Remove unnecessary square brackets.

matlab/+bvar/forecast.m

@@ -95,7 +95,7 @@ end
 
 if p > 0
     skipline()
-    disp(['Some of the VAR models sampled from the posterior distribution'])
+    disp('Some of the VAR models sampled from the posterior distribution')
     disp(['were found to be explosive (' num2str(p/options_.bvar_replic) ' percent).'])
     skipline()
 end

matlab/+bvar/irf.m

@@ -96,7 +96,7 @@ end
 
 if p > 0
     skipline()
-    disp(['Some of the VAR models sampled from the posterior distribution'])
+    disp('Some of the VAR models sampled from the posterior distribution')
     disp(['were found to be explosive (' int2str(p) ' samples).'])
     skipline()
 end

matlab/+gsa/Sampling_Function_2.m

@@ -147,7 +147,7 @@ for i=1:r
     %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
     % a --> Define the random vector x0 for the factors. Note that x0 takes value in the hypercube
     % [0,...,1-Delta]*[0,...,1-Delta]*[0,...,1-Delta]*[0,...,1-Delta]
-    MyInt = repmat([0:(1/(p-1)):(1-Delta)],NumFact,1);     % Construct all possible values of the factors
+    MyInt = repmat(0:(1/(p-1)):(1-Delta),NumFact,1);     % Construct all possible values of the factors
 
     % OLD VERSION - it needs communication toolbox
     % w = randint(NumFact,1,[1,size(MyInt,2)]);

matlab/+identification/checks.m

@@ -94,7 +94,7 @@ if param_nbr > 0 && (rankX<rankrequired)
     % search for singular values associated to ONE individual parameter
     % Compute an orthonormal basis for the null space using the columns of ee1 that correspond
     % to singular values equal to zero and associated to an individual parameter
-    ee0 = [rankX+1:size([Xparnonzero Xrest],2)]; %look into last columns with singular values of problematic parameter sets (except single parameters)
+    ee0 = rankX+1:size([Xparnonzero Xrest],2); %look into last columns with singular values of problematic parameter sets (except single parameters)
     ind11 = ones(length(ind1),1); %initialize
     for j=1:length(ee0)
         % check if nullspace is spanned by only one parameter
@@ -151,9 +151,9 @@ if param_nbr>0 && (rankX<rankrequired || min(1-Mco)<tol_rank)
     if length(ind1)<param_nbr
         % single parameters with zero columns
         ixno = ixno + 1;
-        indno(ixno,:) = (~ismember([1:param_nbr],ind1));
+        indno(ixno,:) = (~ismember(1:param_nbr,ind1));
     end
-    ee0 = [rankX+1:size([Xparnonzero Xrest],2)]; %look into last columns with singular values of problematic parameter sets (except single parameters)
+    ee0 = rankX+1:size([Xparnonzero Xrest],2); %look into last columns with singular values of problematic parameter sets (except single parameters)
     for j=1:length(ee0)
         % linearly dependent parameters
         ixno = ixno + 1;

matlab/+identification/display.m

@@ -54,7 +54,7 @@ tol_rank           = options_ident.tol_rank;
 checks_via_subsets = options_ident.checks_via_subsets;
 
 %% Display settings
-disp(['  ']),
+disp('  '),
 fprintf('Note that differences in the criteria could be due to numerical settings,\n')
 fprintf('numerical errors or the method used to find problematic parameter sets.\n')
 fprintf('Settings:\n')
@@ -157,7 +157,7 @@ for jide = 1:4
                 disp('    !!!WARNING!!!');
                 if SampleSize>1
                     if non_minimal_state_space_error
-                        fprintf(['\n    The minimal state space could not be computed for %u out of %u cases.\n'],SampleSize-EffectiveSampleSize,SampleSize);
+                        fprintf('\n    The minimal state space could not be computed for %u out of %u cases.\n',SampleSize-EffectiveSampleSize,SampleSize);
                     end
                     if jide==2
                         if sum(ide.ino & ide.minimal_state_space)>0

matlab/+mom/objective_function.m

@@ -236,7 +236,7 @@ if strcmp(options_mom_.mom.mom_method,'SMM')
     % remove burn-in and focus on observables (note that y_sim is in declaration order)
     y_sim = y_sim(oo_.dr.order_var(oo_.mom.obs_var) , end-options_mom_.mom.long+1:end)';
     if ~all(diag(M_.H)==0)
-        i_ME = setdiff([1:size(M_.H,1)],find(diag(M_.H) == 0)); % find ME with 0 variance
+        i_ME = setdiff(1:size(M_.H,1),find(diag(M_.H) == 0)); % find ME with 0 variance
         chol_S = chol(M_.H(i_ME,i_ME)); % decompose rest
         shock_mat=zeros(size(options_mom_.mom.ME_shock_series)); % initialize
         shock_mat(:,i_ME)=options_mom_.mom.ME_shock_series(:,i_ME)*chol_S;

matlab/+occbin/graph.m

@@ -109,7 +109,7 @@ for fig = 1:nbplt
             if ndim==2
                 legend([h1,h2],legend_list,'box','off')
             else
-                legend([h1],legend_list,'box','off')
+                legend(h1,legend_list,'box','off')
             end        
         end
         if options_.TeX

matlab/+pruned_SS/bivmom.m

@@ -76,8 +76,8 @@ if odd
    end
    y = y*rho;
 end
-y = prod([1:2:s1])*prod([1:2:s2])*y;
+y = prod(1:2:s1)*prod(1:2:s2)*y;
 if nargout > 1
-    dy = prod([1:2:s1])*prod([1:2:s2])*dy;
+    dy = prod(1:2:s1)*prod(1:2:s2)*dy;
 end
 

matlab/+pruned_SS/prodmom.m

@@ -61,7 +61,7 @@ s2 = s/2;
 %  Use univariate normal results
 %
 if m==1
-   y = V^s2*prod([1:2:s-1]);
+   y = V^s2*prod(1:2:s-1);
    return
 end
 %
@@ -104,4 +104,4 @@ for i=1:fix(prod(nu+1)/2)
         end
     end
 end
-y = y/prod([1:s2]);
+y = y/prod(1:s2);

matlab/+pruned_SS/prodmom_deriv.m

@@ -82,9 +82,9 @@ s2 = s/2;
 %  Use univariate normal results
 %
 if m==1
-    y = V^s2*prod([1:2:s-1]);
+    y = V^s2*prod(1:2:s-1);
     if nargout > 1
-        dy = s2*V^(s2-1)*dV*prod([1:2:s-1]);
+        dy = s2*V^(s2-1)*dV*prod(1:2:s-1);
         dy = reshape(dy,1,size(dV,3));
     end
    return
@@ -169,8 +169,8 @@ for i=1:fix(prod(nu+1)/2)
         end
     end
 end
-y = y/prod([1:s2]);
+y = y/prod(1:s2);
 if nargout > 1
-    dy = dy/prod([1:s2]);
+    dy = dy/prod(1:s2);
     dy = reshape(dy,1,size(dV,3));
 end

matlab/backward/backward_model_forecast.m

@@ -31,7 +31,7 @@ global M_ options_ oo_
 
 % Check that the model is actually backward
 if M_.maximum_lead
-    error(['backward_model_forecast:: The specified model is not backward looking!'])
+    error('backward_model_forecast:: The specified model is not backward looking!')
 end
 
 % Initialize returned argument.

matlab/check_list_of_variables.m

@@ -72,7 +72,7 @@ elseif isempty(varlist) && ~isempty(options_.endo_vars_for_moment_computations_i
     end
 elseif isempty(varlist) && isempty(options_.endo_vars_for_moment_computations_in_estimation)
     skipline()
-    disp(['You did not declare endogenous variables after the estimation/calib_smoother command.'])
+    disp('You did not declare endogenous variables after the estimation/calib_smoother command.')
     cas = '';
     if options_.bayesian_irf
         cas = 'Posterior IRFs';

matlab/clear_persistent_variables.m

@@ -79,7 +79,7 @@ if isempty(ext)
     fname = [pathstr, name, '.m'];
 else
     if ~isequal(ext, '.m')
-        error(['The first argument needs to be the name of a matlab script (with an .m extension)!'])
+        error('The first argument needs to be the name of a matlab script (with an .m extension)!')
     end
 end
 
@@ -88,13 +88,13 @@ if ~iscell(c)
 end
 
 if ndims(c)>2
-    error(['The cell passed has a second argument cannot have more than two dimensions!'])
+    error('The cell passed has a second argument cannot have more than two dimensions!')
 end
 
 variablename = inputname(2);
 
 if isempty(variablename) && nargin<3
-    error(['You must pass the name of the cell (second input argument) as a string in the third input argument!'])
+    error('You must pass the name of the cell (second input argument) as a string in the third input argument!')
 end
 
 if nargin>2

matlab/convergence_diagnostics/mcmc_diagnostics_core.m

@@ -81,7 +81,7 @@ UDIAG = zeros(NumberOfLines,6,npar-fpar+1);
 if whoiam
     waitbarString = ['Please wait... MCMC diagnostics (' int2str(fpar) 'of' int2str(npar) ')...'];
     if Parallel(ThisMatlab).Local
-        waitbarTitle=['Local '];
+        waitbarTitle='Local ';
     else
         waitbarTitle=[Parallel(ThisMatlab).ComputerName];
     end

matlab/datatomfile.m

@@ -63,7 +63,7 @@ ivar=zeros(n, 1);
 for i=1:n
     i_tmp = strmatch(var_list{i}, M_.endo_names, 'exact');
     if isempty(i_tmp)
-        error (['One of the specified variables does not exist']) ;
+        error ('One of the specified variables does not exist') ;
     else
         ivar(i) = i_tmp;
     end

