Consider options_.varobs as a cell of strings.

matlab/DsgeSmoother.m

@@ -59,7 +59,7 @@ R             = [];
 P             = [];
 PK            = [];
 decomp        = [];
-nobs            = size(options_.varobs,1);
+nobs            = length(options_.varobs);
 smpl          = size(Y,2);
 
 M_ = set_all_parameters(xparam1,estim_params_,M_);

matlab/GetPosteriorParametersStatistics.m

@@ -174,17 +174,17 @@ if nvn
             Draws = GetAllPosteriorDraws(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
             [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
                 posterior_moments(Draws,1,options_.mh_conf_sig);
-            name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:));
+            name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
             oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
         else
             try
-                name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:));
+                name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
                 [post_mean,hpd_interval,post_var] = Extractoo(oo_,name,type);
             catch
                 Draws = GetAllPosteriorDraws(ip,FirstMhFile,FirstLine,TotalNumberOfMhFiles,NumberOfDraws);
                 [post_mean, post_median, post_var, hpd_interval, post_deciles, density] = ...
                     posterior_moments(Draws,1,options_.mh_conf_sig);
-                name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:));
+                name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
                 oo_ = Filloo(oo_,name,type,post_mean,hpd_interval,post_median,post_var,post_deciles,density);
             end
         end

matlab/PlotPosteriorDistributions.m

@@ -89,7 +89,7 @@ for i=1:npar
             eval(['pmod = oo_.posterior_mode.shocks_std.' name ';'])
         end
     elseif i <= nvx+nvn
-        name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i-nvx,1),:));
+        name = options_.varobs{estim_params_.nvn_observable_correspondence(i-nvx,1)};
         eval(['x1 = oo_.posterior_density.measurement_errors_std.' name '(:,1);'])
         eval(['f1 = oo_.posterior_density.measurement_errors_std.' name '(:,2);'])    
         eval(['oo_.prior_density.mearsurement_errors_std.' name '(:,1) = x2;'])

matlab/PosteriorFilterSmootherAndForecast.m

@@ -48,7 +48,7 @@ MaxNumberOfPlotPerFigure = 4;% The square root must be an integer!
 MaxNumberOfBytes=options_.MaxNumberOfBytes;
 endo_nbr=M_.endo_nbr;
 exo_nbr=M_.exo_nbr;
-nvobs     = size(options_.varobs,1);
+nvobs     = length(options_.varobs);
 nn = sqrt(MaxNumberOfPlotPerFigure);
 iendo = 1:endo_nbr;
 i_last_obs = gend+(1-M_.maximum_endo_lag:0);
@@ -70,9 +70,9 @@ B = 200;
 MAX_nruns = min(B,ceil(options_.MaxNumberOfBytes/(npar+2)/8));
 MAX_nsmoo = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*gend)/8));
 MAX_ninno = min(B,ceil(MaxNumberOfBytes/(exo_nbr*gend)/8));
-MAX_nerro = min(B,ceil(MaxNumberOfBytes/(size(options_.varobs,1)*gend)/8));
+MAX_nerro = min(B,ceil(MaxNumberOfBytes/(length(options_.varobs)*gend)/8));
 if naK
-    MAX_naK   = min(B,ceil(MaxNumberOfBytes/(size(options_.varobs,1)* ...
+    MAX_naK   = min(B,ceil(MaxNumberOfBytes/(length(options_.varobs)* ...
                                              length(options_.filter_step_ahead)*gend)/8));
 end
 if horizon

matlab/PosteriorIRF.m

@@ -42,7 +42,7 @@ end
 % Set varlist if necessary
 varlist = options_.varlist;
 if isempty(varlist)
-    varlist = options_.varobs;
+    varlist = char(options_.varobs);
 end
 options_.varlist = varlist;
 nvar = size(varlist,1);

matlab/ReshapeMatFiles.m

@@ -70,7 +70,7 @@ switch type
     TYPEarray = 4;    
   case 'irf_bvardsge'
     CAPtype  = 'IRF_BVARDSGE';
-    TYPEsize = [ options_.irf , size(options_.varobs,1) , M_.exo_nbr ];
+    TYPEsize = [ options_.irf , length(options_.varobs) , M_.exo_nbr ];
     TYPEarray = 4;      
   case 'smooth'
     CAPtype  = 'SMOOTH';
@@ -82,7 +82,7 @@ switch type
     TYPEarray = 3;
   case 'error'
     CAPtype = 'ERROR';
-    TYPEsize = [ size(options_.varobs,1) , options_.nobs ];
+    TYPEsize = [ length(options_.varobs) , options_.nobs ];
     TYPEarray = 3;
   case 'innov'
     CAPtype = 'INNOV';

