+	trend-component-and-var-models/legacy/tcm2.mod \
+	trend-component-and-var-models/legacy/tcm3.mod \
+	trend-component-and-var-models/legacy/tcm4.mod \
+	trend-component-and-var-models/legacy/tcm5.mod \
 	pac/var-1/example.mod \
 	pac/var-2/example.mod \
 	pac/var-3/example.mod \
diff --git a/tests/trend-component-and-var-models/legacy/tcm1.mod b/tests/trend-component-and-var-models/legacy/tcm1.mod
new file mode 100644
index 000000000..a85366fb3
--- /dev/null
+++ b/tests/trend-component-and-var-models/legacy/tcm1.mod
@@ -0,0 +1,138 @@
+var y1 y2 y3 y4;
+
+varexo e1 e2 e3 e4;
+
+parameters A111 A112 A113 A114
+           A121 A122 A123 A124
+           A131 A132 A133 A134
+           A141 A142 A143 A144
+           A211 A212 A213 A214
+           A221 A222 A223 A224
+           A231 A232 A233 A234
+           A241 A242 A243 A244
+           A311 A312 A313 A314
+           A321 A322 A323 A324
+           A331 A332 A333 A334
+           A341 A342 A343 A344
+           B11 B12 B21 B22;
+
+A1 = randn(4);
+A2 = randn(4);
+A3 = randn(4);
+
+A1(3:4,:) = 0;
+A2(3:4,:) = 0;
+A3(3:4,:) = 0;
+
+A1(1:2,3:4) = 0;
+A2(1:2,3:4) = 0;
+A3(1:2,3:4) = 0;
+
+A1(3:4,3:4) = eye(2); // y3 and y4 are pure random walks.
+
+
+A111 = A1(1,1);
+A112 = A1(1,2);
+A113 = A1(1,3);
+A114 = A1(1,4);
+A121 = A1(2,1);
+A122 = A1(2,2);
+A123 = A1(2,3);
+A124 = A1(2,4);
+A131 = A1(3,1);
+A132 = A1(3,2);
+A133 = A1(3,3);
+A134 = A1(3,4);
+A141 = A1(4,1);
+A142 = A1(4,2);
+A143 = A1(4,3);
+A144 = A1(4,4);
+
+A211 = A2(1,1);
+A212 = A2(1,2);
+A213 = A2(1,3);
+A214 = A2(1,4);
+A221 = A2(2,1);
+A222 = A2(2,2);
+A223 = A2(2,3);
+A224 = A2(2,4);
+A231 = A2(3,1);
+A232 = A2(3,2);
+A233 = A2(3,3);
+A234 = A2(3,4);
+A241 = A2(4,1);
+A242 = A2(4,2);
+A243 = A2(4,3);
+A244 = A2(4,4);
+
+A311 = A3(1,1);
+A312 = A3(1,2);
+A313 = A3(1,3);
+A314 = A3(1,4);
+A321 = A3(2,1);
+A322 = A3(2,2);
+A323 = A3(2,3);
+A324 = A3(2,4);
+A331 = A3(3,1);
+A332 = A3(3,2);
+A333 = A3(3,3);
+A334 = A3(3,4);
+A341 = A3(4,1);
+A342 = A3(4,2);
+A343 = A3(4,3);
+A344 = A3(4,4);
+
+B = rand(2);
+B(1,1) = -B(1,1);
+B(2,2) = -B(2,2);
+
+B11 = B(1,1);
+B12 = B(1,2);
+B21 = B(2,1);
+B22 = B(2,2);
+
+trend_component_model(model_name=toto, eqtags=['eq:y1', 'eq:y2', 'eq:y3', 'eq:y4'], trends=['eq:y3', 'eq:y4']);
+
+model;
+
+[name='eq:y1']
+diff(y1) = B11*(y1(-1)-y3(-1)) + B12*(y2(-1)-y4(-1)) + 
+     A111*diff(y1(-1)) + A112*diff(y2(-1)) +
+     A211*diff(y1(-2)) + A212*diff(y2(-2)) +
+     A311*diff(y1(-3)) + A312*diff(y2(-3)) + e1; 
+
+
+[name='eq:y2']
+diff(y2) = B21*(y1(-1)-y3(-1)) + B22*(y2(-1)-y4(-1)) +
+     A121*diff(y1(-1)) + A122*diff(y2(-1)) +
+     A221*diff(y1(-2)) + A222*diff(y2(-2)) +
+     A321*diff(y1(-3)) + A322*diff(y2(-3)) + e2; 
+
+[name='eq:y3']
+y3 = y3(-1) + e3; 
+
+[name='eq:y4']
+y4 = y4(-1) + e4;
+
+end;
+
+[EC, AR, T] = get_companion_matrix_legacy('toto');
+
+if max(max(abs(EC-B)))>1e-12
+   error('Error component matrix is wrong.')
+end
+
+A1fake = oo_.trend_component.toto.ar(:,:,1);
+A1fake(1:2,3:4) = .0;
+
+if max(max(abs(A1fake-A1)))>1e-12
+   error('First order autoregressive matrix is wrong.')
+end
+
+if max(max(abs(oo_.trend_component.toto.ar(:,:,2)-A2)))>1e-12
+   error('Second order autoregressive matrix is wrong.')
+end
+
+if max(max(abs(oo_.trend_component.toto.ar(:,:,3)-A3)))>1e-12
+   error('Third order autoregressive matrix is wrong.')
+end
\ No newline at end of file
diff --git a/tests/trend-component-and-var-models/legacy/tcm2.mod b/tests/trend-component-and-var-models/legacy/tcm2.mod
new file mode 100644
index 000000000..15c1c2f14
--- /dev/null
+++ b/tests/trend-component-and-var-models/legacy/tcm2.mod
@@ -0,0 +1,135 @@
+var y1 y2 y3 y4;
+
+varexo e1 e2 e3 e4;
+
+parameters A111 A112 A113 A114
+           A121 A122 A123 A124
+           A131 A132 A133 A134
+           A141 A142 A143 A144
+           A211 A212 A213 A214
+           A221 A222 A223 A224
+           A231 A232 A233 A234
+           A241 A242 A243 A244
+           A311 A312 A313 A314
+           A321 A322 A323 A324
+           A331 A332 A333 A334
+           A341 A342 A343 A344
+           B11 B12 B21 B22;
+
+A1 = randn(4);
+A2 = randn(4);
+A3 = randn(4);
+
+A1(3:4,:) = 0;
+A2(3:4,:) = 0;
+A3(3:4,:) = 0;
+
+A1(1:2,3:4) = 0;
+A2(1:2,3:4) = 0;
+A3(1:2,3:4) = 0;
+
+A1(3:4,3:4) = eye(2); // y3 and y4 are pure random walks.
+
+
+A111 = A1(1,1);
+A112 = A1(1,2);
+A113 = A1(1,3);
+A114 = A1(1,4);
+A121 = A1(2,1);
+A122 = A1(2,2);
+A123 = A1(2,3);
+A124 = A1(2,4);
+A131 = A1(3,1);
+A132 = A1(3,2);
+A133 = A1(3,3);
+A134 = A1(3,4);
+A141 = A1(4,1);
+A142 = A1(4,2);
+A143 = A1(4,3);
+A144 = A1(4,4);
+
+A211 = A2(1,1);
+A212 = A2(1,2);
+A213 = A2(1,3);
+A214 = A2(1,4);
+A221 = A2(2,1);
+A222 = A2(2,2);
+A223 = A2(2,3);
+A224 = A2(2,4);
+A231 = A2(3,1);
+A232 = A2(3,2);
+A233 = A2(3,3);
+A234 = A2(3,4);
+A241 = A2(4,1);
+A242 = A2(4,2);
+A243 = A2(4,3);
+A244 = A2(4,4);
+
+A311 = A3(1,1);
+A312 = A3(1,2);
+A313 = A3(1,3);
+A314 = A3(1,4);
+A321 = A3(2,1);
+A322 = A3(2,2);
+A323 = A3(2,3);
+A324 = A3(2,4);
+A331 = A3(3,1);
+A332 = A3(3,2);
+A333 = A3(3,3);
+A334 = A3(3,4);
+A341 = A3(4,1);
+A342 = A3(4,2);
+A343 = A3(4,3);
+A344 = A3(4,4);
+
+B = rand(2);
+B(1,1) = -B(1,1);
+B(2,2) = -B(2,2);
+
+B11 = B(1,1);
+B12 = B(1,2);
+B21 = B(2,1);
+B22 = B(2,2);
+