matlab/distributions/beta_specification.m

@@ -47,7 +47,7 @@ else
 end
 
 if mu<lb
-    error(['The prior expectation (%f) %scannot be smaller than the lower bound of the Beta distribution (%f)!'], mu, name1, lb)
+    error('The prior expectation (%f) %scannot be smaller than the lower bound of the Beta distribution (%f)!', mu, name1, lb)
 end
 
 if mu>ub

matlab/dseries

@@ -1 +1 @@
-Subproject commit 4fceb85e3a1d10e8040b60bcb5cf79a3c533956c
+Subproject commit c799f003eb8d8ca51ef2926f1e9deec1f80cb5e2

matlab/dyn2vec.m

@@ -36,9 +36,9 @@ if ~(nargin >= 3)
 end
 
 if options_.smpl == 0
-    k = [1:size(oo_.endo_simul,2)];
+    k = 1:size(oo_.endo_simul,2);
 else
-    k = [M_.maximum_lag+options_.smpl(1):M_.maximum_lag+options_.smpl(2)];
+    k = M_.maximum_lag+options_.smpl(1):M_.maximum_lag+options_.smpl(2);
 end
 
 if nargin == 3

matlab/dyn_waitbar.m

@@ -69,7 +69,7 @@ else
         running_text = varargin{2};
     end
     if Parallel.Local
-        waitbarTitle=['Local '];
+        waitbarTitle='Local ';
     else
         waitbarTitle=[Parallel.ComputerName];
     end

matlab/dynare.m

@@ -83,7 +83,7 @@ if isoctave
         skipline()
     elseif octave_ver_less_than('7.1.0') % Should match the test in meson.build, and also the one in matlab/modules/dseries/src/initialize_dseries_class.m
         skipline()
-        warning(['This version of Dynare has only been tested on Octave 7.1.0 and above. Dynare may fail to run or give unexpected result. Consider upgrading your version of Octave.'])
+        warning('This version of Dynare has only been tested on Octave 7.1.0 and above. Dynare may fail to run or give unexpected result. Consider upgrading your version of Octave.')
         skipline()
     end
 else

matlab/endogenous_prior_restrictions.m

@@ -132,7 +132,7 @@ if ~isempty(endo_prior_restrictions.moment)
     for i=1:nvar
         i_tmp = strmatch(var_list_(i,:), M_.endo_names, 'exact');
         if isempty(i_tmp)
-            error (['One of the variable specified does not exist'])
+            error ('One of the variable specified does not exist')
         else
             ivar(i) = i_tmp;
         end

matlab/ep/setup_stochastic_perfect_foresight_model_solver.m

@@ -49,11 +49,11 @@ else
 end
 pfm.nd = pfm.nyp+pfm.ny0+pfm.nyf;
 pfm.nrc = pfm.nyf+1;
-pfm.isp = [1:pfm.nyp];
+pfm.isp = 1:pfm.nyp;
-pfm.is = [pfm.nyp+1:pfm.ny+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.isf1 = pfm.nyp+pfm.ny+1:pfm.nyf+pfm.nyp+pfm.ny+1;
-pfm.iz = [1:pfm.ny+pfm.nyp+pfm.nyf];
+pfm.iz = 1:pfm.ny+pfm.nyp+pfm.nyf;
 pfm.periods = options_.ep.periods;
 pfm.steady_state = oo_.steady_state;
 pfm.params = M_.params;

matlab/ep/solve_stochastic_perfect_foresight_model_1.m

@@ -70,8 +70,8 @@ else
 end
 
 if verbose
-    disp ([' -----------------------------------------------------']);
+    disp (' -----------------------------------------------------');
-    disp (['MODEL SIMULATION :']);
+    disp ('MODEL SIMULATION :');
     fprintf('\n');
 end
 

matlab/estimation/GetPosteriorParametersStatistics.m

@@ -337,7 +337,7 @@ fprintf(fidTeX, '  & \\multicolumn{3}{c}{Prior}  &  \\multicolumn{4}{c}{Posterio
 fprintf(fidTeX, '  \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-8}\n');
 fprintf(fidTeX, '  & Dist. & Mean  & Stdev. & Mean & Stdev. & HPD inf & HPD sup\\\\\n');
 fprintf(fidTeX, '\\midrule \\endfirsthead \n');
-fprintf(fidTeX, ['\\caption{(continued)}\\\\']);
+fprintf(fidTeX, '\\caption{(continued)}\\\\');
 fprintf(fidTeX, '\\toprule \n');
 fprintf(fidTeX, '  & \\multicolumn{3}{c}{Prior}  &  \\multicolumn{4}{c}{Posterior} \\\\\n');
 fprintf(fidTeX, '  \\cmidrule(r{.75em}){2-4} \\cmidrule(r{.75em}){5-8}\n');
@@ -349,7 +349,7 @@ fid = fidTeX;
 
 
 function TeXCore(fid, name, shape, priormean, priorstd, postmean, poststd, hpd)
-fprintf(fid,['$%s$ & %s & %7.3f & %6.4f & %7.3f& %6.4f & %7.4f & %7.4f \\\\ \n'],...
+fprintf(fid,'$%s$ & %s & %7.3f & %6.4f & %7.3f& %6.4f & %7.4f & %7.4f \\\\ \n',...
         name, ...
         shape, ...
         priormean, ...

matlab/estimation/PosteriorIRF.m

@@ -400,7 +400,7 @@ if ~options_.nograph && ~options_.no_graph.posterior
                     fprintf(fidTeX,'\\centering \n');
                     fprintf(fidTeX,'\\includegraphics[width=%2.2f\\textwidth]{%s/%s_Bayesian_IRF_%s_%d}\n',options_.figures.textwidth*min(subplotnum/nn,1),DirectoryName,M_.fname,tit{ii},figunumber);
                     if options_.relative_irf
-                        fprintf(fidTeX,['\\caption{Bayesian relative IRF.}']);
+                        fprintf(fidTeX,'\\caption{Bayesian relative IRF.}');
                     else
                         fprintf(fidTeX,'\\caption{Bayesian IRF: Orthogonalized shock to $%s$.}\n',titTeX{ii});
                     end

matlab/estimation/compute_Pinf_Pstar.m

@@ -60,7 +60,7 @@ end
 
 if nargin == 6
     indx = restrict_columns;
-    indx0=find(~ismember([1:np],indx));
+    indx0=find(~ismember(1:np,indx));
 else
     indx=(find(max(abs(T))>=1.e-10));
     indx0=(find(max(abs(T))<1.e-10));

matlab/estimation/display_estimation_results_table.m

@@ -204,7 +204,7 @@ if any(bayestopt_.pshape > 0) && options_.TeX %% Bayesian estimation (posterior
         ip = 1;
         for i=1:nvx
             k = estim_params_.var_exo(i,1);
-            fprintf(fidTeX,[ '$%s$ & %4s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n'],...
+            fprintf(fidTeX, '$%s$ & %4s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n',...
                     M_.exo_names_tex{k},...
                     pnames{bayestopt_.pshape(ip)+1},...
                     bayestopt_.p1(ip),...
@@ -241,7 +241,7 @@ if any(bayestopt_.pshape > 0) && options_.TeX %% Bayesian estimation (posterior
         for i=1:ncx
             k1 = estim_params_.corrx(i,1);
             k2 = estim_params_.corrx(i,2);
-            fprintf(fidTeX,[ '$%s$ & %s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n'],...
+            fprintf(fidTeX, '$%s$ & %s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n',...
                     [M_.exo_names_tex{k1} ',' M_.exo_names_tex{k2}], ...
                     pnames{bayestopt_.pshape(ip)+1}, ...
                     bayestopt_.p1(ip), ...
@@ -294,7 +294,7 @@ elseif all(bayestopt_.pshape == 0) && options_.TeX %% MLE and GMM Latex output
         ip = 1;
         for i=1:nvx
             k = estim_params_.var_exo(i,1);
-            fprintf(fidTeX,[ '$%s$ & %8.4f & %7.4f & %7.4f\\\\ \n'], ...
+            fprintf(fidTeX, '$%s$ & %8.4f & %7.4f & %7.4f\\\\ \n', ...
                     M_.exo_names_tex{k}, ...
                     xparam1(ip), ...
                     stdh(ip), ...
@@ -327,7 +327,7 @@ elseif all(bayestopt_.pshape == 0) && options_.TeX %% MLE and GMM Latex output
         for i=1:ncx
             k1 = estim_params_.corrx(i,1);
             k2 = estim_params_.corrx(i,2);
-            fprintf(fidTeX,[ '$%s$  & %8.4f & %7.4f & %7.4f \\\\ \n'], ...
+            fprintf(fidTeX, '$%s$  & %8.4f & %7.4f & %7.4f \\\\ \n', ...
                     [M_.exo_names_tex{k1} ',' M_.exo_names_tex{k2}], ...
                     xparam1(ip), ...
                     stdh(ip), ...