matlab/bvar_forecast.m

@@ -120,7 +120,7 @@ for i = 1:ny
     dynare_graph([ sims_no_shock_median(:, i) ...
                    sims_no_shock_up_conf(:, i) sims_no_shock_down_conf(:, i) ...
                    sims_with_shocks_up_conf(:, i) sims_with_shocks_down_conf(:, i) ], ...
-                 options_.varobs(i, :));
+                 options_.varobs{i});
 end
 
 dynare_graph_close;
@@ -145,8 +145,8 @@ if ~isempty(forecast_data.realized_val)
     
     fprintf('RMSE of BVAR(%d):\n', nlags);
     
-    for i = 1:size(options_.varobs, 1)
-        fprintf('%s: %10.4f\n', options_.varobs(i, :), rmse(i));
+    for i = 1:length(options_.varobs)
+        fprintf('%s: %10.4f\n', options_.varobs{i}, rmse(i));
     end 
 end
 
@@ -161,8 +161,8 @@ if ~isdir(DirectoryName)
 end
 save([ DirectoryName '/simulations.mat'], 'sims_no_shock', 'sims_with_shocks');
 
-for i = 1:size(options_.varobs, 1)
-    name = options_.varobs(i, :);
+for i = 1:length(options_.varobs)
+    name = options_.varobs{i};
 
     sims = squeeze(sims_with_shocks(:,i,:));
     eval(['oo_.bvar.forecast.with_shocks.Mean.' name ' = mean(sims, 2);']);

matlab/bvar_irf.m

@@ -138,9 +138,9 @@ save([ DirectoryName '/simulations.mat'], 'sampled_irfs');
 
 % Save results in oo_
 for i=1:ny
-    shock_name = options_.varobs(i, :);
+    shock_name = options_.varobs{i};
     for j=1:ny
-        variable_name = options_.varobs(j, :);
+        variable_name = options_.varobs{j};
         eval(['oo_.bvar.irf.Mean.' variable_name '.' shock_name ' = posterior_mean_irfs(' int2str(j) ',' int2str(i) ',:);'])
         eval(['oo_.bvar.irf.Median.' variable_name '.' shock_name ' = posterior_median_irfs(' int2str(j) ',' int2str(i) ',:);'])
         eval(['oo_.bvar.irf.Var.' variable_name '.' shock_name ' = posterior_variance_irfs(' int2str(j) ',' int2str(i) ',:);'])

matlab/check_list_of_variables.m

@@ -41,7 +41,7 @@ if options_.dsge_var && options_.bayesian_irf
                 msg = 1;
             end
         end
-        if size(varlist,1)~=size(options_.varobs)
+        if size(varlist,1)~=length(options_.varobs)
             msg = 1;
         end
         if msg
@@ -50,7 +50,7 @@ if options_.dsge_var && options_.bayesian_irf
             disp(' ')
         end
     end
-    varlist = options_.varobs;
+    varlist = char(options_.varobs);
     return
 end
 
@@ -110,7 +110,7 @@ if isempty(varlist)
             if choice==1
                 varlist = M_.endo_names(1:M_.orig_endo_nbr, :);
             elseif choice==2
-                varlist = options_.varobs;
+                varlist = char(options_.varobs);
             elseif choice==3
                 varlist = NaN;
             else

matlab/compute_moments_varendo.m

@@ -36,14 +36,14 @@ function oo_ = compute_moments_varendo(type,options_,M_,oo_,var_list_)
 if strcmpi(type,'posterior')
     posterior = 1;
     if nargin==4
-        var_list_ = options_.varobs;
+        var_list_ = char(options_.varobs);
     end
 elseif strcmpi(type,'prior')
     posterior = 0;
     if nargin==4
         var_list_ = options_.prior_analysis_endo_var_list;
         if isempty(var_list_)
-            options_.prior_analysis_var_list = options_.varobs;
+            options_.prior_analysis_var_list = char(options_.varobs);
         end
     end
 else

