Modernization: use tilde (~) syntax for ignored output arguments

matlab/PosteriorIRF_core1.m

@@ -194,7 +194,7 @@ while fpar<B
     end
     if MAX_nirfs_dsgevar
         IRUN = IRUN+1;
-        [fval,info,junk1,junk2,junk3,junk3,junk4,PHI,SIGMAu,iXX] =  dsge_var_likelihood(deep',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
+        [fval,info,~,~,~,~,~,PHI,SIGMAu,iXX] =  dsge_var_likelihood(deep',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
         dsge_prior_weight = M_.params(strmatch('dsge_prior_weight', M_.param_names));
         DSGE_PRIOR_WEIGHT = floor(dataset_.nobs*(1+dsge_prior_weight));
         SIGMA_inv_upper_chol = chol(inv(SIGMAu*dataset_.nobs*(dsge_prior_weight+1)));

matlab/WriteShockDecomp2Excel.m

@@ -93,7 +93,7 @@ for j=1:nvar
     d0={};
     z1 = squeeze(z(i_var(j),:,:));
     if screen_shocks
-        [junk, isort] = sort(mean(abs(z1(1:end-2,:)')), 'descend');
+        [~, isort] = sort(mean(abs(z1(1:end-2,:)')), 'descend');
         labels = char(char(shock_names(isort(1:16))),'Others', 'Initial values');
         zres = sum(z1(isort(17:end),:),1);
         z1 = [z1(isort(1:16),:); zres; z1(comp_nbr0:end,:)];

matlab/annualized_shock_decomposition.m

@@ -128,7 +128,7 @@ if realtime_==0
         myopts=options_;
         myopts.plot_shock_decomp.type='qoq';
         myopts.plot_shock_decomp.realtime=0;
-        [z, junk] = plot_shock_decomposition(M_,oo_,myopts,[]);
+        [z, ~] = plot_shock_decomposition(M_,oo_,myopts,[]);
     else
         z = oo_;
     end
@@ -287,7 +287,7 @@ if isstruct(aux)
     yaux=aux.y;
 end
 
-[nvar , nterms, junk] = size(z);
+[nvar, nterms, ~] = size(z);
 for j=1:nvar
     for k =1:nterms
         ztmp = squeeze(z(j,k,min((t0-3):-4:1):end));

matlab/bksupk.m

@@ -40,7 +40,7 @@ irf = icc1+(options_.periods-1)*ny ;
 d1 = zeros(options_.periods*ny,1) ;
 
 ofs = (((options_.periods-1)*ny+1)-1)*jcf*8 ;
-junk = fseek(fid,ofs,-1) ;
+[~] = fseek(fid,ofs,-1) ;
 c = fread(fid,[jcf,ny],'float64')';
 
 d1(ir) = c(:,jcf) ;
@@ -52,7 +52,7 @@ while i <= M_.maximum_lead || i <= options_.periods
     irf1 = selif(irf,irf<=options_.periods*ny) ;
 
     ofs = (((options_.periods-i)*ny+1)-1)*jcf*8 ;
-    junk = fseek(fid,ofs,-1) ;
+    [~] = fseek(fid,ofs,-1) ;
     c = fread(fid,[jcf,ny],'float64')' ;
 
     d1(ir) = c(:,jcf) - c(:,1:size(irf1,1))*d1(irf1) ;
@@ -64,7 +64,7 @@ end
 while i <= options_.periods
 
     ofs = (((options_.periods-i)*ny+1)-1)*jcf*8 ;
-    junk = fseek(fid,ofs,-1) ;
+    [~] = fseek(fid,ofs,-1) ;
     c = fread(fid,[jcf,ny],'float64')' ;
 
     d1(ir) = c(:,jcf)-c(:,icf)*d1(irf) ;

matlab/check_list_of_variables.m

@@ -32,7 +32,7 @@ function varlist = check_list_of_variables(options_, M_, varlist)
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 % Get uniques
-[junk1, junk2, index_uniques] = varlist_indices(varlist, M_.endo_names);
+[~, ~, index_uniques] = varlist_indices(varlist, M_.endo_names);
 varlist = varlist(index_uniques);
 
 msg = false;

matlab/check_posterior_sampler_options.m

@@ -387,7 +387,7 @@ if ~strcmp(posterior_sampler_options.posterior_sampling_method,'slice')
 end
 
 if options_.load_mh_file && posterior_sampler_options.use_mh_covariance_matrix
-    [junk, invhess] = compute_mh_covariance_matrix;
+    [~, invhess] = compute_mh_covariance_matrix;
     posterior_sampler_options.invhess = invhess;
 end
 