matlab/estimation/dsge_likelihood.m

@@ -394,7 +394,7 @@ switch options_.lik_init
         end
     catch ME
         disp(ME.message)
-        disp(['dsge_likelihood:: I am not able to solve the Riccati equation, so I switch to lik_init=1!']);
+        disp('dsge_likelihood:: I am not able to solve the Riccati equation, so I switch to lik_init=1!');
         options_.lik_init = 1;
         Pstar=lyapunov_solver(T,R,Q,options_);
     end
@@ -886,7 +886,7 @@ if isfield(M_,'filter_initial_state') && ~isempty(M_.filter_initial_state)
             elseif ~options_.loglinear && ~options_.logged_steady_state
                 a(bayestopt_.mf0(ii)) = eval(M_.filter_initial_state{state_indices(ii),2}) - dr.ys(state_indices(ii));
             else
-                error(['The steady state is logged. This should not happen. Please contact the developers'])
+                error('The steady state is logged. This should not happen. Please contact the developers')
             end
         end
     end

matlab/estimation/dynare_estimation_init.m

@@ -111,7 +111,7 @@ end
 % Set options_.lik_init equal to 3 if diffuse filter is used or kalman_algo refers to a diffuse filter algorithm.
 if isequal(options_.diffuse_filter,1) || (options_.kalman_algo>2)
     if isequal(options_.lik_init,2)
-        error(['options diffuse_filter, lik_init and/or kalman_algo have contradictory settings'])
+        error('options diffuse_filter, lik_init and/or kalman_algo have contradictory settings')
     else
         options_.lik_init = 3;
     end
@@ -428,7 +428,7 @@ if info(1)
         M_local.params=params;
         plist = list_of_parameters_calibrated_as_NaN(M_local);
         if ~isempty(plist)
-            message = ['dynare_estimation_init:: Some of the parameters are NaN (' ];
+            message = 'dynare_estimation_init:: Some of the parameters are NaN (' ;
             for i=1:length(plist)
                 if i<length(plist)
                     message = [message, plist{i} ', '];
@@ -440,7 +440,7 @@ if info(1)
         fprintf('%s\n',message)
         plist = list_of_parameters_calibrated_as_Inf(M_local);
         if ~isempty(plist)
-            message = ['dynare_estimation_init:: Some of the parameters are Inf (' ];
+            message = 'dynare_estimation_init:: Some of the parameters are Inf (' ;
             for i=1:length(plist)
                 if i<size(plist)
                     message = [message, plist{i} ', '];

matlab/estimation/initial_estimation_checks.m

@@ -125,30 +125,30 @@ non_zero_ME=length(estim_params_.H_entries_to_check_for_positive_definiteness);
 
 print_init_check_warning=false;
 if maximum_number_non_missing_observations>M_.exo_nbr+non_zero_ME
-    error(['initial_estimation_checks:: Estimation can''t take place because there are less declared shocks than observed variables!'])
+    error('initial_estimation_checks:: Estimation can''t take place because there are less declared shocks than observed variables!')
 end
 if init_number_non_missing_observations>M_.exo_nbr+non_zero_ME
     if options_.no_init_estimation_check_first_obs
         print_init_check_warning=true;
     else
-        error(['initial_estimation_checks:: Estimation can''t take place because there are less declared shocks than observed variables in first period!'])
+        error('initial_estimation_checks:: Estimation can''t take place because there are less declared shocks than observed variables in first period!')
     end
 end
 
 if options_.heteroskedastic_filter
     if any(observations_by_period>(non_zero_shocks_by_period+non_zero_ME))
-        error(['initial_estimation_checks:: Estimation can''t take place because too many shocks have been calibrated with a zero variance: Check heteroskedastic block and shocks calibration!'])
+        error('initial_estimation_checks:: Estimation can''t take place because too many shocks have been calibrated with a zero variance: Check heteroskedastic block and shocks calibration!')
     end
 else
     if maximum_number_non_missing_observations>length(find(diag(M_.Sigma_e)))+non_zero_ME
-        error(['initial_estimation_checks:: Estimation can''t take place because too many shocks have been calibrated with a zero variance!'])
+        error('initial_estimation_checks:: Estimation can''t take place because too many shocks have been calibrated with a zero variance!')
     end
 end
 if init_number_non_missing_observations>length(find(diag(M_.Sigma_e)))+non_zero_ME
     if options_.no_init_estimation_check_first_obs
         print_init_check_warning=true;
     else
-        error(['initial_estimation_checks:: Estimation can''t take place because too many shocks have been calibrated with a zero variance in first period!'])
+        error('initial_estimation_checks:: Estimation can''t take place because too many shocks have been calibrated with a zero variance in first period!')
     end
 end
 if print_init_check_warning
@@ -158,7 +158,7 @@ if print_init_check_warning
 end
 
 if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) && options_.mh_nblck<1
-    error(['initial_estimation_checks:: Bayesian estimation cannot be conducted with mh_nblocks=0.'])
+    error('initial_estimation_checks:: Bayesian estimation cannot be conducted with mh_nblocks=0.')
 end
 
 % check and display warnings if steady-state solves static model (except if diffuse_filter == 1) and if steady-state changes estimated parameters
@@ -251,8 +251,8 @@ end
 
 if options_.prefilter==1
     if (~options_.loglinear && any(abs(oo_.steady_state(bayestopt_.mfys))>1e-9)) || (options_.loglinear && any(abs(log(oo_.steady_state(bayestopt_.mfys)))>1e-9))
-        disp(['You are trying to estimate a model with a non zero steady state for the observed endogenous'])
+        disp('You are trying to estimate a model with a non zero steady state for the observed endogenous')
-        disp(['variables using demeaned data!'])
+        disp('variables using demeaned data!')
         error('You should change something in your mod file...')
     end
 end

matlab/estimation/model_comparison.m

@@ -91,14 +91,14 @@ for i=1:NumberOfModels
     catch
         if strcmpi(type,'LaplaceApproximation')
             if isfield(mstruct.oo_,'mle_mode')
-                disp(['MODEL_COMPARISON: Model comparison is a Bayesian approach and does not support models estimated with ML'])
+                disp('MODEL_COMPARISON: Model comparison is a Bayesian approach and does not support models estimated with ML')
             else
                 disp(['MODEL_COMPARISON: I cant''t find the Laplace approximation associated to model ' ModelNames{i}])
             end
             return
         elseif strcmpi(type,'ModifiedHarmonicMean')
             if isfield(mstruct.oo_,'mle_mode')
-                disp(['MODEL_COMPARISON: Model comparison is a Bayesian approach and does not support models estimated with ML'])
+                disp('MODEL_COMPARISON: Model comparison is a Bayesian approach and does not support models estimated with ML')
             else
                 disp(['MODEL_COMPARISON: I cant''t find the modified harmonic mean estimate associated to model ' ModelNames{i}])
             end

matlab/estimation/pm3.m

@@ -155,7 +155,7 @@ elseif filter_covar_indicator
         hpd_interval=NaN([size_matrix(1:3),2]);
     end
     if size(stock1_filter_covar,draw_dimension)>9
-        post_deciles =quantile(stock1_filter_covar,[0.1:0.1:0.9],draw_dimension);
+        post_deciles =quantile(stock1_filter_covar,0.1:0.1:0.9,draw_dimension);
     else
         size_matrix=size(stock1_filter_covar);
         post_deciles=NaN([size_matrix(1:3),9]);
@@ -177,7 +177,7 @@ elseif state_uncert_indicator
         hpd_interval=NaN([size_matrix(1:3),2]);
     end
     if size(stock1_state_uncert,draw_dimension)>9
-        post_deciles =quantile(stock1_state_uncert,[0.1:0.1:0.9],draw_dimension);
+        post_deciles =quantile(stock1_state_uncert,0.1:0.1:0.9,draw_dimension);
     else
         size_matrix=size(stock1_state_uncert);
         post_deciles=NaN([size_matrix(1:3),9]);

matlab/estimation/posterior_analysis.m

@@ -38,7 +38,7 @@ switch info
     nvar = length(ivar);
     oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_,nvar,vartan);
   otherwise
-    error(['posterior_analysis:: Check_posterior_analysis_data gave a meaningless output!'])
+    error('posterior_analysis:: Check_posterior_analysis_data gave a meaningless output!')
 end
 
 

matlab/estimation/posterior_sampler_core.m

@@ -227,23 +227,23 @@ for curr_block = fblck:nblck
             end
             save([BaseName '_mh' int2str(NewFile(curr_block)) '_blck' int2str(curr_block) '.mat'],'x2','logpo2','LastSeeds','accepted_draws_this_chain','accepted_draws_this_file','feval_this_chain','feval_this_file');
             fidlog = fopen([MetropolisFolder '/metropolis.log'],'a');
-            fprintf(fidlog,['\n']);
+            fprintf(fidlog,'\n');
             fprintf(fidlog,['%% Mh' int2str(NewFile(curr_block)) 'Blck' int2str(curr_block) ' (' datestr(now,0) ')\n']);
             fprintf(fidlog,' \n');
             fprintf(fidlog,['  Number of simulations.: ' int2str(length(logpo2)) '\n']);
             fprintf(fidlog,['  Acceptance ratio......: ' num2str(accepted_draws_this_file/length(logpo2)) '\n']);
             fprintf(fidlog,['  Feval per iteration...: ' num2str(feval_this_file/length(logpo2)) '\n']);
-            fprintf(fidlog,['  Posterior mean........:\n']);
+            fprintf(fidlog,'  Posterior mean........:\n');
             for i=1:length(x2(1,:))
                 fprintf(fidlog,['    params:' int2str(i) ': ' num2str(mean(x2(:,i))) '\n']);
             end
             fprintf(fidlog,['    log2po:' num2str(mean(logpo2)) '\n']);
-            fprintf(fidlog,['  Minimum value.........:\n']);
+            fprintf(fidlog,'  Minimum value.........:\n');
             for i=1:length(x2(1,:))
                 fprintf(fidlog,['    params:' int2str(i) ': ' num2str(min(x2(:,i))) '\n']);
             end
             fprintf(fidlog,['    log2po:' num2str(min(logpo2)) '\n']);
-            fprintf(fidlog,['  Maximum value.........:\n']);
+            fprintf(fidlog,'  Maximum value.........:\n');
             for i=1:length(x2(1,:))
                 fprintf(fidlog,['    params:' int2str(i) ': ' num2str(max(x2(:,i))) '\n']);
             end