matlab/dsgevar_posterior_density.m

@@ -38,7 +38,7 @@ dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
 DSGE_PRIOR_WEIGHT = floor(gend*(1+dsge_prior_weight));
 
 bvar.NumberOfLags = options_.varlag;
-bvar.NumberOfVariables = size(options_.varobs,1);
+bvar.NumberOfVariables = length(options_.varobs);
 bvar.Constant = 'no';
 bvar.NumberOfEstimatedParameters = bvar.NumberOfLags*bvar.NumberOfVariables;
 if ~options_.noconstant

matlab/dyn_forecast.m

@@ -81,8 +81,8 @@ switch task
         order_var = oo_.dr.order_var;
         i_var_obs = [];
         trend_coeffs = [];
-        for i=1:size(var_obs,1)
-            tmp = strmatch(var_obs(i,:),endo_names(i_var,:),'exact');
+        for i=1:length(var_obs)
+            tmp = strmatch(var_obs{i},endo_names(i_var,:),'exact');
             if ~isempty(tmp)
                 i_var_obs = [ i_var_obs; tmp];
                 trend_coeffs = [trend_coeffs; oo_.Smoother.TrendCoeffs(i)];

matlab/dynare_estimation.m

@@ -93,7 +93,7 @@ if nnobs > 1 && horizon > 0
     end
 
     endo_names = M_.endo_names;
-    n_varobs = size(options_.varobs,1);
+    n_varobs = length(options_.varobs);
 
     if isempty(var_list)
         var_list = endo_names;
@@ -116,7 +116,7 @@ if nnobs > 1 && horizon > 0
     IdObs    = zeros(n_varobs,1);
     for j=1:n_varobs
         for i=1:nvar
-            iobs = strmatch(options_.varobs(j,:),var_list,'exact');
+            iobs = strmatch(options_.varobs{j},var_list,'exact');
         end
         if ~isempty(iobs)
             IdObs(j,1) = iobs;

matlab/dynare_estimation_1.m

@@ -103,7 +103,7 @@ missing_value = dataset_.missing.state;
 % Set number of observations
 gend = options_.nobs;
 % Set the number of observed variables.
-n_varobs = size(options_.varobs,1);
+n_varobs = length(options_.varobs);
 % Get the number of parameters to be estimated.
 nvx = estim_params_.nvx;  % Variance of the structural innovations (number of parameters).
 nvn = estim_params_.nvn;  % Variance of the measurement innovations (number of parameters).
@@ -572,7 +572,7 @@ if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
         disp(tit1)
         ip = nvx+1;
         for i=1:nvn
-            name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:));
+            name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
             disp(sprintf('%-*s %7.3f %8.4f %7.4f %7.4f %4s %6.4f', ...
                          header_width,name,bayestopt_.p1(ip), ...
                          xparam1(ip),stdh(ip),tstath(ip), ...
@@ -671,7 +671,7 @@ elseif ~any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
         disp(tit1)
         ip = nvx+1;
         for i=1:nvn
-            name = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:));
+            name = options_.varobs{estim_params_.nvn_observable_correspondence(i,1)};
             disp(sprintf('%-*s %8.4f %7.4f %7.4f',header_width,name,xparam1(ip),stdh(ip),tstath(ip)))
             eval(['oo_.mle_mode.measurement_errors_std.' name ' = xparam1(ip);']);
             eval(['oo_.mle_std.measurement_errors_std.' name ' = stdh(ip);']);
@@ -817,7 +817,7 @@ if any(bayestopt_.pshape > 0) && options_.TeX %% Bayesian estimation (posterior
         fprintf(fidTeX,'\\hline \\hline \\endlastfoot \n');
         ip = nvx+1;
         for i=1:nvn
-            idx = strmatch(options_.varobs(estim_params_.nvn_observable_correspondence(i,1),:),M_.endo_names);
+            idx = strmatch(options_.varobs{estim_params_.nvn_observable_correspondence(i,1)},M_.endo_names);
             fprintf(fidTeX,'$%s$ & %4s & %7.3f & %6.4f & %8.4f & %7.4f \\\\ \n',...
                     deblank(M_.endo_names_tex(idx,:)), ...
                     deblank(pnames(bayestopt_.pshape(ip)+1,:)), ...
@@ -1106,8 +1106,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
                 number_of_plots_to_draw = number_of_plots_to_draw + 1;
                 index = cat(1,index,i);
             end
-            eval(['oo_.SmoothedMeasurementErrors.' deblank(options_.varobs(i,:)) ...
-                  ' = measurement_error(i,:)'';']);
+            eval(['oo_.SmoothedMeasurementErrors.' options_.varobs{i} ' = measurement_error(i,:)'';']);
         end
         if ~options_.nograph
             [nbplt,nr,nc,lr,lc,nstar] = pltorg(number_of_plots_to_draw);
@@ -1129,7 +1128,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
                     hold on
                     plot(1:gend,measurement_error(index(k),:),'-k','linewidth',1)
                     hold off
-                    name = deblank(options_.varobs(index(k),:));
+                    name = options_.varobs{index(k)};
                     xlim([1 gend])
                     if isempty(NAMES)
                         NAMES = name;
@@ -1141,7 +1140,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
                         set(gca,'XTickLabel',options_.XTickLabel)
                     end
                     if options_.TeX
-                        idx = strmatch(options_.varobs(index(k),:),M_.endo_names,'exact');
+                        idx = strmatch(options_.varobs{index(k)},M_.endo_names,'exact');
                         texname = M_.endo_names_tex(idx,:);
                         if isempty(TeXNAMES)
                             TeXNAMES = ['$ ' deblank(texname) ' $'];
@@ -1195,7 +1194,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
             hold on
             plot(1:gend,rawdata(:,k),'--k','linewidth',1)
             hold off
-            name = deblank(options_.varobs(k,:));
+            name = options_.varobs{k};
             if isempty(NAMES)
                 NAMES = name;
             else
@@ -1207,7 +1206,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
             end
             xlim([1 gend])
             if options_.TeX
-                idx = strmatch(options_.varobs(k,:),M_.endo_names,'exact');
+                idx = strmatch(options_.varobs{k},M_.endo_names,'exact');
                 texname = M_.endo_names_tex(idx,:);
                 if isempty(TeXNAMES)
                     TeXNAMES = ['$ ' deblank(texname) ' $'];