@@ -409,7 +409,7 @@ if strcmp(posterior_sampler_options.posterior_sampling_method,'slice')
                 error('check_posterior_sampler_options:: This error should not occur, please contact developers.')
             end
             % % %             if options_.load_mh_file && options_.use_mh_covariance_matrix,
-            % % %                 [junk, invhess] = compute_mh_covariance_matrix;
+            % % %                 [~, invhess] = compute_mh_covariance_matrix;
             % % %                 posterior_sampler_options.invhess = invhess;
             % % %             end
             [V1, D]=eig(invhess);

matlab/discretionary_policy_1.m

@@ -63,7 +63,7 @@ end
 %call steady_state_file if present to update parameters
 if options_.steadystate_flag
     % explicit steady state file
-    [junk,M_.params,info] = evaluate_steady_state_file(oo_.steady_state,[oo_.exo_steady_state; oo_.exo_det_steady_state],M_, ...
+    [~,M_.params,info] = evaluate_steady_state_file(oo_.steady_state,[oo_.exo_steady_state; oo_.exo_det_steady_state],M_, ...
                                                       options_,0);
 end
 [U,Uy,W] = feval([M_.fname,'.objective.static'],zeros(endo_nbr,1),[], M_.params);
@@ -129,7 +129,7 @@ iter=1;
 for j=1:numel(Indices)
     eval(['A',Indices{j},'=zeros(eq_nbr,endo_nbr);'])
     if strcmp(Indices{j},'0')||(strcmp(Indices{j},'lag') && MaxLag)||(strcmp(Indices{j},'lead') && MaxLead)
-        [junk,row,col]=find(lead_lag_incidence(iter,:));
+        [~,row,col]=find(lead_lag_incidence(iter,:));
         eval(['A',Indices{j},'(:,row)=jacobia_(:,col);'])
         iter=iter+1;
     end

matlab/dr_block.m

@@ -409,7 +409,7 @@ for i = 1:Size
         index_c = lead_lag_incidence(2,:);             % Index of all endogenous variables present at time=t
         index_s = lead_lag_incidence(2,1:n_static);    % Index of all static endogenous variables present at time=t
         if n_static > 0
-            [Q, junk] = qr(jacob(:,index_s));
+            [Q, ~] = qr(jacob(:,index_s));
             aa = Q'*jacob;
         else
             aa = jacob;
@@ -476,7 +476,7 @@ for i = 1:Size
                     if isfield(options_,'indeterminacy_continuity')
                         if options_.indeterminacy_msv == 1
                             [ss,tt,w,q] = qz(E',D');
-                            [ss,tt,w,junk] = reorder(ss,tt,w,q);
+                            [ss,tt,w,~] = reorder(ss,tt,w,q);
                             ss = ss';
                             tt = tt';
                             w  = w';

matlab/draw_prior_density.m

@@ -111,7 +111,7 @@ switch pshape(indx)
 end
 
 if pshape(indx) ~= 5
-    [junk,k1] = max(dens);
+    [~,k1] = max(dens);
     if k1 == 1 || k1 == length(dens)
         k = find(dens > 10);
         dens(k) = NaN;

matlab/dyn_first_order_solver.m

@@ -124,7 +124,7 @@ if isempty(reorder_jacobian_columns)
                                                       nsfwrd)))];
     index_e2 = npred+nboth+(1:nboth);
 