matlab/estimation/posterior_sampler_initialization.m

@@ -182,7 +182,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
     % Find initial values for the NumberOfBlocks chains...
     if NumberOfBlocks > 1 || options_.mh_initialize_from_previous_mcmc.status% Case 1: multiple chains
         options_=set_dynare_seed_local_options(options_,'default');
-        fprintf(fidlog,['  Initial values of the parameters:\n']);
+        fprintf(fidlog,'  Initial values of the parameters:\n');
         fprintf('%s: Searching for initial values...\n', dispString);
         if ~options_.mh_initialize_from_previous_mcmc.status
             ix2 = zeros(NumberOfBlocks,npar);
@@ -253,7 +253,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
         fprintf(fidlog,' \n');
         fprintf('%s: Initial values found!\n\n',dispString);
     else% Case 2: one chain (we start from the posterior mode)
-        fprintf(fidlog,['  Initial values of the parameters:\n']);
+        fprintf(fidlog,'  Initial values of the parameters:\n');
         candidate = transpose(xparam1(:));%
         if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
             ix2 = candidate;
@@ -313,10 +313,10 @@ if ~options_.load_mh_file && ~options_.mh_recover
     fprintf('Ok!\n');
     id = write_mh_history_file(MetropolisFolder, ModelName, record);
     fprintf('%s: Details about the MCMC are available in %s_mh_history_%u.mat\n\n', dispString,BaseName,id);
-    fprintf(fidlog,['  CREATION OF THE MH HISTORY FILE!\n\n']);
+    fprintf(fidlog,'  CREATION OF THE MH HISTORY FILE!\n\n');
     fprintf(fidlog,['    Expected number of files per block.......: ' int2str(AnticipatedNumberOfFiles) '.\n']);
     fprintf(fidlog,['    Expected number of lines in the last file: ' int2str(AnticipatedNumberOfLinesInTheLastFile) '.\n']);
-    fprintf(fidlog,['\n']);
+    fprintf(fidlog,'\n');
     for j = 1:NumberOfBlocks
         fprintf(fidlog,['    Initial state of the Gaussian random number generator for chain number ',int2str(j),':\n']);
         for i=1:length(record.InitialSeeds(j).Normal)

matlab/estimation/print_table_prior.m

@@ -142,7 +142,7 @@ skipline(2)
 
 
 function format_string = build_format_string(PriorMode,PriorStandardDeviation,LowerBound,UpperBound)
-format_string = ['%s \t %6.4f \t'];
+format_string = '%s \t %6.4f \t';
 if isnan(PriorMode)
     format_string = [ format_string , ' %s \t'];
 else

matlab/estimation/prior_analysis.m

@@ -38,7 +38,7 @@ switch info
     nvar = length(ivar);
     oo_ = job(type,SampleSize,arg1,arg2,arg3,options_,M_,oo_,nvar,vartan);
   otherwise
-    error(['prior_analysis:: Check_prior_analysis_data gave a meaningless output!'])
+    error('prior_analysis:: Check_prior_analysis_data gave a meaningless output!')
 end
 
 

matlab/estimation/prior_posterior_statistics_core.m

@@ -352,7 +352,7 @@ for b=fpar:B
             stock_forcst_point(:,:,irun(7)) = yf1(maxlag+1:end,:)';
             if ~isequal(M_.H,0)
                 ME_shocks=zeros(length(varobs),horizon);
-                i_exo_var = setdiff([1:length(varobs)],find(diag(M_.H) == 0));
+                i_exo_var = setdiff(1:length(varobs),find(diag(M_.H) == 0));
                 nxs = length(i_exo_var);
                 chol_H = chol(M_.H(i_exo_var,i_exo_var));
                 if ~isempty(M_.H)

matlab/estimation/selec_posterior_draws.m

@@ -63,7 +63,7 @@ switch nargin
     end
     drawsize = drsize+npar*8/1048576;
   otherwise
-    error(['selec_posterior_draws:: Unexpected number of input arguments!'])
+    error('selec_posterior_draws:: Unexpected number of input arguments!')
 end
 
 MetropolisFolder = CheckPath('metropolis',M_.dname);

matlab/estimation/smc/dsmh.m

@@ -99,7 +99,7 @@ for l=1:npar
     name = get_the_name(l,TeX,M_,estim_params_,options_.varobs);
     str = sprintf('%s\n %s \t\t %5.4f \t\t %7.5f \t\t %5.4f', str, name, lb95_xparam(l), mean_xparam(l), ub95_xparam(l));
 end
-disp([str])
+disp(str)
 disp('')
 
 %% Plot parameters densities

matlab/evaluate_steady_state.m

@@ -53,7 +53,7 @@ if ~options_.bytecode && options_.block && options_.solve_algo == 5
 end
 
 if isoctave && options_.solve_algo == 11
-    error(['STEADY: you can''t use solve_algo = %u under Octave'],options_.solve_algo)
+    error('STEADY: you can''t use solve_algo = %u under Octave',options_.solve_algo)
 end
 
 % To ensure that the z and zx matrices constructed by repmat and passed to bytecode

matlab/hessian.m

@@ -78,7 +78,7 @@ hessian_mat = zeros(size(f0,1), n*n);
 for i=1:n
     if i > 1
         %fill symmetric part of Hessian based on previously computed results
-        k = [i:n:n*(i-1)];
+        k = i:n:n*(i-1);
         hessian_mat(:,(i-1)*n+1:(i-1)*n+i-1) = hessian_mat(:,k);
     end
     hessian_mat(:,(i-1)*n+i) = (f1(:,i)+f_1(:,i)-2*f0)./(h1(i)*h_1(i)); %formula 25.3.23

matlab/internals.m

@@ -130,7 +130,7 @@ if strcmpi(flag,'--load-mh-history') || strcmpi(flag,'--display-mh-history')
             disp([' o Acceptance ratio in the current chain is ' num2str(oar(b)*100,'%5.2f') '%']);
             disp([' o Initial value of the posterior kernel is: ' num2str(oo.InitialLogPost(b),'%10.5f')])
             disp([' o Last value of the posterior kernel is: ' num2str(o.LastLogPost(b),'%10.5f')])
-            disp([' o State of the chain:'])
+            disp(' o State of the chain:')
             skipline()
             d1 = num2str(transpose(oo.InitialParameters(b,:)),'%10.5f\n');
             d2 = num2str(transpose(o.LastParameters(b,:)),'%10.5f\n');

matlab/kalman/DsgeSmoother.m

@@ -699,7 +699,7 @@ if isfield(M_,'filter_initial_state') && ~isempty(M_.filter_initial_state)
             elseif ~options_.loglinear && ~options_.logged_steady_state
                 a(oo_.dr.restrict_columns(ii)) = eval(M_.filter_initial_state{state_indices(ii),2}) - oo_.dr.ys(state_indices(ii));
             else
-                error(['The steady state is logged. This should not happen. Please contact the developers'])
+                error('The steady state is logged. This should not happen. Please contact the developers')
             end
         end
     end

matlab/kalman/likelihood/kalman_filter_d.m

@@ -123,7 +123,7 @@ while rank(Z*Pinf*Z',diffuse_kalman_tol) && (t<=last)
 end
 
 if t>last
-    warning(['kalman_filter_d: There isn''t enough information to estimate the initial conditions of the nonstationary variables. The diffuse Kalman filter never left the diffuse stage.']);
+    warning('kalman_filter_d: There isn''t enough information to estimate the initial conditions of the nonstationary variables. The diffuse Kalman filter never left the diffuse stage.');
     dLIK = NaN;
     return
 end

matlab/kalman/likelihood/missing_observations_kalman_filter_d.m

@@ -134,7 +134,7 @@ while rank(Pinf,diffuse_kalman_tol) && (t<=last)
 end
 
 if t==(last+1)
-    warning(['kalman_filter_d: There isn''t enough information to estimate the initial conditions of the nonstationary variables. The diffuse Kalman filter never left the diffuse stage.']);
+    warning('kalman_filter_d: There isn''t enough information to estimate the initial conditions of the nonstationary variables. The diffuse Kalman filter never left the diffuse stage.');
     dLIK = NaN;
     return
 end

matlab/kalman/likelihood/univariate_kalman_filter_d.m

@@ -181,7 +181,7 @@ while newRank && (t<=last)
 end
 
 if (t>last)
-    warning(['univariate_diffuse_kalman_filter:: There isn''t enough information to estimate the initial conditions of the nonstationary variables']);
+    warning('univariate_diffuse_kalman_filter:: There isn''t enough information to estimate the initial conditions of the nonstationary variables');
     dLIK = NaN;
     return
 end

matlab/kalman/missing_DiffuseKalmanSmootherH3_Z.m

@@ -815,6 +815,6 @@ epsilonhat = Y - Z*alphahat;
 
 
 if (d==smpl)
-    warning(['missing_DiffuseKalmanSmootherH3_Z:: There isn''t enough information to estimate the initial conditions of the nonstationary variables']);
+    warning('missing_DiffuseKalmanSmootherH3_Z:: There isn''t enough information to estimate the initial conditions of the nonstationary variables');
     return
 end

matlab/latex/write_latex_parameter_table.m

@@ -48,7 +48,7 @@ if Long_names_present==1
 else
     fprintf(fid, '\\begin{longtable}{cc}\n');
 end
-fprintf(fid, ['\\caption{Parameter Values}\\\\%%\n']);
+fprintf(fid, '\\caption{Parameter Values}\\\\%%\n');
 
 fprintf(fid, '\\toprule%%\n');
 fprintf(fid, '\\multicolumn{1}{c}{\\textbf{Parameter}} &\n');

matlab/latex/write_latex_prior_table.m

@@ -175,7 +175,7 @@ fprintf(fidTeX,'%% End of TeX file.\n');
 fclose(fidTeX);
 
 function format_string = build_format_string(PriorMode,PriorStandardDeviation,LowerBound,UpperBound)
-format_string = ['%s & %s & %6.4f &'];
+format_string = '%s & %s & %6.4f &';
 if isnan(PriorMode)
     format_string = [ format_string , ' %s &'];
 else