matlab/dynare_estimation_init.m

@@ -116,7 +116,7 @@ else
 end
 
 % Set the number of observed variables.
-n_varobs = size(options_.varobs,1);
+n_varobs = length(options_.varobs);
 
 % Set priors over the estimated parameters.
 if ~isempty(estim_params_)
@@ -223,8 +223,8 @@ end
 var_obs_index = [];
 k1 = [];
 for i=1:n_varobs
-    var_obs_index = [var_obs_index; strmatch(deblank(options_.varobs(i,:)),M_.endo_names(dr.order_var,:),'exact')];
-    k1 = [k1; strmatch(deblank(options_.varobs(i,:)),M_.endo_names, 'exact')];
+    var_obs_index = [var_obs_index; strmatch(options_.varobs{i},M_.endo_names(dr.order_var,:),'exact')];
+    k1 = [k1; strmatch(options_.varobs{i},M_.endo_names, 'exact')];
 end
 
 % Define union of observed and state variables

matlab/dynare_identification.m

@@ -132,7 +132,7 @@ options_.smoother=1;
 options_ident.analytic_derivation_mode = options_.analytic_derivation_mode;
 if isempty(dataset_),
     dataset_.info.ntobs = periods;
-    dataset_.info.nvobs = rows(options_.varobs);
+    dataset_.info.nvobs = length(options_.varobs);
     dataset_.info.varobs = options_.varobs;
     dataset_.rawdata = [];
     dataset_.missing.state = 0;
@@ -145,17 +145,8 @@ if isempty(dataset_),
     dataset_.missing.no_more_missing_observations = 1;
     dataset_.descriptive.mean = [];
     dataset_.data = [];
-
-%     data_info.gend = periods;
-%     data_info.data = [];
-%     data_info.data_index = [];
-%     data_info.number_of_observations = periods*size(options_.varobs,1);
-%     data_info.no_more_missing_observations = 0;
-%     data_info.missing_value = 0;
 end
 