-    [junk,cols_b] = find(lead_lag_incidence(maximum_lag+1, order_var));
+    [~,cols_b] = find(lead_lag_incidence(maximum_lag+1, order_var));
 
     reorder_jacobian_columns = [nonzeros(lead_lag_incidence(:,order_var)'); ...
                         nz+(1:exo_nbr)'];
@@ -138,7 +138,7 @@ dr.state_var = state_var;
 jacobia = jacobia(:,reorder_jacobian_columns);
 
 if nstatic > 0
-    [Q, junk] = qr(jacobia(:,index_s));
+    [Q, ~] = qr(jacobia(:,index_s));
     aa = Q'*jacobia;
 else
     aa = jacobia;

matlab/dyn_ramsey_static.m

@@ -75,7 +75,7 @@ elseif options_.steadystate_flag
     ys_init(k_inst) = inst_val;
     exo_ss = [oo.exo_steady_state oo.exo_det_steady_state];
     [xx,params] = evaluate_steady_state_file(ys_init,exo_ss,M,options_,~options_.steadystate.nocheck); %run steady state file again to update parameters
-    [junk,junk,steady_state] = nl_func(inst_val); %compute and return steady state
+    [~,~,steady_state] = nl_func(inst_val); %compute and return steady state
 else
     n_var = M.orig_endo_nbr;
     xx = oo.steady_state(1:n_var);
@@ -85,7 +85,7 @@ else
     if info1~=0
         check=81;
     end
-    [junk,junk,steady_state] = nl_func(xx);
+    [~,~,steady_state] = nl_func(xx);
 end
 
 
@@ -194,8 +194,8 @@ end
 function result = check_static_model(ys,M,options_,oo)
 result = false;
 if (options_.bytecode)
-    [chck, res, junk] = bytecode('static',ys,[oo.exo_steady_state oo.exo_det_steady_state], ...
-                                 M.params, 'evaluate');
+    [chck, res, ~] = bytecode('static',ys,[oo.exo_steady_state oo.exo_det_steady_state], ...
+                              M.params, 'evaluate');
 else
     res = feval([M.fname '.static'],ys,[oo.exo_steady_state oo.exo_det_steady_state], ...
                 M.params);

matlab/dyn_risky_steadystate_solver.m

@@ -88,7 +88,7 @@ exo_nbr = M.exo_nbr;
 
 M.var_order_endo_names = M.endo_names(dr.order_var);
 
-[junk,dr.i_fwrd_g,i_fwrd_f] = find(lead_lag_incidence(3,order_var));
+[~,dr.i_fwrd_g,i_fwrd_f] = find(lead_lag_incidence(3,order_var));
 dr.i_fwrd_f = i_fwrd_f;
 nd = nnz(lead_lag_incidence) + M.exo_nbr;
 dr.nd = nd;

matlab/dyn_second_order_solver.m

@@ -105,7 +105,7 @@ k1 = find(kstate(:,2) == M_.maximum_endo_lag+2);
 % Jacobian with respect to the variables with the highest lead
 fyp = jacobia(:,kstate(k1,3)+nnz(M_.lead_lag_incidence(M_.maximum_endo_lag+1,:)));
 B(:,nstatic+M_.npred+1:end) = fyp;
-[junk,k1,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+M_.maximum_endo_lead+1,order_var));
+[~,k1,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+M_.maximum_endo_lead+1,order_var));
 A(1:M_.endo_nbr,nstatic+1:nstatic+nspred)=...
     A(1:M_.endo_nbr,nstatic+[1:nspred])+fyp*gx1(k1,1:nspred);
 C = Gy;
@@ -162,7 +162,7 @@ kp = sum(kstate(:,2) <= M_.maximum_endo_lag+1);
 E1 = [eye(nspred); zeros(kp-nspred,nspred)];
 H = E1;
 hxx = dr.ghxx(nstatic+[1:nspred],:);
-[junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+2,order_var));
+[~,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+2,order_var));
 k3 = nnz(M_.lead_lag_incidence(1:M_.maximum_endo_lag+1,:))+(1:M_.nsfwrd)';
 [B1, err] = sparse_hessian_times_B_kronecker_C(hessian_mat(:,kh(k3,k3)),gu(k2a,:),threads_BC);
 mexErrCheck('sparse_hessian_times_B_kronecker_C', err);