matlab/lmmcp/dyn_lmmcp.m

@@ -35,12 +35,12 @@ iyf = find(lead_lag_incidence(3,:)>0) ;
 
 nd = nyp+ny0+nyf;
 nrc = nyf+1 ;
-isp = [1:nyp] ;
+isp = 1:nyp ;
-is = [nyp+1:ny+nyp] ;
+is = nyp+1:ny+nyp ;
 isf = iyf+nyp ;
-isf1 = [nyp+ny+1:nyf+nyp+ny+1] ;
+isf1 = nyp+ny+1:nyf+nyp+ny+1 ;
 stop = 0 ;
-iz = [1:ny+nyp+nyf];
+iz = 1:ny+nyp+nyf;
 
 periods = options.periods;
 steady_state = oo_.steady_state;

matlab/load_mat_file_data_legacy.m

@@ -34,7 +34,7 @@ else
         try
             data_mat=[data_mat vec(data_file.(varobs{var_iter}))];
         catch
-            error(['makedataset: The variable %s does not have dimensions conformable with the previous one'], varobs{var_iter});
+            error('makedataset: The variable %s does not have dimensions conformable with the previous one', varobs{var_iter});
         end
     end
 end

matlab/model_diagnostics.m

@@ -59,19 +59,19 @@ problem_dummy=0;
 %naming conflict in steady state file
 if options_.steadystate_flag == 1
     if strmatch('ys',M_.endo_names,'exact') 
-        disp(['MODEL_DIAGNOSTICS: using the name ys for an endogenous variable will typically conflict with the internal naming in user-defined steady state files.'])
+        disp('MODEL_DIAGNOSTICS: using the name ys for an endogenous variable will typically conflict with the internal naming in user-defined steady state files.')
         problem_dummy=1;
     end
     if strmatch('ys',M_.param_names,'exact')
-        disp(['MODEL_DIAGNOSTICS: using the name ys for a parameter will typically conflict with the internal naming in user-defined steady state files.'])
+        disp('MODEL_DIAGNOSTICS: using the name ys for a parameter will typically conflict with the internal naming in user-defined steady state files.')
         problem_dummy=1;
     end
     if strmatch('M_',M_.endo_names,'exact') 
-        disp(['MODEL_DIAGNOSTICS: using the name M_ for an endogenous variable will typically conflict with the internal naming in user-defined steady state files.'])
+        disp('MODEL_DIAGNOSTICS: using the name M_ for an endogenous variable will typically conflict with the internal naming in user-defined steady state files.')
         problem_dummy=1;
     end
     if strmatch('M_',M_.param_names,'exact')
-        disp(['MODEL_DIAGNOSTICS: using the name M_ for a parameter will typically conflict with the internal naming in user-defined steady state files.'])
+        disp('MODEL_DIAGNOSTICS: using the name M_ for a parameter will typically conflict with the internal naming in user-defined steady state files.')
         problem_dummy=1;
     end
 end
@@ -124,10 +124,10 @@ if check1(1)
     problem_dummy=1;
     disp('MODEL_DIAGNOSTICS: The steady state cannot be computed')
     if any(isnan(dr.ys))
-        disp(['MODEL_DIAGNOSTICS: Steady state contains NaNs'])
+        disp('MODEL_DIAGNOSTICS: Steady state contains NaNs')
     end
     if any(isinf(dr.ys))
-        disp(['MODEL_DIAGNOSTICS: Steady state contains Inf'])
+        disp('MODEL_DIAGNOSTICS: Steady state contains Inf')
     end
     return
 end

matlab/moments/UnivariateSpectralDensity.m

@@ -58,7 +58,7 @@ ivar=zeros(nvar,1);
 for i=1:nvar
     i_tmp = strmatch(var_list{i}, M_.endo_names, 'exact');
     if isempty(i_tmp)
-        error (['One of the variables specified does not exist']) ;
+        error ('One of the variables specified does not exist') ;
     else
         ivar(i) = i_tmp;
     end
@@ -70,9 +70,9 @@ nspred = M_.nspred;
 nstatic = M_.nstatic;
 kstate = oo_.dr.kstate;
 order = oo_.dr.order_var;
-iv(order) = [1:length(order)];
+iv(order) = 1:length(order);
 nx = size(ghx,2);
-ikx = [nstatic+1:nstatic+nspred];
+ikx = nstatic+1:nstatic+nspred;
 k0 = kstate(find(kstate(:,2) <= M_.maximum_lag+1),:);
 i0 = find(k0(:,2) == M_.maximum_lag+1);
 i00 = i0;

matlab/moments/compute_moments_varendo.m

@@ -302,6 +302,6 @@ if options_.order==1
         end
     end
 else
-    fprintf(['Estimation::compute_moments_varendo: (conditional) variance decomposition only available at order=1. Skipping computations\n'])
+    fprintf('Estimation::compute_moments_varendo: (conditional) variance decomposition only available at order=1. Skipping computations\n')
 end
 fprintf('Done!\n\n');

matlab/moments/disp_moments.m

@@ -58,7 +58,7 @@ if ~all(M_.H==0)
     end
     if ~isempty(observable_pos_requested_vars)
         ME_present=1;
-        i_ME = setdiff([1:size(M_.H,1)],find(diag(M_.H) == 0)); % find ME with 0 variance
+        i_ME = setdiff(1:size(M_.H,1),find(diag(M_.H) == 0)); % find ME with 0 variance
         chol_S = chol(M_.H(i_ME,i_ME)); %decompose rest
         shock_mat=zeros(options_.periods,size(M_.H,1)); %initialize
         shock_mat(:,i_ME)=randn(length(i_ME),options_.periods)'*chol_S;
@@ -173,7 +173,7 @@ if ~options_.nodecomposition
             end
         end
         %back out shock matrix used for generating y
-        i_exo_var = setdiff([1:M_.exo_nbr],find(diag(M_.Sigma_e) == 0)); % find shocks with 0 variance
+        i_exo_var = setdiff(1:M_.exo_nbr,find(diag(M_.Sigma_e) == 0)); % find shocks with 0 variance
         chol_S = chol(M_.Sigma_e(i_exo_var,i_exo_var)); %decompose rest
         shock_mat=zeros(options_.periods,M_.exo_nbr); %initialize
         shock_mat(:,i_exo_var)=oo_.exo_simul(:,i_exo_var)/chol_S; %invert construction of oo_.exo_simul from simult.m

matlab/moments/disp_th_moments_pruned_state_space.m

@@ -43,7 +43,7 @@ function oo_=disp_th_moments_pruned_state_space(dr,M_,options_,i_var,oo_)
 
 
 if options_.one_sided_hp_filter || options_.hp_filter || options_.bandpass.indicator
-    error(['disp_th_moments:: theoretical moments incompatible with filtering. Use simulated moments instead'])
+    error('disp_th_moments:: theoretical moments incompatible with filtering. Use simulated moments instead')
 end
 
 nvars=length(i_var);

matlab/ms-sbvar/ms_mardd.m

@@ -121,7 +121,7 @@ vlog_a0_Yao = zeros(nvar,1);
 vlog=zeros(ndraws2,1);
 for k=1:nvar
     bk = Uiconst{k}'*A0xhat(:,k);
-    indx_ks=[k:nvar];  % the columns that exclude 1-(k-1)th columns
+    indx_ks=k:nvar;  % the columns that exclude 1-(k-1)th columns
     A0gbs0 = A0hat;   % starting at some point such as the peak
     nk = n0(k);
 

matlab/ms-sbvar/ms_write_markov_file.m

@@ -64,7 +64,7 @@ for i_chain = 1:n_chains
     %//== column of the transition matrix.  Each element must be positive.  For each column,
     %//== the relative size of the prior elements determine the relative size of the elements
     %//== of the transition matrix and overall larger sizes implies a tighter prior.
-    fprintf(fh,['//== Transition matrix prior for state_variable[%d] ==//\n'], ...
+    fprintf(fh,'//== Transition matrix prior for state_variable[%d] ==//\n', ...
             i_chain);
     Alpha = ones(n_states,n_states);
     for i_state = 1:n_states

matlab/ms-sbvar/msstart2.m

@@ -289,7 +289,7 @@ if options_.ms.indxestima
             % - 0.01 (or any number < 1)  is used so that qmStart+options_.ms.nlags -options_.ms.dummy_obs ==-?*options_.ms.freq give us an extra year back.
         end
     end
-    dateswd = fn_dataext([yrStartEsti qmStartEsti],[yrEnd qmEnd],xdatae(:,[1:2]));  % dates with dummies
+    dateswd = fn_dataext([yrStartEsti qmStartEsti],[yrEnd qmEnd],xdatae(:,1:2));  % dates with dummies
     phie = [dateswd phi];
     ye = [dateswd y];
 
@@ -484,8 +484,8 @@ if options_.ms.indxestima
     % only actual growth rates
     yafyrghate
     if options_.ms.indxgforhat
-        keyindx = [1:nvar];
+        keyindx = 1:nvar;
-        conlab=['unconditional'];
+        conlab='unconditional';
 
         figure
         yafyrghate(:,3:end) = yafyrghate(:,3:end)/100;
@@ -680,9 +680,9 @@ if options_.ms.indxestima
     [yacyrghate,yacyrhate,yacqmyghate] = fn_datana(yachate,options_.ms.freq,options_.ms.log_var(1:nlogeno),options_.ms.percent_var(1:npereno));
     % actual and conditional forecast growth rates
     if options_.ms.indxgdls && nconstr
-        keyindx = [1:nvar];
+        keyindx = 1:nvar;
         %  conlab=['conditional on' ylab{PorR(1)}];
-        conlab=['v-conditions'];
+        conlab='v-conditions';
 
         figure
         fn_foregraph(yafyrghate,yact2yrge,keyindx,rnum,cnum,options_.ms.freq,ylab,forelabel,conlab)
@@ -713,9 +713,9 @@ if options_.ms.indxestima
         disp([sprintf('%4.0f %2.0f %8.3f %8.3f %8.3f %8.3f %8.3f %8.3f\n',yacEyrghate')])
 
         if 1
-            keyindx = [1:nvar];
+            keyindx = 1:nvar;
             %  conlab=['conditional on' ylab{PorR(1)}];
-            conlab=['shock-conditions'];
+            conlab='shock-conditions';
 
             figure
             gyrfore(yacEyrghate,yact2yrge,keyindx,rnum,cnum,ylab,forelabel,conlab)
@@ -733,7 +733,7 @@ if options_.ms.indxestima
         qmStartWod = options_.ms.freq;
     end
     yrStartWod = yrStart + floor((qmStart+options_.ms.nlags -1)/options_.ms.freq);
-    dateswod = fn_dataext([yrStartWod qmStartWod],[yrEnd qmEnd],xdatae(:,[1:2]));
+    dateswod = fn_dataext([yrStartWod qmStartWod],[yrEnd qmEnd],xdatae(:,1:2));
     eplhate = [dateswod eplhat];
 