+trend_component_model(model_name=toto, eqtags=['eq:y1', 'eq:y4', 'eq:y2', 'eq:y3'], trends=['eq:y3', 'eq:y4']);
+
+model;
+
+[name='eq:y1']
+diff(y1) = B11*(y1(-1)-y3(-1)) + B12*(y2(-1)-y4(-1)) + 
+     A111*diff(y1(-1)) + A112*diff(y2(-1)) +
+     A211*diff(y1(-2)) + A212*diff(y2(-2)) +
+     A311*diff(y1(-3)) + A312*diff(y2(-3)) + e1; 
+
+
+[name='eq:y2']
+diff(y2) = B21*(y1(-1)-y3(-1)) + B22*(y2(-1)-y4(-1)) +
+     A121*diff(y1(-1)) + A122*diff(y2(-1)) +
+     A221*diff(y1(-2)) + A222*diff(y2(-2)) +
+     A321*diff(y1(-3)) + A322*diff(y2(-3)) + e2; 
+
+[name='eq:y3']
+y3 = y3(-1) + e3; 
+
+[name='eq:y4']
+y4 = y4(-1) + e4;
+
+end;
+
+[EC, AR, T] = get_companion_matrix_legacy('toto');
+
+if max(max(abs(EC-B)))>1e-12
+   error('Error component matrix is wrong.')
+end
+
+if max(max(abs(AR(:,:,1)-A1(1:2,1:2))))>1e-12
+   error('First order autoregressive matrix is wrong.')
+end
+
+if max(max(abs(AR(:,:,2)-A2(1:2,1:2))))>1e-12
+   error('Second order autoregressive matrix is wrong.')
+end
+
+if max(max(abs(AR(:,:,3)-A3(1:2,1:2))))>1e-12
+   error('Third order autoregressive matrix is wrong.')
+end
\ No newline at end of file
diff --git a/tests/trend-component-and-var-models/legacy/tcm3.mod b/tests/trend-component-and-var-models/legacy/tcm3.mod
new file mode 100644
index 000000000..c81bf65bf
--- /dev/null
+++ b/tests/trend-component-and-var-models/legacy/tcm3.mod
@@ -0,0 +1,135 @@
+var y1 y2 y3 y4;
+
+varexo e1 e2 e3 e4;
+
+parameters A111 A112 A113 A114
+           A121 A122 A123 A124
+           A131 A132 A133 A134
+           A141 A142 A143 A144
+           A211 A212 A213 A214
+           A221 A222 A223 A224
+           A231 A232 A233 A234
+           A241 A242 A243 A244
+           A311 A312 A313 A314
+           A321 A322 A323 A324
+           A331 A332 A333 A334
+           A341 A342 A343 A344
+           B11 B12 B21 B22;
+
+A1 = randn(4);
+A2 = randn(4);
+A3 = randn(4);
+
+A1(3:4,:) = 0;
+A2(3:4,:) = 0;
+A3(3:4,:) = 0;
+
+A1(1:2,3:4) = 0;
+A2(1:2,3:4) = 0;
+A3(1:2,3:4) = 0;
+
+A1(3:4,3:4) = eye(2); // y3 and y4 are pure random walks.
+
+
+A111 = A1(1,1);
+A112 = A1(1,2);
+A113 = A1(1,3);
+A114 = A1(1,4);
+A121 = A1(2,1);
+A122 = A1(2,2);
+A123 = A1(2,3);
+A124 = A1(2,4);
+A131 = A1(3,1);
+A132 = A1(3,2);
+A133 = A1(3,3);
+A134 = A1(3,4);
+A141 = A1(4,1);
+A142 = A1(4,2);
+A143 = A1(4,3);
+A144 = A1(4,4);
+
+A211 = A2(1,1);
+A212 = A2(1,2);
+A213 = A2(1,3);
+A214 = A2(1,4);
+A221 = A2(2,1);
+A222 = A2(2,2);
+A223 = A2(2,3);
+A224 = A2(2,4);
+A231 = A2(3,1);
+A232 = A2(3,2);
+A233 = A2(3,3);
+A234 = A2(3,4);
+A241 = A2(4,1);
+A242 = A2(4,2);
+A243 = A2(4,3);
+A244 = A2(4,4);
+
+A311 = A3(1,1);
+A312 = A3(1,2);
+A313 = A3(1,3);
+A314 = A3(1,4);
+A321 = A3(2,1);
+A322 = A3(2,2);
+A323 = A3(2,3);
+A324 = A3(2,4);
+A331 = A3(3,1);
+A332 = A3(3,2);
+A333 = A3(3,3);
+A334 = A3(3,4);
+A341 = A3(4,1);
+A342 = A3(4,2);
+A343 = A3(4,3);
+A344 = A3(4,4);
+
+B = rand(2);
+B(1,1) = -B(1,1);
+B(2,2) = -B(2,2);
+
+B11 = B(1,1);
+B12 = B(1,2);
+B21 = B(2,1);
+B22 = B(2,2);
+
+trend_component_model(model_name=toto, eqtags=['eq:y1', 'eq:y2', 'eq:y3', 'eq:y4'], trends=['eq:y4', 'eq:y3']);
+
+model;
+
+[name='eq:y1']
+diff(y1) = B11*(y1(-1)-y3(-1)) + B12*(y2(-1)-y4(-1)) + 
+     A111*diff(y1(-1)) + A112*diff(y2(-1)) +
+     A211*diff(y1(-2)) + A212*diff(y2(-2)) +
+     A311*diff(y1(-3)) + A312*diff(y2(-3)) + e1; 
+
+
+[name='eq:y2']
+diff(y2) = B21*(y1(-1)-y3(-1)) + B22*(y2(-1)-y4(-1)) +
+     A121*diff(y1(-1)) + A122*diff(y2(-1)) +
+     A221*diff(y1(-2)) + A222*diff(y2(-2)) +
+     A321*diff(y1(-3)) + A322*diff(y2(-3)) + e2; 
+
+[name='eq:y3']
+y3 = y3(-1) + e3; 
+
+[name='eq:y4']
+y4 = y4(-1) + e4;
+
+end;
+
+[EC, AR, T] = get_companion_matrix_legacy('toto');
+
+if max(max(abs(EC-B)))>1e-12
+   error('Error component matrix is wrong.')
+end
+
+if max(max(abs(AR(:,:,1)-A1(1:2,1:2))))>1e-12
+   error('First order autoregressive matrix is wrong.')
+end
+
+if max(max(abs(AR(:,:,2)-A2(1:2,1:2))))>1e-12
+   error('Second order autoregressive matrix is wrong.')
+end
+
+if max(max(abs(AR(:,:,3)-A3(1:2,1:2))))>1e-12
+   error('Third order autoregressive matrix is wrong.')
+end
\ No newline at end of file
diff --git a/tests/trend-component-and-var-models/legacy/tcm4.mod b/tests/trend-component-and-var-models/legacy/tcm4.mod
new file mode 100644
index 000000000..ba848f0f8
--- /dev/null
+++ b/tests/trend-component-and-var-models/legacy/tcm4.mod
@@ -0,0 +1,136 @@
+var y1 y2 y3 y4;
+
+varexo e1 e2 e3 e4;
+
+parameters A111 A112 A113 A114
+           A121 A122 A123 A124
+           A131 A132 A133 A134
+           A141 A142 A143 A144
+           A211 A212 A213 A214
+           A221 A222 A223 A224
+           A231 A232 A233 A234
+           A241 A242 A243 A244
+           A311 A312 A313 A314
+           A321 A322 A323 A324
+           A331 A332 A333 A334
+           A341 A342 A343 A344
+           B11 B12 B21 B22;
+
+A1 = randn(4);
+A2 = randn(4);
+A3 = randn(4);
+
+A1(3:4,:) = 0;
+A2(3:4,:) = 0;
+A3(3:4,:) = 0;
+
+A1(1:2,3:4) = 0;
+A2(1:2,3:4) = 0;
+A3(1:2,3:4) = 0;
+
+A1(3:4,3:4) = eye(2); // y3 and y4 are pure random walks.
+
+
+A111 = A1(1,1);
+A112 = A1(1,2);
+A113 = A1(1,3);
+A114 = A1(1,4);
+A121 = A1(2,1);
+A122 = A1(2,2);
+A123 = A1(2,3);
+A124 = A1(2,4);
+A131 = A1(3,1);
+A132 = A1(3,2);
+A133 = A1(3,3);
+A134 = A1(3,4);
+A141 = A1(4,1);
+A142 = A1(4,2);
+A143 = A1(4,3);
+A144 = A1(4,4);