matlab/dynare.m

@@ -191,7 +191,7 @@ end
 if ispc
     arch = getenv('PROCESSOR_ARCHITECTURE');
 else
-    [junk, arch] = system('uname -m');
+    [~, arch] = system('uname -m');
 end
 
 if isempty(strfind(arch, '64'))

matlab/dynare_estimation_1.m

@@ -235,8 +235,8 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
             if options_.analytic_derivation && strcmp(func2str(objective_function),'dsge_likelihood')
                 ana_deriv_old = options_.analytic_derivation;
                 options_.analytic_derivation = 2;
-                [junk1, junk2,junk3, junk4, hh] = feval(objective_function,xparam1, ...
-                                                        dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
+                [~,~,~,~,hh] = feval(objective_function,xparam1, ...
+                                     dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
                 options_.analytic_derivation = ana_deriv_old;
             elseif ~isnumeric(options_.mode_compute) || ~(isequal(options_.mode_compute,5) && newratflag~=1 && strcmp(func2str(objective_function),'dsge_likelihood'))
                 % with flag==0, we force to use the hessian from outer product gradient of optimizer 5
@@ -373,9 +373,8 @@ if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
         skipline()
     end
     if options_.dsge_var
-        [junk1,junk2,junk3,junk4,junk5,junk6,junk7,oo_.dsge_var.posterior_mode.PHI_tilde,oo_.dsge_var.posterior_mode.SIGMA_u_tilde,oo_.dsge_var.posterior_mode.iXX,oo_.dsge_var.posterior_mode.prior] =...
+        [~,~,~,~,~,~,~,oo_.dsge_var.posterior_mode.PHI_tilde,oo_.dsge_var.posterior_mode.SIGMA_u_tilde,oo_.dsge_var.posterior_mode.iXX,oo_.dsge_var.posterior_mode.prior] =...
             feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
-        clear('junk1','junk2','junk3','junk4','junk5','junk6','junk7');
     end
 
 elseif ~any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
@@ -519,7 +518,7 @@ if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
                 oo_.posterior.metropolis=oo_load_mh.oo_.posterior.metropolis;
             end
         end
-        [error_flag,junk,options_]= metropolis_draw(1,options_,estim_params_,M_);
+        [error_flag,~,options_]= metropolis_draw(1,options_,estim_params_,M_);
         if options_.bayesian_irf
             if error_flag
                 error('Estimation::mcmc: I cannot compute the posterior IRFs!')

matlab/dynare_estimation_init.m

@@ -454,38 +454,38 @@ if options_.block == 1
     % 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);
+    [~,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);
+    [~,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_dr;
     bayestopt_.mfys = k1;
     oo_.dr.restrict_columns = [size(i_posA,1)+(1:size(M_.state_var,2))];
     [k2, i_posA, i_posB] = union(k3p, M_.state_var', 'rows');
     bayestopt_.smoother_var_list = [k3p(i_posA); M_.state_var(sort(i_posB))'];
-    [junk,junk,bayestopt_.smoother_saved_var_list] = intersect(k3p,bayestopt_.smoother_var_list(:));
-    [junk,ic] = intersect(bayestopt_.smoother_var_list,M_.state_var);
+    [~,~,bayestopt_.smoother_saved_var_list] = intersect(k3p,bayestopt_.smoother_var_list(:));
+    [~,ic] = intersect(bayestopt_.smoother_var_list,M_.state_var);
     bayestopt_.smoother_restrict_columns = ic;
-    [junk,bayestopt_.smoother_mf] = ismember(k1, bayestopt_.smoother_var_list);
+    [~,bayestopt_.smoother_mf] = ismember(k1, bayestopt_.smoother_var_list);
 else
     % Define union of observed and state variables
     k2 = union(var_obs_index_dr,[M_.nstatic+1:M_.nstatic+M_.nspred]', '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([M_.nstatic+1:M_.nstatic+M_.nspred]',k2);
+    [~,bayestopt_.mf0] = ismember([M_.nstatic+1:M_.nstatic+M_.nspred]',k2);
     % Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
-    [junk,bayestopt_.mf1] = ismember(var_obs_index_dr,k2);
+    [~,bayestopt_.mf1] = ismember(var_obs_index_dr,k2);
     % Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
     bayestopt_.mf2  = var_obs_index_dr;
     bayestopt_.mfys = k1;
-    [junk,ic] = intersect(k2,nstatic+(1:npred)');
+    [~,ic] = intersect(k2,nstatic+(1:npred)');
     oo_.dr.restrict_columns = [ic; length(k2)+(1:nspred-npred)'];
     bayestopt_.smoother_var_list = union(k2,k3);
-    [junk,junk,bayestopt_.smoother_saved_var_list] = intersect(k3,bayestopt_.smoother_var_list(:));
-    [junk,ic] = intersect(bayestopt_.smoother_var_list,nstatic+(1:npred)');
+    [~,~,bayestopt_.smoother_saved_var_list] = intersect(k3,bayestopt_.smoother_var_list(:));
+    [~,ic] = intersect(bayestopt_.smoother_var_list,nstatic+(1:npred)');
     bayestopt_.smoother_restrict_columns = ic;
-    [junk,bayestopt_.smoother_mf] = ismember(var_obs_index_dr, bayestopt_.smoother_var_list);
+    [~,bayestopt_.smoother_mf] = ismember(var_obs_index_dr, bayestopt_.smoother_var_list);
 end
 
