Merge branch 'use-dseries'

doc/dynare.texi

@@ -9733,7 +9733,7 @@ Instantiates an empty @dseries object, with, if defined, an initial date given b
 
 @sp 1
 
-@deftypefn {dseries} dseries (@var{FILENAME})
+@deftypefn {dseries} dseries (@var{FILENAME}[, @var{INITIAL_DATE}])
 
 Instantiates and populates a @dseries object with a data file specified by @var{FILENAME}, a string passed as input. Valid file types are @file{.m} file, @file{.mat} file, @file{.csv} file, and @file{.xls} file. A typical @file{.m} file will have the following form:
 
@@ -9746,7 +9746,7 @@ azert = randn(100,1);
 yuiop = randn(100,1);
 @end example
 
-If a @file{.mat} file is used instead, it should provide the same informations. Note that the @code{INIT__} variable can be either a @dates object or a string which could be used to instantiate the same @dates object.
+If a @file{.mat} file is used instead, it should provide the same informations. Note that the @code{INIT__} variable can be either a @dates object or a string which could be used to instantiate the same @dates object. If @code{INIT__} is not provided in the @file{.mat} or @file{.m} file, the initial is by default set equal to @code{dates('1Y')}. If a second input argument is passed to the constructor, @dates object @var{INITIAL_DATE}, the initial date defined in @var{FILENAME} is reset to @var{INITIAL_DATE}. This is typically usefull if @code{INIT__} is not provided in the data file.
 
 @end deftypefn
 

matlab/@dates/dates.m

@@ -45,7 +45,7 @@ function dd = dates(varargin) % --*-- Unitary tests --*--
 %! @end deftypefn
 %@eod:
 
-% Copyright (C) 2011-2013 Dynare Team
+% Copyright (C) 2011-2014 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -62,6 +62,7 @@ function dd = dates(varargin) % --*-- Unitary tests --*--
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
+% Initialization.
 if nargin>0 && ischar(varargin{1}) && isequal(varargin{1},'initialize')
     dd = struct('ndat', 0, 'freq', NaN(0), 'time', NaN(0,2));
     dd = class(dd,'dates');
@@ -71,61 +72,98 @@ end
 
 dd = evalin('base','emptydatesobject');
 