+
+A211 = A2(1,1);
+A212 = A2(1,2);
+A213 = A2(1,3);
+A214 = A2(1,4);
+A221 = A2(2,1);
+A222 = A2(2,2);
+A223 = A2(2,3);
+A224 = A2(2,4);
+A231 = A2(3,1);
+A232 = A2(3,2);
+A233 = A2(3,3);
+A234 = A2(3,4);
+A241 = A2(4,1);
+A242 = A2(4,2);
+A243 = A2(4,3);
+A244 = A2(4,4);
+
+A311 = A3(1,1);
+A312 = A3(1,2);
+A313 = A3(1,3);
+A314 = A3(1,4);
+A321 = A3(2,1);
+A322 = A3(2,2);
+A323 = A3(2,3);
+A324 = A3(2,4);
+A331 = A3(3,1);
+A332 = A3(3,2);
+A333 = A3(3,3);
+A334 = A3(3,4);
+A341 = A3(4,1);
+A342 = A3(4,2);
+A343 = A3(4,3);
+A344 = A3(4,4);
+
+B = rand(2);
+B(1,1) = -B(1,1);
+B(2,2) = -B(2,2);
+
+B11 = B(1,1);
+B12 = B(1,2);
+B21 = B(2,1);
+B22 = B(2,2);
+
+trend_component_model(model_name=toto, eqtags=['eq:y1', 'eq:y2', 'eq:y3', 'eq:y4'], trends=['eq:y3', 'eq:y4']);
+
+model;
+
+[name='eq:y1']
+diff(y1) = B11*(y1(-1)-y3(-1)) + B12*(y2(-1)-y4(-1)) + 
+     A111*diff(y1(-1)) + A112*diff(y2(-1)) +
+     A211*diff(y1(-2)) + A212*diff(y2(-2)) +
+     A311*diff(y1(-3)) + A312*diff(y2(-3)) + e1; 
+
+
+[name='eq:y4']
+y4 = y4(-1) + e4;
+
+
+[name='eq:y2']
+diff(y2) = B21*(y1(-1)-y3(-1)) + B22*(y2(-1)-y4(-1)) +
+     A121*diff(y1(-1)) + A122*diff(y2(-1)) +
+     A221*diff(y1(-2)) + A222*diff(y2(-2)) +
+     A321*diff(y1(-3)) + A322*diff(y2(-3)) + e2; 
+
+[name='eq:y3']
+y3 = y3(-1) + e3; 
+
+end;
+
+[EC, AR, T] = get_companion_matrix_legacy('toto');
+
+if max(max(abs(EC-B)))>1e-12
+   error('Error component matrix is wrong.')
+end
+
+if max(max(abs(AR(:,:,1)-A1(1:2,1:2))))>1e-12
+   error('First order autoregressive matrix is wrong.')
+end
+
+if max(max(abs(AR(:,:,2)-A2(1:2,1:2))))>1e-12
+   error('Second order autoregressive matrix is wrong.')
+end
+
+if max(max(abs(AR(:,:,3)-A3(1:2,1:2))))>1e-12
+   error('Third order autoregressive matrix is wrong.')
+end
\ No newline at end of file
diff --git a/tests/trend-component-and-var-models/legacy/tcm5.mod b/tests/trend-component-and-var-models/legacy/tcm5.mod
new file mode 100644
index 000000000..b972f08ab
--- /dev/null
+++ b/tests/trend-component-and-var-models/legacy/tcm5.mod
@@ -0,0 +1,136 @@
+var y1 y2 y3 y4;
+
+varexo e1 e2 e3 e4;
+
+parameters A111 A112 A113 A114
+           A121 A122 A123 A124
+           A131 A132 A133 A134
+           A141 A142 A143 A144
+           A211 A212 A213 A214
+           A221 A222 A223 A224
+           A231 A232 A233 A234
+           A241 A242 A243 A244
+           A311 A312 A313 A314
+           A321 A322 A323 A324
+           A331 A332 A333 A334
+           A341 A342 A343 A344
+           B11 B12 B21 B22;
+
+A1 = randn(4);
+A2 = randn(4);
+A3 = randn(4);
+
+A1(3:4,:) = 0;
+A2(3:4,:) = 0;
+A3(3:4,:) = 0;
+
+A1(1:2,3:4) = 0;
+A2(1:2,3:4) = 0;
+A3(1:2,3:4) = 0;
+
+A1(3:4,3:4) = eye(2); // y3 and y4 are pure random walks.
+
+
+A111 = A1(1,1);
+A112 = A1(1,2);
+A113 = A1(1,3);
+A114 = A1(1,4);
+A121 = A1(2,1);
+A122 = A1(2,2);
+A123 = A1(2,3);
+A124 = A1(2,4);
+A131 = A1(3,1);
+A132 = A1(3,2);
+A133 = A1(3,3);
+A134 = A1(3,4);
+A141 = A1(4,1);
+A142 = A1(4,2);
+A143 = A1(4,3);
+A144 = A1(4,4);
+
+A211 = A2(1,1);
+A212 = A2(1,2);
+A213 = A2(1,3);
+A214 = A2(1,4);
+A221 = A2(2,1);
+A222 = A2(2,2);
+A223 = A2(2,3);
+A224 = A2(2,4);
+A231 = A2(3,1);
+A232 = A2(3,2);
+A233 = A2(3,3);
+A234 = A2(3,4);
+A241 = A2(4,1);
+A242 = A2(4,2);
+A243 = A2(4,3);
+A244 = A2(4,4);
+
+A311 = A3(1,1);
+A312 = A3(1,2);
+A313 = A3(1,3);
+A314 = A3(1,4);
+A321 = A3(2,1);
+A322 = A3(2,2);
+A323 = A3(2,3);
+A324 = A3(2,4);
+A331 = A3(3,1);
+A332 = A3(3,2);
+A333 = A3(3,3);
+A334 = A3(3,4);
+A341 = A3(4,1);
+A342 = A3(4,2);
+A343 = A3(4,3);
+A344 = A3(4,4);
+
+B = rand(2);
+B(1,1) = -B(1,1);
+B(2,2) = -B(2,2);
+
+B11 = B(1,1);
+B12 = B(1,2);
+B21 = B(2,1);
+B22 = B(2,2);
+
+trend_component_model(model_name=toto, eqtags=['eq:y4', 'eq:y1', 'eq:y2', 'eq:y3'], trends=['eq:y3', 'eq:y4']);
+
+model;
+
+[name='eq:y1']
+diff(y1) = B11*(y1(-1)-y3(-1)) + B12*(y2(-1)-y4(-1)) + 
+     A111*diff(y1(-1)) + A112*diff(y2(-1)) +
+     A211*diff(y1(-2)) + A212*diff(y2(-2)) +
+     A311*diff(y1(-3)) + A312*diff(y2(-3)) + e1; 
+
+
+[name='eq:y4']
+y4 = y4(-1) + e4;
+
+
+[name='eq:y2']
+diff(y2) = B21*(y1(-1)-y3(-1)) + B22*(y2(-1)-y4(-1)) +
+     A121*diff(y1(-1)) + A122*diff(y2(-1)) +
+     A221*diff(y1(-2)) + A222*diff(y2(-2)) +
+     A321*diff(y1(-3)) + A322*diff(y2(-3)) + e2; 
+
+[name='eq:y3']
+y3 = y3(-1) + e3; 
+
+end;
+
+[EC, AR, T] = get_companion_matrix_legacy('toto');
+
+if max(max(abs(EC-B)))>1e-12
+   error('Error component matrix is wrong.')
+end
+
+if max(max(abs(AR(:,:,1)-A1(1:2,1:2))))>1e-12
+   error('First order autoregressive matrix is wrong.')
+end
+
+if max(max(abs(AR(:,:,2)-A2(1:2,1:2))))>1e-12
+   error('Second order autoregressive matrix is wrong.')
+end
+
+if max(max(abs(AR(:,:,3)-A3(1:2,1:2))))>1e-12
+   error('Third order autoregressive matrix is wrong.')
+end
\ No newline at end of file
diff --git a/tests/trend-component-and-var-models/vm1p.mod b/tests/trend-component-and-var-models/legacy/vm1.mod
similarity index 93%
rename from tests/trend-component-and-var-models/vm1p.mod
rename to tests/trend-component-and-var-models/legacy/vm1.mod
index d78641753..1869e3a05 100644
--- a/tests/trend-component-and-var-models/vm1p.mod
+++ b/tests/trend-component-and-var-models/legacy/vm1.mod
@@ -96,17 +96,17 @@ y4 = A141*y1(-1) + A142*y2(-1) + A143*y3(-1) + A144*y4(-1) +
 
