- Corrects the first order approximation of block-decomposed models

- The block-decomposed models can now be estimated

matlab/block_mfs_steadystate.m

@@ -21,9 +21,7 @@ function [r, g1] = block_mfs_steadystate(y, b, y_all)
 
 global M_ oo_
 
-indx = M_.blocksMFS{b};
-
-y_all(indx) = y;
+y_all(M_.blocksMFS{b}) = y;
 x = [oo_.exo_steady_state; oo_.exo_det_steady_state];
 
 eval(['[r,g1] = ' M_.fname '_static(b, y_all, x, M_.params);']);

matlab/check.m

@@ -55,7 +55,7 @@ oo_.exo_simul = tempex;
 
 eigenvalues_ = dr.eigval;
 if (options_.block)
-    nyf = dr.nfwrd+dr.nboth;
+    nyf = dr.nyf;
 else
     nyf = nnz(dr.kstate(:,2)>M_.maximum_endo_lag+1);
 end;

matlab/disp_dr.m

@@ -109,7 +109,11 @@ for k=1:nx
     end;
     str = sprintf('%-20s',str1);
     for i=1:nvar
-        x = dr.ghx(ivar(i),k);
+        if options_.block
+            x = dr.ghx(i,k);
+        else
+            x = dr.ghx(ivar(i),k);
+        end;
         if abs(x) > 1e-6
             flag = 1;
             str = [str sprintf('%16.6f',x)];
@@ -128,7 +132,11 @@ for k=1:nu
     flag = 0;
     str = sprintf('%-20s',M_.exo_names(k,:));
     for i=1:nvar
-        x = dr.ghu(ivar(i),k);
+        if options_.block
+            x = dr.ghu(i,k);
+        else
+            x = dr.ghu(ivar(i),k);
+        end;
         if abs(x) > 1e-6
             flag = 1;
             str = [str sprintf('%16.6f',x)];

matlab/dynare_estimation_1.m

@@ -143,7 +143,11 @@ if isequal(options_.mode_compute,0) && isempty(options_.mode_file) && options_.m
             end
         end
         for i=bayestopt_.smoother_saved_var_list'
-            i1 = dr.order_var(bayestopt_.smoother_var_list(i));
+            if options_.block == 1
+                i1 = M_.block_structure.variable_reordered(bayestopt_.smoother_var_list(i));
+            else
+                i1 = dr.order_var(bayestopt_.smoother_var_list(i));
+            end;
             eval(['oo_.SmoothedVariables.' deblank(M_.endo_names(i1,:)) ' = atT(i,:)'';']);
             eval(['oo_.FilteredVariables.' deblank(M_.endo_names(i1,:)) ' = squeeze(aK(1,i,:));']);
             eval(['oo_.UpdatedVariables.' deblank(M_.endo_names(i1,:)) ' = updated_variables(i,:)'';']);
@@ -937,7 +941,11 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
         end
     end
     for i=bayestopt_.smoother_saved_var_list'
-        i1 = dr.order_var(bayestopt_.smoother_var_list(i));
+        if options_.block == 1
+            i1 = M_.block_structure.variable_reordered(bayestopt_.smoother_var_list(i));
+        else
+            i1 = dr.order_var(bayestopt_.smoother_var_list(i));
+        end;
         eval(['oo_.SmoothedVariables.' deblank(M_.endo_names(i1,:)) ' = atT(i,:)'';']);
         eval(['oo_.FilteredVariables.' deblank(M_.endo_names(i1,:)) ' = squeeze(aK(1,i,:));']);
         eval(['oo_.UpdatedVariables.' deblank(M_.endo_names(i1,:)) ...

