Try first Ferhat's code and if it fails try the matlab's implementation of the...

Try first Ferhat's code and if it fails try the matlab's implementation of the the perfect foresight model solver.

matlab/ep/extended_path.m

@@ -35,40 +35,35 @@ global M_ options_ oo_
 options_.verbosity = options_.ep.verbosity;
 verbosity = options_.ep.verbosity+options_.ep.debug;
 
-% Test if bytecode and block options are used (these options are mandatory)
-if ~( options_.bytecode && options_.block )
-    pfm.lead_lag_incidence = M_.lead_lag_incidence;
-    pfm.ny = M_.endo_nbr;
-    pfm.max_lag = M_.maximum_endo_lag;
-    pfm.nyp = nnz(pfm.lead_lag_incidence(1,:));
-    pfm.iyp = find(pfm.lead_lag_incidence(1,:)>0);
-    pfm.ny0 = nnz(pfm.lead_lag_incidence(2,:));
-    pfm.iy0 = find(pfm.lead_lag_incidence(2,:)>0);
-    pfm.nyf = nnz(pfm.lead_lag_incidence(3,:));
-    pfm.iyf = find(pfm.lead_lag_incidence(3,:)>0);
-    pfm.nd = pfm.nyp+pfm.ny0+pfm.nyf;
-    pfm.nrc = pfm.nyf+1;
-    pfm.isp = [1:pfm.nyp];
-    pfm.is = [pfm.nyp+1:pfm.ny+pfm.nyp];
-    pfm.isf = pfm.iyf+pfm.nyp;
-    pfm.isf1 = [pfm.nyp+pfm.ny+1:pfm.nyf+pfm.nyp+pfm.ny+1];
-    pfm.iz = [1:pfm.ny+pfm.nyp+pfm.nyf];
-    pfm.periods = options_.ep.periods;
-    pfm.steady_state = oo_.steady_state;
-    pfm.params = M_.params;
-    pfm.i_cols_1 = nonzeros(pfm.lead_lag_incidence(2:3,:)');
-    pfm.i_cols_A1 = find(pfm.lead_lag_incidence(2:3,:)');
-    pfm.i_cols_T = nonzeros(pfm.lead_lag_incidence(1:2,:)');
-    pfm.i_cols_j = 1:pfm.nd;
-    pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
-    pfm.dynamic_model = str2func([M_.fname,'_dynamic']);
-    pfm.verbose = options_.ep.verbosity;
-    pfm.maxit_ = options_.maxit_;
-    pfm.tolerance = options_.dynatol.f;
-    use_solve_perfect_foresight_models_routine = 1;
-else
-    use_solve_perfect_foresight_models_routine = 0;
-end
+% Prepare a structure needed by the matlab implementation of the perfect foresight model solver
+pfm.lead_lag_incidence = M_.lead_lag_incidence;
+pfm.ny = M_.endo_nbr;
+pfm.max_lag = M_.maximum_endo_lag;
+pfm.nyp = nnz(pfm.lead_lag_incidence(1,:));
+pfm.iyp = find(pfm.lead_lag_incidence(1,:)>0);
+pfm.ny0 = nnz(pfm.lead_lag_incidence(2,:));
+pfm.iy0 = find(pfm.lead_lag_incidence(2,:)>0);
+pfm.nyf = nnz(pfm.lead_lag_incidence(3,:));
+pfm.iyf = find(pfm.lead_lag_incidence(3,:)>0);
+pfm.nd = pfm.nyp+pfm.ny0+pfm.nyf;
+pfm.nrc = pfm.nyf+1;
+pfm.isp = [1:pfm.nyp];
+pfm.is = [pfm.nyp+1:pfm.ny+pfm.nyp];
+pfm.isf = pfm.iyf+pfm.nyp;
+pfm.isf1 = [pfm.nyp+pfm.ny+1:pfm.nyf+pfm.nyp+pfm.ny+1];
+pfm.iz = [1:pfm.ny+pfm.nyp+pfm.nyf];
+pfm.periods = options_.ep.periods;
+pfm.steady_state = oo_.steady_state;
+pfm.params = M_.params;
+pfm.i_cols_1 = nonzeros(pfm.lead_lag_incidence(2:3,:)');
+pfm.i_cols_A1 = find(pfm.lead_lag_incidence(2:3,:)');
+pfm.i_cols_T = nonzeros(pfm.lead_lag_incidence(1:2,:)');
+pfm.i_cols_j = 1:pfm.nd;
+pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
+pfm.dynamic_model = str2func([M_.fname,'_dynamic']);
+pfm.verbose = options_.ep.verbosity;
+pfm.maxit_ = options_.maxit_;
+pfm.tolerance = options_.dynatol.f;
 
 % Set default initial conditions.
 if isempty(initial_conditions)
@@ -80,10 +75,8 @@ options_.maxit_ = options_.ep.maxit;
 
 % Set the number of periods for the perfect foresight model
 options_.periods = options_.ep.periods;
-if use_solve_perfect_foresight_models_routine
-    pfm.periods = options_.ep.periods;
-    pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
-end
+pfm.periods = options_.ep.periods;
+pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
 
 % Set the algorithm for the perfect foresight solver
 options_.stack_solve_algo = options_.ep.stack_solve_algo;
@@ -226,10 +219,9 @@ while (t<sample_size)
         endo_simul = oo_.endo_simul;
         while 1
             if ~increase_periods
-                if use_solve_perfect_foresight_models_routine
+                [flag,tmp] = bytecode('dynamic');
+                if flag
                     [flag,tmp] = solve_perfect_foresight_model(oo_.endo_simul,oo_.exo_simul,pfm);
-                else
-                    [flag,tmp] = bytecode('dynamic');
                 end
                 info.convergence = ~flag;
             end
@@ -266,10 +258,8 @@ while (t<sample_size)
             else
                 % Increase the number of periods.
                 options_.periods = options_.periods + options_.ep.step;
-                if use_solve_perfect_foresight_models_routine
-                    pfm.periods = options_.periods;
-                    pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
-                end
+                pfm.periods = options_.periods;
+                pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
                 % Increment the counter.
                 increase_periods = increase_periods + 1;
                 if verbosity
@@ -300,10 +290,9 @@ while (t<sample_size)
                     oo_.exo_simul  = [ oo_.exo_simul ; zeros(options_.ep.step,size(shocks,2)) ];
                 end
                 % Solve the perfect foresight model with an increased number of periods.
-                if use_solve_perfect_foresight_models_routine
+                [flag,tmp] = bytecode('dynamic');
+                if flag
                     [flag,tmp] = solve_perfect_foresight_model(oo_.endo_simul,oo_.exo_simul,pfm);
-                else
-                    [flag,tmp] = bytecode('dynamic');
                 end
                 info.convergence = ~flag;
                 if info.convergence
@@ -316,10 +305,8 @@ while (t<sample_size)
                         % If the maximum deviation is close enough to zero, reset the number
                         % of periods to ep.periods
                         options_.periods = options_.ep.periods;
-                        if use_solve_perfect_foresight_models_routine
-                            pfm.periods = options_.periods;
-                            pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
-                        end
+                        pfm.periods = options_.periods;
+                        pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
                         % Cut oo_.exo_simul and oo_.endo_simul consistently with the resetted
                         % number of periods and exit from the while loop.
                         oo_.exo_simul = oo_.exo_simul(1:(options_.periods+2),:);