 end;
 
-[A0, AR, B] = get_companion_matrix_preprocessor('toto');
+get_companion_matrix_legacy('toto');
 
-if max(max(abs(AR(:,:,1)-A1)))>1e-12
+if max(max(abs(oo_.var.toto.ar(:,:,1)-A1)))>1e-12
    error('First order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,2)-A2)))>1e-12
+if max(max(abs(oo_.var.toto.ar(:,:,2)-A2)))>1e-12
    error('Second order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,3)-A3)))>1e-12
+if max(max(abs(oo_.var.toto.ar(:,:,3)-A3)))>1e-12
    error('Third order autoregressive matrix is wrong.')
 end
 
@@ -116,4 +116,4 @@ CompanionMatrix = [A1, A2, A3;
 
 if max(max(abs(CompanionMatrix-oo_.var.toto.CompanionMatrix)))>1e-12
    error('Companion matrix is wrong.')
-end
+end
\ No newline at end of file
diff --git a/tests/trend-component-and-var-models/vm2p.mod b/tests/trend-component-and-var-models/legacy/vm2.mod
similarity index 93%
rename from tests/trend-component-and-var-models/vm2p.mod
rename to tests/trend-component-and-var-models/legacy/vm2.mod
index 6fe6d18ae..a241b9290 100644
--- a/tests/trend-component-and-var-models/vm2p.mod
+++ b/tests/trend-component-and-var-models/legacy/vm2.mod
@@ -96,17 +96,17 @@ y4 = A141*y1(-1) + A142*y2(-1) + A143*y3(-1) + A144*y4(-1) +
 
 end;
 
-[A0, AR, B] = get_companion_matrix_preprocessor('toto');
+get_companion_matrix_legacy('toto');
 
-if max(max(abs(AR(:,:,1)-A1)))>1e-12
+if max(max(abs(oo_.var.toto.ar(:,:,1)-A1)))>1e-12
    error('First order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,2)-A2)))>1e-12
+if max(max(abs(oo_.var.toto.ar(:,:,2)-A2)))>1e-12
    error('Second order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,3)-A3)))>1e-12
+if max(max(abs(oo_.var.toto.ar(:,:,3)-A3)))>1e-12
    error('Third order autoregressive matrix is wrong.')
 end
 
@@ -116,4 +116,4 @@ CompanionMatrix = [A1, A2, A3;
 
 if max(max(abs(CompanionMatrix-oo_.var.toto.CompanionMatrix)))>1e-12
    error('Companion matrix is wrong.')
-end
+end
\ No newline at end of file
diff --git a/tests/trend-component-and-var-models/vm3p.mod b/tests/trend-component-and-var-models/legacy/vm3.mod
similarity index 93%
rename from tests/trend-component-and-var-models/vm3p.mod
rename to tests/trend-component-and-var-models/legacy/vm3.mod
index 53667744c..0585f78a9 100644
--- a/tests/trend-component-and-var-models/vm3p.mod
+++ b/tests/trend-component-and-var-models/legacy/vm3.mod
@@ -96,17 +96,17 @@ y1 = A111*y1(-1) + A112*y2(-1) + A113*y3(-1) + A114*y4(-1) +
 
 end;
 
-[A0, AR, B] = get_companion_matrix_preprocessor('toto');
+get_companion_matrix_legacy('toto');
 
-if max(max(abs(AR(:,:,1)-A1)))>1e-12
+if max(max(abs(oo_.var.toto.ar(:,:,1)-A1)))>1e-12
    error('First order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,2)-A2)))>1e-12
+if max(max(abs(oo_.var.toto.ar(:,:,2)-A2)))>1e-12
    error('Second order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,3)-A3)))>1e-12
+if max(max(abs(oo_.var.toto.ar(:,:,3)-A3)))>1e-12
    error('Third order autoregressive matrix is wrong.')
 end
 
@@ -116,4 +116,4 @@ CompanionMatrix = [A1, A2, A3;
 
 if max(max(abs(CompanionMatrix-oo_.var.toto.CompanionMatrix)))>1e-12
    error('Companion matrix is wrong.')
-end
+end
\ No newline at end of file
diff --git a/tests/trend-component-and-var-models/tcm1.mod b/tests/trend-component-and-var-models/tcm1.mod
index 3805207e9..dfec6aff4 100644
--- a/tests/trend-component-and-var-models/tcm1.mod
+++ b/tests/trend-component-and-var-models/tcm1.mod
@@ -2,85 +2,32 @@ var y1 y2 y3 y4;
 
 varexo e1 e2 e3 e4;
 
-parameters A111 A112 A113 A114
-           A121 A122 A123 A124
-           A131 A132 A133 A134
-           A141 A142 A143 A144
-           A211 A212 A213 A214
-           A221 A222 A223 A224
-           A231 A232 A233 A234
-           A241 A242 A243 A244
-           A311 A312 A313 A314
-           A321 A322 A323 A324
-           A331 A332 A333 A334
-           A341 A342 A343 A344
+parameters A111 A112
+           A121 A122
+           A211 A212
+           A221 A222
+           A311 A312
+           A321 A322
            B11 B12 B21 B22;
 
-A1 = randn(4);
-A2 = randn(4);
-A3 = randn(4);
-
-A1(3:4,:) = 0;
-A2(3:4,:) = 0;
-A3(3:4,:) = 0;
-
-A1(1:2,3:4) = 0;
-A2(1:2,3:4) = 0;
-A3(1:2,3:4) = 0;
-
-A1(3:4,3:4) = eye(2); // y3 and y4 are pure random walks.
-
+A1 = randn(2);
+A2 = randn(2);
+A3 = randn(2);
 
 A111 = A1(1,1);
 A112 = A1(1,2);
-A113 = A1(1,3);
-A114 = A1(1,4);
 A121 = A1(2,1);
 A122 = A1(2,2);
-A123 = A1(2,3);
-A124 = A1(2,4);
-A131 = A1(3,1);
-A132 = A1(3,2);
-A133 = A1(3,3);
-A134 = A1(3,4);
-A141 = A1(4,1);
-A142 = A1(4,2);
-A143 = A1(4,3);
-A144 = A1(4,4);
 
 A211 = A2(1,1);
 A212 = A2(1,2);
-A213 = A2(1,3);
-A214 = A2(1,4);
 A221 = A2(2,1);
 A222 = A2(2,2);
-A223 = A2(2,3);
-A224 = A2(2,4);
-A231 = A2(3,1);
-A232 = A2(3,2);
-A233 = A2(3,3);
-A234 = A2(3,4);
-A241 = A2(4,1);
-A242 = A2(4,2);
-A243 = A2(4,3);
-A244 = A2(4,4);
 
 A311 = A3(1,1);
 A312 = A3(1,2);
-A313 = A3(1,3);
-A314 = A3(1,4);
 A321 = A3(2,1);
 A322 = A3(2,2);
-A323 = A3(2,3);
-A324 = A3(2,4);
-A331 = A3(3,1);
-A332 = A3(3,2);
-A333 = A3(3,3);
-A334 = A3(3,4);
-A341 = A3(4,1);
-A342 = A3(4,2);
-A343 = A3(4,3);
-A344 = A3(4,4);
 
 B = rand(2);
 B(1,1) = -B(1,1);
@@ -122,17 +69,16 @@ if max(max(abs(EC-B)))>1e-12
    error('Error component matrix is wrong.')
 end
 
-A1fake = oo_.trend_component.toto.ar(:,:,1);
-A1fake(1:2,3:4) = .0;
+A1fake = AR(:,:,1);
 
