Remove aux variables from target definition (PAC).

matlab/cherrypick.m

@@ -201,6 +201,7 @@ try
             % Note that auxlhs and rhs are already defined in the previous block, it is not possible to be here if isempty(ispac) is true.
             auxlhs{end+1} = M_.endo_names{M_.pac.(ispac.name).ec.vars(M_.pac.(ispac.name).ec.istarget)};
             rhs{end+1} = M_.aux_vars(strmatch(auxlhs{end}, M_.endo_names, 'exact')==[M_.aux_vars(:).endo_index]).orig_expr;
+            rhs{end} = remove_aux_variables_from_expression(rhs{end}, M_);
             RHS = strrep(RHS, sprintf('pac_target_nonstationary(model_name = %s)', ispac.name), sprintf('%s(-1)', auxlhs{end}));
         end
         % Print equation for unrolled PAC/VAR-expectation and update

matlab/remove_aux_variables_from_expression.m

@@ -0,0 +1,35 @@
+function expression = remove_aux_variables_from_expression(expression, DynareModel)
+
+% Copyright © 2022 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
+
+% Get list of endogenous variables in expression
+list_of_words = regexp(expression, '\<\w*\>', 'match');
+list_of_words = setdiff(list_of_words, DynareModel.param_names);
+list_of_words = setdiff(list_of_words, DynareModel.exo_names);
+isnotanumber = isnan(str2double(list_of_words));
+list_of_words = list_of_words(isnotanumber);
+list_of_words = setdiff(list_of_words, {'diff','log','exp'});
+
+for i=1:length(list_of_words)
+    id = find(strcmp(list_of_words{i}, DynareModel.endo_names));
+    if isempty(id) || id<=DynareModel.orig_endo_nbr
+        continue
+    end
+    auxinfo = DynareModel.aux_vars(get_aux_variable_id(id));
+    expression = regexprep(expression, sprintf('\\<%s\\>', list_of_words{i}), auxinfo.orig_expr);
+end

tests/Makefile.am

@@ -534,6 +534,7 @@ ECB_MODFILES = \
 	pac/var-12/example2.mod \
 	pac/var-12/example3.mod \
 	pac/var-12/example4.mod \
+	pac/var-12/example5.mod \
 	pac/var-12/example11.mod \
 	pac/var-12/example12.mod \
 	pac/trend-component-1/example1.mod \
@@ -1568,6 +1569,8 @@ clean-local:
 
 	rm -f optimal_policy/Ramsey/oo_ramsey_policy_initval.mat
 
+	rm -rf tests/pac/var-12/toto
+
 	find . -name "*.tex" -type f -delete
 	find . -name "*.aux" -type f -delete
 	find . -name "*.log" -type f -delete

tests/pac/var-12/example5.mod

@@ -0,0 +1,78 @@
+// --+ options: json=compute, stochastic +--
+
+var y x z w v;
+
+varexo ex ey ez ew;
+
+parameters a_y_1 a_y_2 b_y_1 b_y_2 b_x_1 b_x_2 d_y; // 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;
+d_y = .5;
+
+
+beta  =  .9;
+e_c_m =  .1;
+c_z_1 =  .7;
+c_z_2 = -.3;
+
+var_model(model_name=toto, structural, eqtags=['eq:x', 'eq:w', 'eq:y']);
+
+pac_model(auxiliary_model_name=toto, discount=beta, model_name=pacman);
+
+pac_target_info(pacman);
+  target v;
+  auxname_target_nonstationary vns;
+
+  component y;
+  auxname pv_y_;
+  kind ll;
+
+  component log(x);
+  growth diff(log(x(-2)));
+  auxname pv_dx_;
+  kind dd;
+
+  component log(w);
+  growth diff(log(x(-2)));
+  auxname pv_dw_;
+  kind dd;
+
+end;
+
+model;
+
+  [name='eq:y']
+  y = a_y_1*y(-1) + a_y_2*diff(log(x(-1))) + b_y_1*y(-2) + b_y_2*diff(log(x(-2))) + ey ;
+
+
+  [name='eq:x']
+  diff(log(x)) = b_x_1*y(-2) + b_x_2*diff(log(x(-1))) + ex ;
+
+  [name='eq:w']
+  diff(log(w)) = b_x_1*y(-2) + b_x_2*diff(log(w(-1))) + ew ;
+
+  [name='eq:v']
+  v = log(x) + .5*log(w) + d_y*y ;
+
+  [name='eq:pac']
+  diff(z) = e_c_m*(pac_target_nonstationary(pacman)-z(-1)) + c_z_1*diff(z(-1))  + c_z_2*diff(z(-2)) + pac_expectation(pacman) + ez;
+
+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');
+
+// Print expanded PAC_EXPECTATION term.
+pac.print('pacman', 'eq:pac');
+
+cherrypick('example5', 'toto', {'eq:pac'});