-% results = prior_sampler(0,M_,bayestopt_,options_,oo_);
-
 if prior_exist
     if any(bayestopt_.pshape > 0)
         if options_ident.prior_range

matlab/dynare_sensitivity.m

@@ -400,9 +400,9 @@ if options_gsa.glue,
     Obs.data = data;
     Obs.time = [1:gend];
     Obs.num  = gend;
-    for j=1:size(options_.varobs,1)
-        Obs.name{j} = deblank(options_.varobs(j,:));
-        vj=deblank(options_.varobs(j,:));
+    for j=1:length(options_.varobs)
+        Obs.name{j} = options_.varobs{j};
+        vj = options_.varobs{j};
         
         jxj = strmatch(vj,lgy_(dr_.order_var,:),'exact');
         js = strmatch(vj,lgy_,'exact');
@@ -440,7 +440,7 @@ if options_gsa.glue,
         ismoo(j)=jxj;
         
     end
-    jsmoo = size(options_.varobs,1);
+    jsmoo = length(options_.varobs);
     for j=1:M_.endo_nbr,
         if ~ismember(j,ismoo),
             jsmoo=jsmoo+1;
@@ -465,10 +465,10 @@ if options_gsa.glue,
         Exo(j).name = deblank(tit(j,:));    
     end
     if ~options_gsa.ppost
-        Lik(size(options_.varobs,1)+1).name = 'logpo';
-        Lik(size(options_.varobs,1)+1).ini  = 'yes';
-        Lik(size(options_.varobs,1)+1).isam = 1;
-        Lik(size(options_.varobs,1)+1).data = -logpo2;
+        Lik(length(options_.varobs)+1).name = 'logpo';
+        Lik(length(options_.varobs)+1).ini  = 'yes';
+        Lik(length(options_.varobs)+1).isam = 1;
+        Lik(length(options_.varobs)+1).data = -logpo2;
     end
     Sam.name = bayestopt_.name;
     Sam.dim  = [size(x) 0];

matlab/forecast_graphs.m

@@ -24,10 +24,6 @@ nr = 3;
 exo_nbr = M_.exo_nbr;
 endo_names = M_.endo_names;
 fname = M_.fname;
-% $$$     varobs = options_.varobs;
-% $$$     y = oo_.SmoothedVariables;
-% $$$     ys = oo_.dr.ys;
-% $$$     gend = size(y,2);
 yf = oo_.forecast.Mean;
 hpdinf = oo_.forecast.HPDinf;
 hpdsup = oo_.forecast.HPDsup;
@@ -44,13 +40,6 @@ for i = 1:size(var_list)
 end
 nvar = length(i_var);
 
-% $$$     % build trend for smoothed variables if necessary
-% $$$     trend = zeros(size(varobs,1),10);
-% $$$     if isfield(oo_.Smoother,'TrendCoeffs')
-% $$$         trend_coeffs = oo_.Smoother.TrendCoeffs;
-% $$$         trend = trend_coeffs*(gend-9:gend);
-% $$$     end
-
 % create subdirectory <fname>/graphs if id doesn't exist
 if ~exist(fname, 'dir')
     mkdir('.',fname);

matlab/get_posterior_parameters.m

@@ -50,7 +50,7 @@ end
 
 for i=1:nvn
     k1 = estim_params_.nvn_observable_correspondence(i,1);
-    name1 = deblank(options_.varobs(k1,:));
+    name1 = options_.varobs{k1};
     xparam(m) = eval(['oo_.posterior_' type '.measurement_errors_std.' name1]);
     m = m+1;
 end
@@ -69,8 +69,8 @@ end
 for i=1:ncn
     k1 = estim_params_.corrn_observable_correspondence(i,1);
     k2 = estim_params_.corrn_observable_correspondence(i,2);
-    name1 = deblank(options_.varobs(k1,:));
-    name2 = deblank(options_.varobs(k2,:));
+    name1 = options_.varobs{k1};
+    name2 = options_.varobs{k2};
     xparam(m) = eval(['oo_.posterior_' type '.measurement_errors_corr.' name1 '_' name2]);
     m = m+1;
 end

matlab/get_the_name.m

@@ -73,7 +73,7 @@ if k <= nvx
         texnam = ['$ SE_{' tname '} $'];
     end
 elseif  k <= (nvx+nvn)
-    vname = deblank(options_.varobs(estim_params_.nvn_observable_correspondence(k-estim_params_.nvx,1),:));
+    vname = options_.varobs{estim_params_.nvn_observable_correspondence(k-estim_params_.nvx,1)};
     nam=['SE_EOBS_',vname];
     if TeX
         tname  = deblank(M_.endo_names_tex(estim_params_.var_endo(k-estim_params_.nvx,1),:));