 if max(max(abs(A1fake-A1)))>1e-12
    error('First order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(oo_.trend_component.toto.ar(:,:,2)-A2)))>1e-12
+if max(max(abs(AR(:,:,2)-A2)))>1e-12
    error('Second order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(oo_.trend_component.toto.ar(:,:,3)-A3)))>1e-12
+if max(max(abs(AR(:,:,3)-A3)))>1e-12
    error('Third order autoregressive matrix is wrong.')
-end
\ No newline at end of file
+end
diff --git a/tests/trend-component-and-var-models/tcm1p.mod b/tests/trend-component-and-var-models/tcm1p.mod
deleted file mode 100644
index 4ad46cfe5..000000000
--- a/tests/trend-component-and-var-models/tcm1p.mod
+++ /dev/null
@@ -1,84 +0,0 @@
-var y1 y2 y3 y4;
-
-varexo e1 e2 e3 e4;
-
-parameters A111 A112
-           A121 A122
-           A211 A212
-           A221 A222
-           A311 A312
-           A321 A322
-           B11 B12 B21 B22;
-
-A1 = randn(2);
-A2 = randn(2);
-A3 = randn(2);
-
-A111 = A1(1,1);
-A112 = A1(1,2);
-A121 = A1(2,1);
-A122 = A1(2,2);
-
-A211 = A2(1,1);
-A212 = A2(1,2);
-A221 = A2(2,1);
-A222 = A2(2,2);
-
-A311 = A3(1,1);
-A312 = A3(1,2);
-A321 = A3(2,1);
-A322 = A3(2,2);
-
-B = rand(2);
-B(1,1) = -B(1,1);
-B(2,2) = -B(2,2);
-
-B11 = B(1,1);
-B12 = B(1,2);
-B21 = B(2,1);
-B22 = B(2,2);
-
-trend_component_model(model_name=toto, eqtags=['eq:y1', 'eq:y2', 'eq:y3', 'eq:y4'], trends=['eq:y3', 'eq:y4']);
-
-model;
-
-[name='eq:y1']
-diff(y1) = B11*(y1(-1)-y3(-1)) + B12*(y2(-1)-y4(-1)) + 
-     A111*diff(y1(-1)) + A112*diff(y2(-1)) +
-     A211*diff(y1(-2)) + A212*diff(y2(-2)) +
-     A311*diff(y1(-3)) + A312*diff(y2(-3)) + e1; 
-
-
-[name='eq:y2']
-diff(y2) = B21*(y1(-1)-y3(-1)) + B22*(y2(-1)-y4(-1)) +
-     A121*diff(y1(-1)) + A122*diff(y2(-1)) +
-     A221*diff(y1(-2)) + A222*diff(y2(-2)) +
-     A321*diff(y1(-3)) + A322*diff(y2(-3)) + e2; 
-
-[name='eq:y3']
-y3 = y3(-1) + e3; 
-
-[name='eq:y4']
-y4 = y4(-1) + e4;
-
-end;
-
-[EC, AR, T] = get_companion_matrix_preprocessor('toto');
-
-if max(max(abs(EC-B)))>1e-12
-   error('Error component matrix is wrong.')
-end
-
-A1fake = AR(:,:,1);
-
-if max(max(abs(A1fake-A1)))>1e-12
-   error('First order autoregressive matrix is wrong.')
-end
-
-if max(max(abs(AR(:,:,2)-A2)))>1e-12
-   error('Second order autoregressive matrix is wrong.')
-end
-
-if max(max(abs(AR(:,:,3)-A3)))>1e-12
-   error('Third order autoregressive matrix is wrong.')
-end
diff --git a/tests/trend-component-and-var-models/tcm2.mod b/tests/trend-component-and-var-models/tcm2.mod
index c9f12d810..f0d700cd1 100644
--- a/tests/trend-component-and-var-models/tcm2.mod
+++ b/tests/trend-component-and-var-models/tcm2.mod
@@ -2,85 +2,32 @@ var y1 y2 y3 y4;
 
 varexo e1 e2 e3 e4;
 
-parameters A111 A112 A113 A114
-           A121 A122 A123 A124
-           A131 A132 A133 A134
-           A141 A142 A143 A144
-           A211 A212 A213 A214
-           A221 A222 A223 A224
-           A231 A232 A233 A234
-           A241 A242 A243 A244
-           A311 A312 A313 A314
-           A321 A322 A323 A324
-           A331 A332 A333 A334
-           A341 A342 A343 A344
+parameters A111 A112
+           A121 A122
+           A211 A212
+           A221 A222
+           A311 A312
+           A321 A322
            B11 B12 B21 B22;
 
-A1 = randn(4);
-A2 = randn(4);
-A3 = randn(4);
-
-A1(3:4,:) = 0;
-A2(3:4,:) = 0;
-A3(3:4,:) = 0;
-
-A1(1:2,3:4) = 0;
-A2(1:2,3:4) = 0;
-A3(1:2,3:4) = 0;
-
-A1(3:4,3:4) = eye(2); // y3 and y4 are pure random walks.
-
+A1 = randn(2);
+A2 = randn(2);
+A3 = randn(2);
 
 A111 = A1(1,1);
 A112 = A1(1,2);
-A113 = A1(1,3);
-A114 = A1(1,4);
 A121 = A1(2,1);
 A122 = A1(2,2);
-A123 = A1(2,3);
-A124 = A1(2,4);
-A131 = A1(3,1);
-A132 = A1(3,2);
-A133 = A1(3,3);
-A134 = A1(3,4);
-A141 = A1(4,1);
-A142 = A1(4,2);
-A143 = A1(4,3);
-A144 = A1(4,4);
 
 A211 = A2(1,1);
 A212 = A2(1,2);
-A213 = A2(1,3);
-A214 = A2(1,4);
 A221 = A2(2,1);
 A222 = A2(2,2);
-A223 = A2(2,3);
-A224 = A2(2,4);
-A231 = A2(3,1);
-A232 = A2(3,2);
-A233 = A2(3,3);
-A234 = A2(3,4);
-A241 = A2(4,1);
-A242 = A2(4,2);
-A243 = A2(4,3);
-A244 = A2(4,4);
 
 A311 = A3(1,1);
 A312 = A3(1,2);
-A313 = A3(1,3);
-A314 = A3(1,4);
 A321 = A3(2,1);
 A322 = A3(2,2);
-A323 = A3(2,3);
-A324 = A3(2,4);
-A331 = A3(3,1);
-A332 = A3(3,2);
-A333 = A3(3,3);
-A334 = A3(3,4);
-A341 = A3(4,1);
-A342 = A3(4,2);
-A343 = A3(4,3);
-A344 = A3(4,4);
 
 B = rand(2);
 B(1,1) = -B(1,1);
@@ -122,14 +69,16 @@ if max(max(abs(EC-B)))>1e-12
    error('Error component matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,1)-A1(1:2,1:2))))>1e-12
+A1fake = AR(:,:,1);
+
+if max(max(abs(A1fake-A1)))>1e-12
    error('First order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,2)-A2(1:2,1:2))))>1e-12
+if max(max(abs(AR(:,:,2)-A2)))>1e-12
    error('Second order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,3)-A3(1:2,1:2))))>1e-12
+if max(max(abs(AR(:,:,3)-A3)))>1e-12
    error('Third order autoregressive matrix is wrong.')
-end
\ No newline at end of file
+end
diff --git a/tests/trend-component-and-var-models/tcm2p.mod b/tests/trend-component-and-var-models/tcm2p.mod
deleted file mode 100644
index db7b91263..000000000
--- a/tests/trend-component-and-var-models/tcm2p.mod
+++ /dev/null
@@ -1,84 +0,0 @@
-var y1 y2 y3 y4;
-
-varexo e1 e2 e3 e4;
-
-parameters A111 A112
-           A121 A122
-           A211 A212
-           A221 A222
-           A311 A312
-           A321 A322
-           B11 B12 B21 B22;
-
-A1 = randn(2);
-A2 = randn(2);
-A3 = randn(2);
-
-A111 = A1(1,1);
-A112 = A1(1,2);
-A121 = A1(2,1);
-A122 = A1(2,2);
-
-A211 = A2(1,1);
-A212 = A2(1,2);
-A221 = A2(2,1);
-A222 = A2(2,2);
-
-A311 = A3(1,1);
-A312 = A3(1,2);
-A321 = A3(2,1);
-A322 = A3(2,2);
-
-B = rand(2);
-B(1,1) = -B(1,1);
-B(2,2) = -B(2,2);
-
-B11 = B(1,1);
-B12 = B(1,2);
-B21 = B(2,1);
-B22 = B(2,2);
-
-trend_component_model(model_name=toto, eqtags=['eq:y1', 'eq:y4', 'eq:y2', 'eq:y3'], trends=['eq:y3', 'eq:y4']);
-
-model;
-
-[name='eq:y1']
-diff(y1) = B11*(y1(-1)-y3(-1)) + B12*(y2(-1)-y4(-1)) + 
-     A111*diff(y1(-1)) + A112*diff(y2(-1)) +
-     A211*diff(y1(-2)) + A212*diff(y2(-2)) +
-     A311*diff(y1(-3)) + A312*diff(y2(-3)) + e1; 
-
-
-[name='eq:y2']
-diff(y2) = B21*(y1(-1)-y3(-1)) + B22*(y2(-1)-y4(-1)) +
-     A121*diff(y1(-1)) + A122*diff(y2(-1)) +
-     A221*diff(y1(-2)) + A222*diff(y2(-2)) +
-     A321*diff(y1(-3)) + A322*diff(y2(-3)) + e2; 
-
-[name='eq:y3']
-y3 = y3(-1) + e3; 
-
-[name='eq:y4']
-y4 = y4(-1) + e4;
-
-end;
-
-[EC, AR, T] = get_companion_matrix_preprocessor('toto');
-
-if max(max(abs(EC-B)))>1e-12
-   error('Error component matrix is wrong.')
-end
-
-A1fake = AR(:,:,1);
-
-if max(max(abs(A1fake-A1)))>1e-12
-   error('First order autoregressive matrix is wrong.')
-end
-
-if max(max(abs(AR(:,:,2)-A2)))>1e-12
-   error('Second order autoregressive matrix is wrong.')
-end
-
-if max(max(abs(AR(:,:,3)-A3)))>1e-12
-   error('Third order autoregressive matrix is wrong.')
-end
diff --git a/tests/trend-component-and-var-models/tcm3.mod b/tests/trend-component-and-var-models/tcm3.mod
index 05dbd7c39..470aa9390 100644
--- a/tests/trend-component-and-var-models/tcm3.mod
+++ b/tests/trend-component-and-var-models/tcm3.mod
@@ -2,85 +2,32 @@ var y1 y2 y3 y4;
 
 varexo e1 e2 e3 e4;
 