 if options_.analytic_derivation

matlab/dynare_gradient.m

@@ -52,11 +52,11 @@ for i=1:m
     else
         h = H(:,i);
     end
-    [Fh,junk1,junk2,flag] = feval(fcn, x+transpose(h), varargin{:});
+    [Fh,~,~,flag] = feval(fcn, x+transpose(h), varargin{:});
     if flag
         G(:,i) = (Fh-F)/epsilon;
     else
-        [Fh,junk1,junk2,flag] = feval(fcn, x-transpose(h), varargin{:});
+        [Fh,~,~,flag] = feval(fcn, x-transpose(h), varargin{:});
         if flag
             G(:,i) = (F-Fh)/epsilon;
         else

matlab/evaluate_steady_state.m

@@ -307,7 +307,7 @@ if M.static_and_dynamic_models_differ
     z = repmat(ys,1,M.maximum_lead + M.maximum_lag + 1);
     zx = repmat([exo_ss'], M.maximum_lead + M.maximum_lag + 1, 1);
     if options.bytecode
-        [chck, r, junk]= bytecode('dynamic','evaluate', z, zx, M.params, ys, 1);
+        [chck, r, ~]= bytecode('dynamic','evaluate', z, zx, M.params, ys, 1);
         mexErrCheck('bytecode', chck);
     elseif options.block
         [r, oo.dr] = feval([M.fname '.dynamic'], z', zx, M.params, ys, M.maximum_lag+1, oo.dr);

matlab/execute_prior_posterior_function.m

@@ -55,7 +55,7 @@ if strcmpi(type,'posterior')
     CutSample(M_, options_, estim_params_);
     %% initialize metropolis draws
     options_.sub_draws=n_draws; %set draws for sampling; changed value is not returned to base workspace
-    [error_flag,junk,options_]= metropolis_draw(1,options_,estim_params_,M_);
+    [error_flag,~,options_]= metropolis_draw(1,options_,estim_params_,M_);
     if error_flag
         error('EXECUTE_POSTERIOR_FUNCTION: The draws could not be initialized')
     end

matlab/getH.m

@@ -319,8 +319,8 @@ if nargout > 5
     end
 end
 
-[junk,cols_b,cols_j] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+1, ...
-                                                  oo_.dr.order_var));
+[~,cols_b,cols_j] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+1, ...
+                                               oo_.dr.order_var));
 GAM0 = zeros(M_.endo_nbr,M_.endo_nbr);
 Dg0 = zeros(M_.endo_nbr,M_.endo_nbr,param_nbr);
 GAM0(:,cols_b) = g1(:,cols_j);