     Aphat = Fhat;

matlab/ms-sbvar/msstart_setup.m

@@ -48,7 +48,7 @@ end
 %1    CBO output gap --  log(x_t)-log(x_t potential)
 %2    GDP deflator -- (P_t/P_{t-1})^4-1.0
 %2    FFR/100.
-options_.ms.vlist = [1:length(options_.varobs)];    % 1: U; 4: PCE inflation.
+options_.ms.vlist = 1:length(options_.varobs);    % 1: U; 4: PCE inflation.
 options_.ms.varlist=cellstr(options_.varobs');
 options_.ms.log_var = sort(varlist_indices(options_.ms.vlistlog,char(options_.varobs)));   % subset of "options_.ms.vlist.  Variables in log level so that differences are in **monthly** growth, unlike R and U which are in annual percent (divided by 100 already).
 options_.ms.percent_var =setdiff(options_.ms.vlist,options_.ms.log_var);

matlab/ms-sbvar/plot_ms_variance_decomposition.m

@@ -83,7 +83,7 @@ for i=1:nvars
     series_names{i} = endo_names{i};
 end
 
-x = [1:T];
+x = 1:T;
 plot_dates = 0;
 data = 0;
 steady = 0;

matlab/name2index.m

@@ -80,7 +80,7 @@ if strcmpi(type,'StructuralShock')
                 end
             end
         catch
-            disp(['Off diagonal terms of the covariance matrix are not estimated (state equation)'])
+            disp('Off diagonal terms of the covariance matrix are not estimated (state equation)')
         end
     end
 end

matlab/optimal_policy/evaluate_planner_objective.m

@@ -309,7 +309,7 @@ else
         ysteady = [ys(oo_.dr.order_var); U/(1-beta)];
 
         % Generates the sequence of shocks to compute unconditional welfare
-        i_exo_var = setdiff([1:M_.exo_nbr],find(diag(M_.Sigma_e) == 0));
+        i_exo_var = setdiff(1:M_.exo_nbr,find(diag(M_.Sigma_e) == 0));
         nxs = length(i_exo_var);
         chol_S = chol(M_.Sigma_e(i_exo_var,i_exo_var));
         exo_simul = zeros(M_.exo_nbr,options_.ramsey.periods);
@@ -397,7 +397,7 @@ if ~isempty(M_.det_shocks)
     end
     shock_indices=find(periods==1);
     if any(cellfun(@(x) ~strcmp(x, 'level'), { M_.det_shocks(shock_indices).type }))
-        fprintf(['\nevaluate_planner_objective: Shock values need to be specified in level.\n'])
+        fprintf('\nevaluate_planner_objective: Shock values need to be specified in level.\n')
     end
     u([M_.det_shocks(shock_indices).exo_id])=[M_.det_shocks(shock_indices).value];
 else

matlab/optimization/cmaes.m

@@ -298,7 +298,7 @@ input.fitfun = fitfun; % record used input
 if isempty(fitfun)
     % fitfun = definput.fitfun;
     % warning(['Objective function not determined, ''' fitfun ''' used']);
-    error(['Objective function not determined']);
+    error('Objective function not determined');
 end
 if ~ischar(fitfun)
     error('first argument FUN must be a string');
@@ -328,7 +328,7 @@ if isempty(insigma)
     if all(size(myeval(xstart)) > 1)
         insigma = std(xstart, 0, 2);
         if any(insigma == 0)
-            error(['Initial search volume is zero, choose SIGMA or X0 appropriate']);
+            error('Initial search volume is zero, choose SIGMA or X0 appropriate');
         end
     else
         % will be captured later
@@ -510,7 +510,7 @@ while irun <= myeval(opts.Restarts) % for-loop does not work with resume
                 stopTolX = myeval(opts.TolX);  % reevaluate these
                 stopTolUpX = myeval(opts.TolUpX);
             else
-                error(['Initial step sizes (SIGMA) not determined']);
+                error('Initial step sizes (SIGMA) not determined');
             end
         end
 
@@ -543,10 +543,10 @@ while irun <= myeval(opts.Restarts) % for-loop does not work with resume
 
         % Initialize dynamic internal state parameters
         if any(insigma <= 0)
-            error(['Initial search volume (SIGMA) must be greater than zero']);
+            error('Initial search volume (SIGMA) must be greater than zero');
         end
         if max(insigma)/min(insigma) > 1e6
-            error(['Initial search volume (SIGMA) badly conditioned']);
+            error('Initial search volume (SIGMA) badly conditioned');
         end
         sigma = max(insigma);              % overall standard deviation
         pc = zeros(N,1); ps = zeros(N,1);  % evolution paths for C and sigma

matlab/optimization/mr_gstep.m

@@ -148,7 +148,7 @@ while i<n
                 x = check_bounds(x,bounds);
             end
             if Verbose
-                fprintf(['Done for param %s = %8.4f; f = %8.4f\n'],parameter_names{i},x(i),f0)
+                fprintf('Done for param %s = %8.4f; f = %8.4f\n',parameter_names{i},x(i),f0)
             end
         end
         xh1=x;

matlab/optimization/mr_hessian.m

@@ -219,7 +219,7 @@ if outer_product_gradient
         hessian_mat = zeros(size(f0,1),n*n);
         for i=1:n
             if i > 1
-                k=[i:n:n*(i-1)];
+                k=i:n:n*(i-1);
                 hessian_mat(:,(i-1)*n+1:(i-1)*n+i-1)=hessian_mat(:,k);
             end
             hessian_mat(:,(i-1)*n+i)=(f1(:,i)+f_1(:,i)-2*f0)./(hess_info.h1(i)*h_1(i));

matlab/optimization/newrat.m

@@ -149,7 +149,7 @@ while norm(gg)>gtol && check==0 && jit<nit
     tic1 = tic;
     icount=icount+1;
     penalty = fval0(icount);
-    disp_verbose([' '],Verbose)
+    disp_verbose(' ',Verbose)
     disp_verbose(['Iteration ',num2str(icount)],Verbose)
     [fval,x0] = csminit1(func0,xparam1,penalty,fval0(icount),gg,0,H,Verbose,varargin{:});
     if igrad
@@ -335,11 +335,11 @@ if jit==nit
 end
 
 if norm(gg)<=gtol
-    disp_verbose(['Estimation ended:'],Verbose)
+    disp_verbose('Estimation ended:',Verbose)
     disp_verbose(['Gradient norm < ', num2str(gtol)],Verbose)
 end
 if check==1
-    disp_verbose(['Estimation successful.'],Verbose)
+    disp_verbose('Estimation successful.',Verbose)
 end
 
 return

matlab/optimization/simplex_optimization_routine.m

@@ -394,7 +394,7 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
             end
         else
             if isfinite(fv(1))
-                dprintf(['%s  %s  %12.7E                                 %12.7E  %s'], iter_, fval_, fv(1) , critX, move)
+                dprintf('%s  %s  %12.7E                                 %12.7E  %s', iter_, fval_, fv(1) , critX, move)
             else
                 dprintf('%s  %s                                         %12.7E  %s', iter_, fval_, critX, move)
             end

matlab/optimization/solve_one_boundary.m

@@ -125,7 +125,7 @@ for it_=start:incr:finish
                                 correcting_factor=correcting_factor*4;
                                 if verbose
                                     disp(['The Jacobian matrix is singular, det(Jacobian)=' num2str(detJ,'%f') '.'])
-                                    disp(['    trying to correct the Jacobian matrix:'])
+                                    disp('    trying to correct the Jacobian matrix:')
                                     disp(['    correcting_factor=' num2str(correcting_factor,'%f') ' max(Jacobian)=' num2str(full(max_factor),'%f')])
                                 end
                                 dx = - r/(g1+correcting_factor*speye(Blck_size));

matlab/optimization/solvopt.m

@@ -761,7 +761,7 @@ while 1
             nsteps(2:kg)=nsteps(1:kg-1);
         end
         nsteps(1)=dx/(abs(h)*norm(g0));
-        kk=sum(nsteps(1:kg).*[kg:-1:1])/sum([kg:-1:1]);
+        kk=sum(nsteps(1:kg).*[kg:-1:1])/sum(kg:-1:1);
         if     kk>des
             if kg==1
                 h=h*(kk-des+1);

matlab/parallel/AnalyseComputationalEnvironment.m

@@ -315,7 +315,7 @@ for Node=1:length(DataInput) % To obtain a recursive function remove the 'for'
         % Build a command file to test the matlab execution and dynare path ...
 