-parameters A111 A112 A113 A114
-           A121 A122 A123 A124
-           A131 A132 A133 A134
-           A141 A142 A143 A144
-           A211 A212 A213 A214
-           A221 A222 A223 A224
-           A231 A232 A233 A234
-           A241 A242 A243 A244
-           A311 A312 A313 A314
-           A321 A322 A323 A324
-           A331 A332 A333 A334
-           A341 A342 A343 A344
+parameters A111 A112
+           A121 A122
+           A211 A212
+           A221 A222
+           A311 A312
+           A321 A322
            B11 B12 B21 B22;
 
-A1 = randn(4);
-A2 = randn(4);
-A3 = randn(4);
-
-A1(3:4,:) = 0;
-A2(3:4,:) = 0;
-A3(3:4,:) = 0;
-
-A1(1:2,3:4) = 0;
-A2(1:2,3:4) = 0;
-A3(1:2,3:4) = 0;
-
-A1(3:4,3:4) = eye(2); // y3 and y4 are pure random walks.
-
+A1 = randn(2);
+A2 = randn(2);
+A3 = randn(2);
 
 A111 = A1(1,1);
 A112 = A1(1,2);
-A113 = A1(1,3);
-A114 = A1(1,4);
 A121 = A1(2,1);
 A122 = A1(2,2);
-A123 = A1(2,3);
-A124 = A1(2,4);
-A131 = A1(3,1);
-A132 = A1(3,2);
-A133 = A1(3,3);
-A134 = A1(3,4);
-A141 = A1(4,1);
-A142 = A1(4,2);
-A143 = A1(4,3);
-A144 = A1(4,4);
 
 A211 = A2(1,1);
 A212 = A2(1,2);
-A213 = A2(1,3);
-A214 = A2(1,4);
 A221 = A2(2,1);
 A222 = A2(2,2);
-A223 = A2(2,3);
-A224 = A2(2,4);
-A231 = A2(3,1);
-A232 = A2(3,2);
-A233 = A2(3,3);
-A234 = A2(3,4);
-A241 = A2(4,1);
-A242 = A2(4,2);
-A243 = A2(4,3);
-A244 = A2(4,4);
 
 A311 = A3(1,1);
 A312 = A3(1,2);
-A313 = A3(1,3);
-A314 = A3(1,4);
 A321 = A3(2,1);
 A322 = A3(2,2);
-A323 = A3(2,3);
-A324 = A3(2,4);
-A331 = A3(3,1);
-A332 = A3(3,2);
-A333 = A3(3,3);
-A334 = A3(3,4);
-A341 = A3(4,1);
-A342 = A3(4,2);
-A343 = A3(4,3);
-A344 = A3(4,4);
 
 B = rand(2);
 B(1,1) = -B(1,1);
@@ -122,14 +69,16 @@ if max(max(abs(EC-B)))>1e-12
    error('Error component matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,1)-A1(1:2,1:2))))>1e-12
+A1fake = AR(:,:,1);
+
+if max(max(abs(A1fake-A1)))>1e-12
    error('First order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,2)-A2(1:2,1:2))))>1e-12
+if max(max(abs(AR(:,:,2)-A2)))>1e-12
    error('Second order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,3)-A3(1:2,1:2))))>1e-12
+if max(max(abs(AR(:,:,3)-A3)))>1e-12
    error('Third order autoregressive matrix is wrong.')
-end
\ No newline at end of file
+end
diff --git a/tests/trend-component-and-var-models/tcm3p.mod b/tests/trend-component-and-var-models/tcm3p.mod
deleted file mode 100644
index 8376a48ae..000000000
--- a/tests/trend-component-and-var-models/tcm3p.mod
+++ /dev/null
@@ -1,84 +0,0 @@
-var y1 y2 y3 y4;
-
-varexo e1 e2 e3 e4;
-
-parameters A111 A112
-           A121 A122
-           A211 A212
-           A221 A222
-           A311 A312
-           A321 A322
-           B11 B12 B21 B22;
-
-A1 = randn(2);
-A2 = randn(2);
-A3 = randn(2);
-
-A111 = A1(1,1);
-A112 = A1(1,2);
-A121 = A1(2,1);
-A122 = A1(2,2);
-
-A211 = A2(1,1);
-A212 = A2(1,2);
-A221 = A2(2,1);
-A222 = A2(2,2);
-
-A311 = A3(1,1);
-A312 = A3(1,2);
-A321 = A3(2,1);
-A322 = A3(2,2);
-
-B = rand(2);
-B(1,1) = -B(1,1);
-B(2,2) = -B(2,2);
-
-B11 = B(1,1);
-B12 = B(1,2);
-B21 = B(2,1);
-B22 = B(2,2);
-
-trend_component_model(model_name=toto, eqtags=['eq:y1', 'eq:y2', 'eq:y3', 'eq:y4'], trends=['eq:y4', 'eq:y3']);
-
-model;
-
-[name='eq:y1']
-diff(y1) = B11*(y1(-1)-y3(-1)) + B12*(y2(-1)-y4(-1)) + 
-     A111*diff(y1(-1)) + A112*diff(y2(-1)) +
-     A211*diff(y1(-2)) + A212*diff(y2(-2)) +
-     A311*diff(y1(-3)) + A312*diff(y2(-3)) + e1; 
-
-
-[name='eq:y2']
-diff(y2) = B21*(y1(-1)-y3(-1)) + B22*(y2(-1)-y4(-1)) +
-     A121*diff(y1(-1)) + A122*diff(y2(-1)) +
-     A221*diff(y1(-2)) + A222*diff(y2(-2)) +
-     A321*diff(y1(-3)) + A322*diff(y2(-3)) + e2; 
-
-[name='eq:y3']
-y3 = y3(-1) + e3; 
-
-[name='eq:y4']
-y4 = y4(-1) + e4;
-
-end;
-
-[EC, AR, T] = get_companion_matrix_preprocessor('toto');
-
-if max(max(abs(EC-B)))>1e-12
-   error('Error component matrix is wrong.')
-end
-
-A1fake = AR(:,:,1);
-
-if max(max(abs(A1fake-A1)))>1e-12
-   error('First order autoregressive matrix is wrong.')
-end
-
-if max(max(abs(AR(:,:,2)-A2)))>1e-12
-   error('Second order autoregressive matrix is wrong.')
-end
-
-if max(max(abs(AR(:,:,3)-A3)))>1e-12
-   error('Third order autoregressive matrix is wrong.')
-end
diff --git a/tests/trend-component-and-var-models/tcm4.mod b/tests/trend-component-and-var-models/tcm4.mod
index 691c1f5fe..2d99b8bcf 100644
--- a/tests/trend-component-and-var-models/tcm4.mod
+++ b/tests/trend-component-and-var-models/tcm4.mod
@@ -2,85 +2,32 @@ var y1 y2 y3 y4;
 
 varexo e1 e2 e3 e4;
 
-parameters A111 A112 A113 A114
-           A121 A122 A123 A124
-           A131 A132 A133 A134
-           A141 A142 A143 A144
-           A211 A212 A213 A214
-           A221 A222 A223 A224
-           A231 A232 A233 A234
-           A241 A242 A243 A244
-           A311 A312 A313 A314
-           A321 A322 A323 A324
-           A331 A332 A333 A334
-           A341 A342 A343 A344
+parameters A111 A112
+           A121 A122
+           A211 A212
+           A221 A222
+           A311 A312
+           A321 A322
            B11 B12 B21 B22;
 
-A1 = randn(4);
-A2 = randn(4);
-A3 = randn(4);
-
-A1(3:4,:) = 0;
-A2(3:4,:) = 0;
-A3(3:4,:) = 0;
-
-A1(1:2,3:4) = 0;
-A2(1:2,3:4) = 0;
-A3(1:2,3:4) = 0;
-
-A1(3:4,3:4) = eye(2); // y3 and y4 are pure random walks.
-
+A1 = randn(2);
+A2 = randn(2);
+A3 = randn(2);
 
 A111 = A1(1,1);
 A112 = A1(1,2);
-A113 = A1(1,3);
-A114 = A1(1,4);
 A121 = A1(2,1);
 A122 = A1(2,2);
-A123 = A1(2,3);
-A124 = A1(2,4);
-A131 = A1(3,1);
-A132 = A1(3,2);
-A133 = A1(3,3);
-A134 = A1(3,4);
-A141 = A1(4,1);
-A142 = A1(4,2);
-A143 = A1(4,3);
-A144 = A1(4,4);
 
 A211 = A2(1,1);
 A212 = A2(1,2);
-A213 = A2(1,3);
-A214 = A2(1,4);
 A221 = A2(2,1);
 A222 = A2(2,2);
-A223 = A2(2,3);
-A224 = A2(2,4);
-A231 = A2(3,1);
-A232 = A2(3,2);
-A233 = A2(3,3);
-A234 = A2(3,4);
-A241 = A2(4,1);
-A242 = A2(4,2);
-A243 = A2(4,3);
-A244 = A2(4,4);
 