matlab/dynare_estimation_init.m

@@ -208,19 +208,32 @@ for i=1:n_varobs
     k1 = [k1 strmatch(deblank(options_.varobs(i,:)),M_.endo_names, 'exact')];
 end
 % Define union of observed and state variables
-k2 = union(var_obs_index',[dr.nstatic+1:dr.nstatic+dr.npred]', 'rows');
-% Set restrict_state to postion of observed + state variables in expanded state vector.
-oo_.dr.restrict_var_list = k2;
-% set mf0 to positions of state variables in restricted state vector for likelihood computation.
-[junk,bayestopt_.mf0] = ismember([dr.nstatic+1:dr.nstatic+dr.npred]',k2);
-% Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
-[junk,bayestopt_.mf1] = ismember(var_obs_index,k2); 
-% Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
-bayestopt_.mf2  = var_obs_index;
-bayestopt_.mfys = k1;
-
-[junk,ic] = intersect(k2,nstatic+(1:npred)');
-oo_.dr.restrict_columns = [ic; length(k2)+(1:nspred-npred)'];
+if options_.block == 1
+    [k2, i_posA, i_posB] = union(k1', M_.state_var', 'rows');
+    % Set restrict_state to postion of observed + state variables in expanded state vector.
+    oo_.dr.restrict_var_list  = [k1(i_posA) M_.state_var(sort(i_posB))];
+    % set mf0 to positions of state variables in restricted state vector for likelihood computation.
+    [junk,bayestopt_.mf0] = ismember(M_.state_var',oo_.dr.restrict_var_list);
+    % Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
+    [junk,bayestopt_.mf1] = ismember(k1,oo_.dr.restrict_var_list); 
+    % Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
+    bayestopt_.mf2  = var_obs_index;
+    bayestopt_.mfys = k1;
+    oo_.dr.restrict_columns = [size(i_posA,1)+(1:size(M_.state_var,2))];
+else
+    k2 = union(var_obs_index',[dr.nstatic+1:dr.nstatic+dr.npred]', 'rows');
+    % Set restrict_state to postion of observed + state variables in expanded state vector.
+    oo_.dr.restrict_var_list = k2;
+    % set mf0 to positions of state variables in restricted state vector for likelihood computation.
+    [junk,bayestopt_.mf0] = ismember([dr.nstatic+1:dr.nstatic+dr.npred]',k2);
+    % Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
+    [junk,bayestopt_.mf1] = ismember(var_obs_index,k2); 
+    % Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
+    bayestopt_.mf2  = var_obs_index;
+    bayestopt_.mfys = k1;
+    [junk,ic] = intersect(k2,nstatic+(1:npred)');
+    oo_.dr.restrict_columns = [ic; length(k2)+(1:nspred-npred)'];
+end;
 
 k3 = [];
 if options_.selected_variables_only

matlab/set_state_space.m

@@ -31,6 +31,7 @@ function dr=set_state_space(dr,M_)
 %
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+global options_
 
 max_lead = M_.maximum_endo_lead;
 max_lag = M_.maximum_endo_lag;
@@ -54,7 +55,11 @@ nboth = length(both_var);
 npred = length(pred_var);
 nfwrd = length(fwrd_var);
 nstatic = length(stat_var);
-order_var = [ stat_var(:); pred_var(:); both_var(:); fwrd_var(:)];
+if options_.block == 1
+    order_var = M_.block_structure.variable_reordered;
+else
+    order_var = [ stat_var(:); pred_var(:); both_var(:); fwrd_var(:)];
+end;
 inv_order_var(order_var) = (1:endo_nbr);
 
 % building kmask for z state vector in t+1

matlab/simult_.m

@@ -67,8 +67,13 @@ if options_.k_order_solver% Call dynare++ routines.
     y_(dr.order_var,:) = y_;
 else
     if options_.block
-        k2 = [dr.glb_pred dr.glb_both];
+        if M_.maximum_lag > 0
+            k2 = dr.state_var;
+        else
+            k2 = [];
+        end;
         order_var = 1:M_.endo_nbr;
+        dr.order_var = order_var;
     else
         k2 = dr.kstate(find(dr.kstate(:,2) <= M_.maximum_lag+1),[1 2]);
         k2 = k2(:,1)+(M_.maximum_lag+1-k2(:,2))*M_.endo_nbr;