         fid = fopen('Tracing.m', 'w+');
-        s1=(['fT = fopen(''MatlabOctaveIsOk.txt'',''w+'');\n']);
+        s1=('fT = fopen(''MatlabOctaveIsOk.txt'',''w+'');\n');
         s2='fclose(fT);\n';
         SBS=strfind(DataInput(Node).DynarePath,'\');
         DPStr=DataInput(Node).DynarePath;
@@ -329,17 +329,17 @@ for Node=1:length(DataInput) % To obtain a recursive function remove the 'for'
             DPStrNew=[DPStrNew,DPStr(SBS(end)+1:end)];
         end
         s3=['addpath(''',DPStrNew,'''),\n'];
-        s4=['try,\n  dynareroot = dynare_config();\n'];
+        s4='try,\n  dynareroot = dynare_config();\n';
-        s41=(['  fT = fopen(''DynareIsOk.txt'',''w+'');\n']);
+        s41=('  fT = fopen(''DynareIsOk.txt'',''w+'');\n');
         s42='  fclose(fT);\n';
-        s5=['catch,\n'];
+        s5='catch,\n';
-        s51=(['  fT = fopen(''DynareFailed.txt'',''w+'');\n']);
+        s51=('  fT = fopen(''DynareFailed.txt'',''w+'');\n');
         s52='  fclose(fT);\n';
-        s6=['end,\n'];
+        s6='end,\n';
-        s7=['if ismac,\n'];
+        s7='if ismac,\n';
-        s71=(['  fT = fopen(''IsMac.txt'',''w+'');\n']);
+        s71=('  fT = fopen(''IsMac.txt'',''w+'');\n');
         s72='  fclose(fT);\n';
-        s8=['end,\n'];
+        s8='end,\n';
         send='exit';
         StrCommand=([s1,s2,s3,s4,s41,s42,s5,s51,s52,s6,s7,s71,s72,s8,send]);
 
@@ -530,7 +530,7 @@ for Node=1:length(DataInput) % To obtain a recursive function remove the 'for'
                 Environment1 = 2;
             end
         else
-            command_string = ['psinfo \\'];
+            command_string = 'psinfo \\';
             [si0, de0] = system(command_string);
         end
     else

matlab/parallel/InitializeComputationalEnvironment.m

@@ -76,7 +76,7 @@ end
 % parallel computations with Strategy == 1 delete the traces (if exists) of
 % previous computations.
 
-delete(['P_slave_*End.txt']);
+delete('P_slave_*End.txt');
 masterParallel(options_.parallel,[],[],[],[],[],[],options_.parallel_info,1);
 
 

matlab/parallel/closeSlave.m

@@ -68,7 +68,7 @@ for indPC=1:length(Parallel)
     delete( 'slaveParallel_input*.mat');
     delete( 'slaveJob*.mat');
     pause(1)
-    delete(['slaveParallel_*.log']);
+    delete('slaveParallel_*.log');
     delete ConcurrentCommand1.bat;
 
 end

matlab/parallel/distributeJobs.m

@@ -53,8 +53,8 @@ for j=1:lP
         skipline()
         disp(['PARALLEL_ERROR:: NumberOfThreadsPerJob = ',int2str(Parallel(j).NumberOfThreadsPerJob),' is not an exact divisor of number of CPUs = ',int2str(length(Parallel(j).CPUnbr)),'!'])
         disp(['                 You must re-set properly NumberOfThreadsPerJob of node ' int2str(j) ' ' Parallel(j).ComputerName])
-        disp(['                 in your configuration file'])
+        disp('                 in your configuration file')
-        error(['PARALLEL_ERROR:: NumberOfThreadsPerJob is not an exact divisor of CPUnbr'])
+        error('PARALLEL_ERROR:: NumberOfThreadsPerJob is not an exact divisor of CPUnbr')
     end
     nCPU(j)=length(Parallel(j).CPUnbr)/Parallel(j).NumberOfThreadsPerJob;
     totCPU=totCPU+nCPU(j);

matlab/parallel/dynareParallelDeleteNewFiles.m

@@ -49,9 +49,9 @@ for indPC=1:length(Parallel)
 
             for i=1:length(NewFilesFromSlaves)
                 SlashNumberAndPosition=[];
-                PRCDirPosition=findstr(NewFilesFromSlaves{i}, ([PRCDir]));
+                PRCDirPosition=findstr(NewFilesFromSlaves{i}, (PRCDir));
                 sT=NewFilesFromSlaves{i};
-                sT(1:(PRCDirPosition+length([PRCDir]))-2)=[];
+                sT(1:(PRCDirPosition+length(PRCDir))-2)=[];
                 sT(1)='.';
                 SlashNumberAndPosition=findstr(sT,fS);
                 fileaddress={sT(1:SlashNumberAndPosition(end)),sT(SlashNumberAndPosition(end)+1:end)};

matlab/parallel/dynareParallelGetFiles.m

@@ -48,7 +48,7 @@ for indPC=1:length(Parallel)
             end
             if ischar(NamFileInput0)
                 for j=1:size(NamFileInput0,1)
-                    NamFile(j,:)={['./'],deblank(NamFileInput0(j,:))};
+                    NamFile(j,:)={'./',deblank(NamFileInput0(j,:))};
                 end
                 NamFileInput = NamFile;
             end
@@ -94,7 +94,7 @@ for indPC=1:length(Parallel)
         else
             if ischar(NamFileInput0)
                 for j=1:size(NamFileInput0,1)
-                    NamFile(j,:)={['.\'],deblank(NamFileInput0(j,:))};
+                    NamFile(j,:)={'.\',deblank(NamFileInput0(j,:))};
                 end
                 NamFileInput = NamFile;
             end

matlab/parallel/dynareParallelGetNewFiles.m

@@ -51,9 +51,9 @@ for indPC=1:length(Parallel)
 
             for i=1:length(NewFilesFromSlaves)
                 SlashNumberAndPosition=[];
-                PRCDirPosition=findstr(NewFilesFromSlaves{i}, ([PRCDir]));
+                PRCDirPosition=findstr(NewFilesFromSlaves{i}, (PRCDir));
                 sT=NewFilesFromSlaves{i};
-                sT(1:(PRCDirPosition+length([PRCDir]))-2)=[];
+                sT(1:(PRCDirPosition+length(PRCDir))-2)=[];
                 sT(1)='.';
                 SlashNumberAndPosition=findstr(sT,fS);
                 fileaddress={sT(1:SlashNumberAndPosition(end)),sT(SlashNumberAndPosition(end)+1:end)};

matlab/parallel/fMessageStatus.m

@@ -43,7 +43,7 @@ catch
 end
 
 fslave = dir( ['slaveParallel_input',int2str(njob),'.mat']);
-fbreak = dir( ['slaveParallel_break.mat']);
+fbreak = dir( 'slaveParallel_break.mat');
 if isempty(fslave) || ~isempty(fbreak)
     error('Master asked to break the job');
 end

matlab/parallel/masterParallel.m

@@ -102,7 +102,7 @@ if nargin>8 && initialize==1
         evalin('base','clear PRCDirTmp,')
     else
         % Delete the traces (if existing) of last local session of computations.
-        mydelete(['slaveParallel_input*.mat']);
+        mydelete('slaveParallel_input*.mat');
     end
     return
 end
@@ -183,11 +183,11 @@ if parallel_recover ==0
 
       case 1
         if exist('fGlobalVar','var')
-            save(['temp_input.mat'],'fInputVar','fGlobalVar')
+            save('temp_input.mat','fInputVar','fGlobalVar')
         else
-            save(['temp_input.mat'],'fInputVar')
+            save('temp_input.mat','fInputVar')
         end
-        save(['temp_input.mat'],'Parallel','-append')
+        save('temp_input.mat','Parallel','-append')
         closeSlave(Parallel,PRCDir,-1);
     end
 
@@ -608,7 +608,7 @@ if parallel_recover ==0
             hstatus(j) = axes('position',[0.05/ncol+(jcol-1)/ncol vstart-vspace*(jrow-1) 0.9/ncol 0.3*vspace], ...
                               'box','on','xtick',[],'ytick',[],'xlim',[0 1],'ylim',[0 1]);
             hpat(j) = patch([0 0 0 0],[0 1 1 0],'r','EdgeColor','r');
-            htit(j) = title(['Initialize ...']);
+            htit(j) = title('Initialize ...');
 
         end
 
@@ -902,13 +902,13 @@ switch Strategy
         catch
         end
 
-        mydelete(['*_core*_input*.mat']);
+        mydelete('*_core*_input*.mat');
 
     end
 
     delete ConcurrentCommand1.bat
   case 1
-    delete(['temp_input.mat'])
+    delete('temp_input.mat')
     if newInstance
         if isempty(dir('dynareParallelLogFiles'))
             rmdir('dynareParallelLogFiles');

matlab/partial_information/dr1_PI.m

@@ -194,7 +194,7 @@ if(options_.ACES_solver)
             for i=1:num_inst
                 i_tmp = strmatch(deblank(lq_instruments.names(i,:)), M_.endo_names,'exact');
                 if isempty(i_tmp)
-                    error (['One of the specified instrument variables does not exist']) ;
+                    error ('One of the specified instrument variables does not exist') ;
                 else
                     i_var(i) = i_tmp;
                 end
@@ -303,7 +303,7 @@ if ( M_.maximum_lag <= 1) && (M_.maximum_lead <= 1)
         if num_exp_0>0
             AA3(:,exp_0)=AA1(:,exp_0);
             XX0=zeros(nendo,num_exp_0);
-            AA1(:,exp_0)=XX0(:,[1:num_exp_0])
+            AA1(:,exp_0)=XX0(:,1:num_exp_0)
         end
     end
 end

matlab/perfect-foresight-models/basic_plan.m

@@ -32,7 +32,7 @@ function plan = basic_plan(plan, exogenous, expectation_type, date, value)
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 
 if ~ischar(expectation_type) || size(expectation_type,1) ~= 1
-    error(['in basic_plan the third argument should be a string containing the simulation type (''perfect_foresight'' or ''surprise'')']);
+    error('in basic_plan the third argument should be a string containing the simulation type (''perfect_foresight'' or ''surprise'')');
 end
 exogenous = strtrim(exogenous);
 ix = find(strcmp(exogenous, plan.exo_names));