 A311 = A3(1,1);
 A312 = A3(1,2);
-A313 = A3(1,3);
-A314 = A3(1,4);
 A321 = A3(2,1);
 A322 = A3(2,2);
-A323 = A3(2,3);
-A324 = A3(2,4);
-A331 = A3(3,1);
-A332 = A3(3,2);
-A333 = A3(3,3);
-A334 = A3(3,4);
-A341 = A3(4,1);
-A342 = A3(4,2);
-A343 = A3(4,3);
-A344 = A3(4,4);
 
 B = rand(2);
 B(1,1) = -B(1,1);
@@ -123,14 +70,16 @@ if max(max(abs(EC-B)))>1e-12
    error('Error component matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,1)-A1(1:2,1:2))))>1e-12
+A1fake = AR(:,:,1);
+
+if max(max(abs(A1fake-A1)))>1e-12
    error('First order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,2)-A2(1:2,1:2))))>1e-12
+if max(max(abs(AR(:,:,2)-A2)))>1e-12
    error('Second order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,3)-A3(1:2,1:2))))>1e-12
+if max(max(abs(AR(:,:,3)-A3)))>1e-12
    error('Third order autoregressive matrix is wrong.')
-end
\ No newline at end of file
+end
diff --git a/tests/trend-component-and-var-models/tcm4p.mod b/tests/trend-component-and-var-models/tcm4p.mod
deleted file mode 100644
index ea911c275..000000000
--- a/tests/trend-component-and-var-models/tcm4p.mod
+++ /dev/null
@@ -1,85 +0,0 @@
-var y1 y2 y3 y4;
-
-varexo e1 e2 e3 e4;
-
-parameters A111 A112
-           A121 A122
-           A211 A212
-           A221 A222
-           A311 A312
-           A321 A322
-           B11 B12 B21 B22;
-
-A1 = randn(2);
-A2 = randn(2);
-A3 = randn(2);
-
-A111 = A1(1,1);
-A112 = A1(1,2);
-A121 = A1(2,1);
-A122 = A1(2,2);
-
-A211 = A2(1,1);
-A212 = A2(1,2);
-A221 = A2(2,1);
-A222 = A2(2,2);
-
-A311 = A3(1,1);
-A312 = A3(1,2);
-A321 = A3(2,1);
-A322 = A3(2,2);
-
-B = rand(2);
-B(1,1) = -B(1,1);
-B(2,2) = -B(2,2);
-
-B11 = B(1,1);
-B12 = B(1,2);
-B21 = B(2,1);
-B22 = B(2,2);
-
-trend_component_model(model_name=toto, eqtags=['eq:y1', 'eq:y2', 'eq:y3', 'eq:y4'], trends=['eq:y3', 'eq:y4']);
-
-model;
-
-[name='eq:y1']
-diff(y1) = B11*(y1(-1)-y3(-1)) + B12*(y2(-1)-y4(-1)) + 
-     A111*diff(y1(-1)) + A112*diff(y2(-1)) +
-     A211*diff(y1(-2)) + A212*diff(y2(-2)) +
-     A311*diff(y1(-3)) + A312*diff(y2(-3)) + e1; 
-
-
-[name='eq:y4']
-y4 = y4(-1) + e4;
-
-
-[name='eq:y2']
-diff(y2) = B21*(y1(-1)-y3(-1)) + B22*(y2(-1)-y4(-1)) +
-     A121*diff(y1(-1)) + A122*diff(y2(-1)) +
-     A221*diff(y1(-2)) + A222*diff(y2(-2)) +
-     A321*diff(y1(-3)) + A322*diff(y2(-3)) + e2; 
-
-[name='eq:y3']
-y3 = y3(-1) + e3; 
-
-end;
-
-[EC, AR, T] = get_companion_matrix_preprocessor('toto');
-
-if max(max(abs(EC-B)))>1e-12
-   error('Error component matrix is wrong.')
-end
-
-A1fake = AR(:,:,1);
-
-if max(max(abs(A1fake-A1)))>1e-12
-   error('First order autoregressive matrix is wrong.')
-end
-
-if max(max(abs(AR(:,:,2)-A2)))>1e-12
-   error('Second order autoregressive matrix is wrong.')
-end
-
-if max(max(abs(AR(:,:,3)-A3)))>1e-12
-   error('Third order autoregressive matrix is wrong.')
-end
diff --git a/tests/trend-component-and-var-models/tcm5.mod b/tests/trend-component-and-var-models/tcm5.mod
index 9dc1ebe5d..810267da5 100644
--- a/tests/trend-component-and-var-models/tcm5.mod
+++ b/tests/trend-component-and-var-models/tcm5.mod
@@ -2,85 +2,32 @@ var y1 y2 y3 y4;
 
 varexo e1 e2 e3 e4;
 
-parameters A111 A112 A113 A114
-           A121 A122 A123 A124
-           A131 A132 A133 A134
-           A141 A142 A143 A144
-           A211 A212 A213 A214
-           A221 A222 A223 A224
-           A231 A232 A233 A234
-           A241 A242 A243 A244
-           A311 A312 A313 A314
-           A321 A322 A323 A324
-           A331 A332 A333 A334
-           A341 A342 A343 A344
+parameters A111 A112
+           A121 A122
+           A211 A212
+           A221 A222
+           A311 A312
+           A321 A322
            B11 B12 B21 B22;
 
-A1 = randn(4);
-A2 = randn(4);
-A3 = randn(4);
-
-A1(3:4,:) = 0;
-A2(3:4,:) = 0;
-A3(3:4,:) = 0;
-
-A1(1:2,3:4) = 0;
-A2(1:2,3:4) = 0;
-A3(1:2,3:4) = 0;
-
-A1(3:4,3:4) = eye(2); // y3 and y4 are pure random walks.
-
+A1 = randn(2);
+A2 = randn(2);
+A3 = randn(2);
 
 A111 = A1(1,1);
 A112 = A1(1,2);
-A113 = A1(1,3);
-A114 = A1(1,4);
 A121 = A1(2,1);
 A122 = A1(2,2);
-A123 = A1(2,3);
-A124 = A1(2,4);
-A131 = A1(3,1);
-A132 = A1(3,2);
-A133 = A1(3,3);
-A134 = A1(3,4);
-A141 = A1(4,1);
-A142 = A1(4,2);
-A143 = A1(4,3);
-A144 = A1(4,4);
 
 A211 = A2(1,1);
 A212 = A2(1,2);
-A213 = A2(1,3);
-A214 = A2(1,4);
 A221 = A2(2,1);
 A222 = A2(2,2);
-A223 = A2(2,3);
-A224 = A2(2,4);
-A231 = A2(3,1);
-A232 = A2(3,2);
-A233 = A2(3,3);
-A234 = A2(3,4);
-A241 = A2(4,1);
-A242 = A2(4,2);
-A243 = A2(4,3);
-A244 = A2(4,4);
 
 A311 = A3(1,1);
 A312 = A3(1,2);
-A313 = A3(1,3);
-A314 = A3(1,4);
 A321 = A3(2,1);
 A322 = A3(2,2);
-A323 = A3(2,3);
-A324 = A3(2,4);
-A331 = A3(3,1);
-A332 = A3(3,2);
-A333 = A3(3,3);
-A334 = A3(3,4);
-A341 = A3(4,1);
-A342 = A3(4,2);
-A343 = A3(4,3);
-A344 = A3(4,4);
 