matlab/perfect-foresight-models/flip_plan.m

@@ -32,7 +32,7 @@ function plan = flip_plan(plan, exogenous, endogenous, expectation_type, date, v
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <https://www.gnu.org/licenses/>.
 if ~ischar(expectation_type) || size(expectation_type,1) ~= 1
-    error(['in flip_plan the fourth argument should be a string containing the simulation type (''perfect_foresight'' or ''surprise'')']);
+    error('in flip_plan the fourth argument should be a string containing the simulation type (''perfect_foresight'' or ''surprise'')');
 end
 exogenous = strtrim(exogenous);
 ix = find(strcmp(exogenous, plan.endo_names));

matlab/perfect-foresight-models/solve_two_boundaries_stacked.m

@@ -118,7 +118,7 @@ while ~(cvg || iter > options_.simul.maxit)
                             correcting_factor=correcting_factor*4;
                             if verbose
                                 disp(['The Jacobian matrix is singular, det(Jacobian)=' num2str(detJ,'%f') '.']);
-                                disp(['    trying to correct the Jacobian matrix:']);
+                                disp('    trying to correct the Jacobian matrix:');
                                 disp(['    correcting_factor=' num2str(correcting_factor,'%f') ' max(Jacobian)=' num2str(full(max_factor),'%f')]);
                             end
                             dx = (g1aa+correcting_factor*speye(periods*Blck_size))\ba- ya_save;

matlab/reporting/@report_graph/writeGraphFile.m

@@ -97,7 +97,7 @@ elseif iscell(o.xTickLabels)
     x = o.xTicks;
     xTickLabels = o.xTickLabels;
 else
-    x = [1:dd.ndat];
+    x = 1:dd.ndat;
     xTickLabels = strings(dd);
 end
 fprintf(fid, 'xticklabels={');

matlab/shock_decomposition/WriteShockDecomp2Excel.m

@@ -64,7 +64,7 @@ if nargin==8
     if isfield(opts_decomp,'fig_name')
         fig_name = opts_decomp.fig_name;
         %         fig_name = ['_' fig_name];
-        fig_name1 = [fig_name];
+        fig_name1 = fig_name;
         fig_name = [fig_name '_'];
     end
     if screen_shocks

matlab/shock_decomposition/graph_decomp_detail.m

@@ -269,7 +269,7 @@ for j=1:nvar
         if nfigs>1
             suffix = ['_detail_' int2str(jf)];
         else
-            suffix = ['_detail'];
+            suffix = '_detail';
         end
         if ~options_.plot_shock_decomp.expand
             dyn_saveas(fhandle,[GraphDirectoryName, filesep, M_.fname, ...

matlab/shock_decomposition/plot_shock_decomposition.m

@@ -605,7 +605,7 @@ for i=1:ngroups
 end
 zothers = sum(z(:,1:nshocks,:),2);
 shock_groups.(['group' int2str(ngroups+1)]).label =  'Others';
-shock_groups.(['group' int2str(ngroups+1)]).shocks =  cellstr(M_.exo_names(find(~ismember([1:M_.exo_nbr],kcum)),:))';
+shock_groups.(['group' int2str(ngroups+1)]).shocks =  cellstr(M_.exo_names(find(~ismember(1:M_.exo_nbr,kcum)),:))';
 M_.shock_groups.(options_.plot_shock_decomp.use_shock_groups)=shock_groups;
 if any(any(zothers))
     shock_names = [shock_names; {'Others + Initial Values'}];

matlab/shock_decomposition/realtime_shock_decomposition.m

@@ -129,7 +129,7 @@ else
     running_text = 'Fast realtime shock decomposition ';
 end
 newString=sprintf(running_text);
-fprintf(['%s'],newString);
+fprintf('%s',newString);
 
 for j=presample+1:nobs
     %    evalin('base',['options_.nobs=' int2str(j) ';'])

matlab/stochastic_solver/irf.m

@@ -60,7 +60,7 @@ if local_order == 1
     y = y2(:,M_.maximum_lag+1:end)-y1;
 else
     % eliminate shocks with 0 variance
-    i_exo_var = setdiff([1:M_.exo_nbr],find(diag(M_.Sigma_e) == 0 ));
+    i_exo_var = setdiff(1:M_.exo_nbr,find(diag(M_.Sigma_e) == 0 ));
     nxs = length(i_exo_var);
     ex1 = zeros(long+drop,M_.exo_nbr);
     chol_S = chol(M_.Sigma_e(i_exo_var,i_exo_var));

matlab/stochastic_solver/simult.m

@@ -72,7 +72,7 @@ if replic > 1
 end
 
 % eliminate shocks with 0 variance
-i_exo_var = setdiff([1:M_.exo_nbr],find(diag(M_.Sigma_e) == 0));
+i_exo_var = setdiff(1:M_.exo_nbr,find(diag(M_.Sigma_e) == 0));
 nxs = length(i_exo_var);
 exo_simul = zeros(options_.periods,M_.exo_nbr);
 chol_S = chol(M_.Sigma_e(i_exo_var,i_exo_var));

matlab/stochastic_solver/simultxdet.m

@@ -53,7 +53,7 @@ end
 nx = size(dr.ghu,2);
 y_ = zeros(size(y0,1),iter+ykmin);
 y_(:,1:ykmin) = y0;
-k1 = [ykmin:-1:1];
+k1 = ykmin:-1:1;
 k2 = dr.kstate(find(dr.kstate(:,2) <= ykmin+1),[1 2]);
 k2 = k2(:,1)+(ykmin+1-k2(:,2))*endo_nbr;
 k3 = M_.lead_lag_incidence(1:ykmin,:)';
@@ -64,14 +64,14 @@ k4 = k4(:,1)+(ykmin+1-k4(:,2))*endo_nbr;
 nvar = length(var_list);
 if nvar == 0
     nvar = endo_nbr;
-    ivar = [1:nvar];
+    ivar = 1:nvar;
 else
     ivar=zeros(nvar,1);
     for i=1:nvar
         i_tmp = strmatch(var_list{i}, M_.endo_names, 'exact');
         if isempty(i_tmp)
             disp(var_list{i})
-            error (['One of the variable specified does not exist']) ;
+            error ('One of the variable specified does not exist') ;
         else
             ivar(i) = i_tmp;
         end

matlab/stochastic_solver/stochastic_solvers.m

@@ -73,7 +73,7 @@ if M_.maximum_endo_lag == 0
         fprintf('\nSTOCHASTIC_SOLVER: Dynare does not solve purely forward models with var_exo_det.\n')
         fprintf('STOCHASTIC_SOLVER: To circumvent this restriction, you can add a backward-looking dummy equation of the form:\n')
         fprintf('STOCHASTIC_SOLVER: junk=0.9*junk(-1);\n')
-        error(['var_exo_det not implemented for purely forward models'])
+        error('var_exo_det not implemented for purely forward models')
     end
 end
 
@@ -81,7 +81,7 @@ if M_.maximum_endo_lead==0 && M_.exo_det_nbr~=0
     fprintf('\nSTOCHASTIC_SOLVER: Dynare does not solve purely backward models with var_exo_det.\n')
     fprintf('STOCHASTIC_SOLVER: To circumvent this restriction, you can add a foward-looking dummy equation of the form:\n')
     fprintf('STOCHASTIC_SOLVER: junk=0.9*junk(+1);\n')
-    error(['var_exo_det not implemented for purely backwards models'])
+    error('var_exo_det not implemented for purely backwards models')
 end
 
 if options_.k_order_solver
@@ -261,7 +261,7 @@ else
         k1 = nonzeros(M_.lead_lag_incidence(:,order_var)');
         kk = [k1; length(k1)+(1:M_.exo_nbr+M_.exo_det_nbr)'];
         nk = size(kk,1);
-        kk1 = reshape([1:nk^2],nk,nk);
+        kk1 = reshape(1:nk^2,nk,nk);
         kk1 = kk1(kk,kk);
         hessian1 = hessian1(:,kk1(:));
     end
@@ -295,7 +295,7 @@ if M_.exo_det_nbr > 0
         zud=[zeros(nspred,M_.exo_det_nbr);dr.ghud{1};gx(:,1:nspred)*hud;zeros(M_.exo_nbr,M_.exo_det_nbr);eye(M_.exo_det_nbr)];
         R1 = hessian1*kron(zx,zud);
         dr.ghxud = cell(M_.exo_det_length,1);
-        kf = [M_.endo_nbr-nfwrd-nboth+1:M_.endo_nbr];
+        kf = M_.endo_nbr-nfwrd-nboth+1:M_.endo_nbr;
         kp = nstatic+[1:nspred];
         dr.ghxud{1} = -M1*(R1+f1*dr.ghxx(kf,:)*kron(dr.ghx(kp,:),dr.ghud{1}(kp,:)));
         Eud = eye(M_.exo_det_nbr);

matlab/test_for_deep_parameters_calibration.m

@@ -32,7 +32,7 @@ function info=test_for_deep_parameters_calibration(M_)
 plist = list_of_parameters_calibrated_as_NaN(M_);
 if ~isempty(plist)
     info=1;
-    message = ['Some of the parameters have no value (' ];
+    message = 'Some of the parameters have no value (' ;
     for i=1:length(plist)
         if i<length(plist)
             message = [message, plist{i} ', '];

matlab/utilities/tests

@@ -1 +1 @@
-Subproject commit a6b97581c379bde8e5022e230075c18421a8094a
+Subproject commit 3362b3f43053a2bb5d3d3a1dbfb1a9e0c42a8fb8