 B = rand(2);
 B(1,1) = -B(1,1);
@@ -123,14 +70,16 @@ if max(max(abs(EC-B)))>1e-12
    error('Error component matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,1)-A1(1:2,1:2))))>1e-12
+A1fake = AR(:,:,1);
+
+if max(max(abs(A1fake-A1)))>1e-12
    error('First order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,2)-A2(1:2,1:2))))>1e-12
+if max(max(abs(AR(:,:,2)-A2)))>1e-12
    error('Second order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(AR(:,:,3)-A3(1:2,1:2))))>1e-12
+if max(max(abs(AR(:,:,3)-A3)))>1e-12
    error('Third order autoregressive matrix is wrong.')
-end
\ No newline at end of file
+end
diff --git a/tests/trend-component-and-var-models/tcm5p.mod b/tests/trend-component-and-var-models/tcm5p.mod
deleted file mode 100644
index 80e6ce7a4..000000000
--- a/tests/trend-component-and-var-models/tcm5p.mod
+++ /dev/null
@@ -1,85 +0,0 @@
-var y1 y2 y3 y4;
-
-varexo e1 e2 e3 e4;
-
-parameters A111 A112
-           A121 A122
-           A211 A212
-           A221 A222
-           A311 A312
-           A321 A322
-           B11 B12 B21 B22;
-
-A1 = randn(2);
-A2 = randn(2);
-A3 = randn(2);
-
-A111 = A1(1,1);
-A112 = A1(1,2);
-A121 = A1(2,1);
-A122 = A1(2,2);
-
-A211 = A2(1,1);
-A212 = A2(1,2);
-A221 = A2(2,1);
-A222 = A2(2,2);
-
-A311 = A3(1,1);
-A312 = A3(1,2);
-A321 = A3(2,1);
-A322 = A3(2,2);
-
-B = rand(2);
-B(1,1) = -B(1,1);
-B(2,2) = -B(2,2);
-
-B11 = B(1,1);
-B12 = B(1,2);
-B21 = B(2,1);
-B22 = B(2,2);
-
-trend_component_model(model_name=toto, eqtags=['eq:y4', 'eq:y1', 'eq:y2', 'eq:y3'], trends=['eq:y3', 'eq:y4']);
-
-model;
-
-[name='eq:y1']
-diff(y1) = B11*(y1(-1)-y3(-1)) + B12*(y2(-1)-y4(-1)) + 
-     A111*diff(y1(-1)) + A112*diff(y2(-1)) +
-     A211*diff(y1(-2)) + A212*diff(y2(-2)) +
-     A311*diff(y1(-3)) + A312*diff(y2(-3)) + e1; 
-
-
-[name='eq:y4']
-y4 = y4(-1) + e4;
-
-
-[name='eq:y2']
-diff(y2) = B21*(y1(-1)-y3(-1)) + B22*(y2(-1)-y4(-1)) +
-     A121*diff(y1(-1)) + A122*diff(y2(-1)) +
-     A221*diff(y1(-2)) + A222*diff(y2(-2)) +
-     A321*diff(y1(-3)) + A322*diff(y2(-3)) + e2; 
-
-[name='eq:y3']
-y3 = y3(-1) + e3; 
-
-end;
-
-[EC, AR, T] = get_companion_matrix_preprocessor('toto');
-
-if max(max(abs(EC-B)))>1e-12
-   error('Error component matrix is wrong.')
-end
-
-A1fake = AR(:,:,1);
-
-if max(max(abs(A1fake-A1)))>1e-12
-   error('First order autoregressive matrix is wrong.')
-end
-
-if max(max(abs(AR(:,:,2)-A2)))>1e-12
-   error('Second order autoregressive matrix is wrong.')
-end
-
-if max(max(abs(AR(:,:,3)-A3)))>1e-12
-   error('Third order autoregressive matrix is wrong.')
-end
diff --git a/tests/trend-component-and-var-models/vm1.mod b/tests/trend-component-and-var-models/vm1.mod
index 032c90f2e..900484ed3 100644
--- a/tests/trend-component-and-var-models/vm1.mod
+++ b/tests/trend-component-and-var-models/vm1.mod
@@ -96,17 +96,17 @@ y4 = A141*y1(-1) + A142*y2(-1) + A143*y3(-1) + A144*y4(-1) +
 
 end;
 
-get_companion_matrix('toto');
+[A0, AR, B] = get_companion_matrix('toto');
 
-if max(max(abs(oo_.var.toto.ar(:,:,1)-A1)))>1e-12
+if max(max(abs(AR(:,:,1)-A1)))>1e-12
    error('First order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(oo_.var.toto.ar(:,:,2)-A2)))>1e-12
+if max(max(abs(AR(:,:,2)-A2)))>1e-12
    error('Second order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(oo_.var.toto.ar(:,:,3)-A3)))>1e-12
+if max(max(abs(AR(:,:,3)-A3)))>1e-12
    error('Third order autoregressive matrix is wrong.')
 end
 
@@ -116,4 +116,4 @@ CompanionMatrix = [A1, A2, A3;
 
 if max(max(abs(CompanionMatrix-oo_.var.toto.CompanionMatrix)))>1e-12
    error('Companion matrix is wrong.')
-end
\ No newline at end of file
+end
diff --git a/tests/trend-component-and-var-models/vm2.mod b/tests/trend-component-and-var-models/vm2.mod
index b76990747..41d07f819 100644
--- a/tests/trend-component-and-var-models/vm2.mod
+++ b/tests/trend-component-and-var-models/vm2.mod
@@ -96,17 +96,17 @@ y4 = A141*y1(-1) + A142*y2(-1) + A143*y3(-1) + A144*y4(-1) +
 
 end;
 
-get_companion_matrix('toto');
+[A0, AR, B] = get_companion_matrix('toto');
 
-if max(max(abs(oo_.var.toto.ar(:,:,1)-A1)))>1e-12
+if max(max(abs(AR(:,:,1)-A1)))>1e-12
    error('First order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(oo_.var.toto.ar(:,:,2)-A2)))>1e-12
+if max(max(abs(AR(:,:,2)-A2)))>1e-12
    error('Second order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(oo_.var.toto.ar(:,:,3)-A3)))>1e-12
+if max(max(abs(AR(:,:,3)-A3)))>1e-12
    error('Third order autoregressive matrix is wrong.')
 end
 
@@ -116,4 +116,4 @@ CompanionMatrix = [A1, A2, A3;
 
 if max(max(abs(CompanionMatrix-oo_.var.toto.CompanionMatrix)))>1e-12
    error('Companion matrix is wrong.')
-end
\ No newline at end of file
+end
diff --git a/tests/trend-component-and-var-models/vm3.mod b/tests/trend-component-and-var-models/vm3.mod
index 454dba96d..d73a25ce3 100644
--- a/tests/trend-component-and-var-models/vm3.mod
+++ b/tests/trend-component-and-var-models/vm3.mod
@@ -96,17 +96,17 @@ y1 = A111*y1(-1) + A112*y2(-1) + A113*y3(-1) + A114*y4(-1) +
 
 end;
 
-get_companion_matrix('toto');
+[A0, AR, B] = get_companion_matrix('toto');
 
-if max(max(abs(oo_.var.toto.ar(:,:,1)-A1)))>1e-12
+if max(max(abs(AR(:,:,1)-A1)))>1e-12
    error('First order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(oo_.var.toto.ar(:,:,2)-A2)))>1e-12
+if max(max(abs(AR(:,:,2)-A2)))>1e-12
    error('Second order autoregressive matrix is wrong.')
 end
 
-if max(max(abs(oo_.var.toto.ar(:,:,3)-A3)))>1e-12
+if max(max(abs(AR(:,:,3)-A3)))>1e-12
    error('Third order autoregressive matrix is wrong.')
 end
 
@@ -116,4 +116,4 @@ CompanionMatrix = [A1, A2, A3;
 
 if max(max(abs(CompanionMatrix-oo_.var.toto.CompanionMatrix)))>1e-12
    error('Companion matrix is wrong.')
-end
\ No newline at end of file
+end
diff --git a/tests/trend-component-and-var-models/vm4.mod b/tests/trend-component-and-var-models/vm4.mod
new file mode 100644
index 000000000..4f1877260
--- /dev/null
+++ b/tests/trend-component-and-var-models/vm4.mod
@@ -0,0 +1,52 @@
+// --+ options: json=compute, stochastic +--
+
+var y x z;
+
+varexo ex ey ez;
+
+parameters a_y_1 a_y_2 b_y_1 b_y_2 b_x_1 b_x_2 ; // VAR parameters
+
+parameters beta e_c_m c_z_1 c_z_2;               // PAC equation parameters
+
+a_y_1 =  .2;
+a_y_2 =  .3;
+b_y_1 =  .1;
+b_y_2 =  .4;
+b_x_1 = -.1;
+b_x_2 = -.2;
+
+beta  =  .9;
+e_c_m =  .1;
+c_z_1 =  .7;
+c_z_2 = -.3;
+
+var_model(model_name=toto, eqtags=['eq:x', 'eq:y']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+model;
+
+[name='eq:y']
+y = a_y_1*y(-1) + a_y_2*diff(x(-1)) + b_y_1*y(-2) + b_y_2*diff(x(-2)) + ey ;
+
+[name='eq:x', data_type='nonstationary']
+diff(x) = b_x_1*y(-2) + b_x_2*diff(x(-1)) + ex ;
+
+[name='eq:pac']
+diff(z) = e_c_m*(x(-1)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+
+end;
+
+shocks;
+    var ex = 1.0;
+    var ey = 1.0;
+    var ez = 1.0;
+end;
+
+[~, b0, ~] = get_companion_matrix_legacy('toto');
+
+[~, b1, ~] = get_companion_matrix('toto');
+
+if any(abs(b0(:)-b1(:))>1e-9)
+   error('get_companion_matrix and get_comapnion_matrix_legacy do not return the same AR matrices.').
+end
\ No newline at end of file