-switch nargin
+if isequal(nargin, 0)
-  case 0
+    % Return an empty dates obect
-    % Returns an empty object
     return
-  case 1
+end
-    if isa(varargin{1},'dates')
+
-        % Returns a copy of the input argument
+if all(cellfun(@isdates, varargin))
-        dd = varargin{1};
+    % Concatenates dates in a dates object.
-    elseif isdate(varargin{1})
+    dd = horzcat(varargin{:});
-        date = string2date(varargin{1});
+    return
-        dd.ndat = 1;
+end
-        dd.freq = date.freq;
+
-        dd.time = date.time;
+if all(cellfun(@isstringdate,varargin))
-    elseif isfreq(varargin{1})
+    % Concatenates dates in a dates object.
-        % Instantiate an empty dates object (only set frequency)
+    tmp = cellfun(@string2date,varargin);
-        if ischar(varargin{1})
+    if all([tmp.freq]-tmp(1).freq==0)
-            dd.freq = string2freq(varargin{1});
+        dd.freq = tmp(1).freq;
-        else
-            dd.freq = varargin{1};
-        end
     else
-        error('dates:: Wrong calling sequence of the constructor!')
+        error('dates::dates: Wrong calling sequence of the constructor! All dates must have common frequency.')
     end
-  otherwise
+    dd.ndat = length(tmp);
-    if isdate(varargin{1})
+    dd.time = transpose(reshape([tmp.time],2,dd.ndat));
-        dd.ndat = nargin;
+    return
-        dd.time = NaN(dd.ndat,2);
+end
-        date = string2date(varargin{1});
+
-        dd.freq = date.freq;
+if isequal(nargin,1) && isfreq(varargin{1})
-        dd.time(1,:) = date.time;
+    % Instantiate an empty dates object (only set frequency)
-    elseif isdates(varargin{1})
+    if ischar(varargin{1})
-        dd = horzcat(varargin{:});
+        dd.freq = string2freq(varargin{1});
-        return
-    elseif isfreq(varargin{1})
-        S.type = '()';
-        S.subs = varargin;
-        dd = subsref(dd,S);
-        return
     else
-        error('dates::dates: Wrong calling sequence!')
+        dd.freq = varargin{1};
     end
-    for i=2:dd.ndat
+    return
-        if isdate(varargin{i})
+end
-            date = string2date(varargin{i});
+
-            if isequal(date.freq,dd.freq)
+if isequal(nargin,3) && isfreq(varargin{1})
-                dd.time(i,:) = date.time;
+    if ischar(varargin{1})
+        dd.freq = string2freq(varargin{1});
+    else
+        dd.freq = varargin{1};
+    end
+    if (isnumeric(varargin{2}) && isvector(varargin{2}) && isint(varargin{2}))
+        if isnumeric(varargin{3}) && isvector(varargin{3}) && isint(varargin{3})
+            if all(varargin{3}>=1) && all(varargin{3}<=dd.freq)
+                dd.time = [varargin{2}(:), varargin{3}(:)];
+                dd.ndat = size(dd.time,1);
             else
-                 error(sprintf('dates::dates: Check that all the inputs have the same frequency (see input number %i)!',i))
+                error(sprintf('dates::dates: Wrong calling sequence of the constructor! Third input must contain integers between 1 and %i.',dd.freq))
             end
         else
-            error(sprintf('dates::dates: Input %i has to be a string date!',i))
+            error('dates::dates: Wrong calling sequence of the constructor! Third input must be a vector of integers.')
+        end
+    else
+        error('dates::dates: Wrong calling sequence of the constructor! Second input must be a vector of integers.')
+    end
+    return
+end
+
+if isequal(nargin,2) && isfreq(varargin{1})
+    if ischar(varargin{1})
+        dd.freq = string2freq(varargin{1});
+    else
+        dd.freq = varargin{1};
+    end
+    if isequal(dd.freq, 1)
+        if (isnumeric(varargin{2}) && isvector(varargin{2}) && isint(varargin{2}))
+            dd.time = [varargin{2}, ones(length(varargin{2}),1)];
+            dd.ndat = size(dd.time,1);
+            return
+        else
+            error('dates::dates: Wrong calling sequence of the constructor! Second input must be a vector of integers.')
+        end
+    else
+        if isequal(size(varargin{2},2), 2)
+            if all(isint(varargin{2}(:,1))) && all(isint(varargin{2}(:,1)))
+                if all(varargin{2}(:,2)>=1) && all(varargin{2}(:,2)<=dd.freq)
+                    dd.time = [varargin{2}(:,1), varargin{2}(:,2)];
+                    dd.ndat = size(dd.time,1);
+                else
+                    error(sprintf('dates::dates: Wrong calling sequence of the constructor! Second column of the last input must contain integers between 1 and %i.',dd.freq))
+                end
+            else
+                error('dates::dates: Wrong calling sequence! Second input argument must be an array of integers.')
+            end
+        else
+            error('dates::dates: Wrong calling sequence!')
         end
     end
+    return
 end
 
+error('dates::dates: Wrong calling sequence!')
+
 %@test:1
 %$ % Define some dates
 %$ B1 = '1945Q3';

matlab/@dseries/dseries.m

@@ -10,7 +10,7 @@ function ts = dseries(varargin) % --*-- Unitary tests --*--
 %! @sp 2
 %! If @code{nargin==0} then an empty dseries object is created. The object can be populated with data subsequently using the overloaded subsref method.
 %! @sp 2
-%! If @code{nargin==1} and if the input argument is a @ref{dynDate} object, then a dseries object without data is created. This object can be populated with the overload subsref method.
+%! If @code{nargin==1} and if the input argument is a @ref{dates} object, then a dseries object without data is created. This object can be populated with the overload subsref method.
 %! @sp 2
 %! If @code{nargin==1} and if the input argument is a string for the name of a csv, m or mat file containing data, then a dseries object is created from these data.
 %! @sp 2
@@ -54,7 +54,7 @@ function ts = dseries(varargin) % --*-- Unitary tests --*--
 %! frequency is unspecified. @var{freq} is equal to 4 if data are on a quaterly basis. @var{freq} is equal to
 %! 12 if data are on a monthly basis. @var{freq} is equal to 52 if data are on a weekly basis.
 %! @item init
-%! @ref{dynDate} object, initial date of the dataset.
+%! @ref{dates} object, initial date of the dataset.
 %! @end table
 %! @end deftypefn
 %@eod:
@@ -153,6 +153,12 @@ switch nargin
         ts.dates = dates(1,1):dates(1,1)+(ts.nobs-1);
     end
   case {2,3,4}
+    if isequal(nargin,2) && ischar(varargin{1}) && isdates(varargin{2})
+        % Instantiate dseries object with a data file and force the initial date to be as given by the second input argument.
+        ds = dseries(varargin{1});
+        ts = dseries(ds.data, varargin{2}, ds.name, ds.tex);
+        return
+    end
     a = varargin{1};
     b = varargin{2};
     if nargin<4

matlab/@dseries/ydiff.m

@@ -151,7 +151,7 @@ end
 %$
 %$ try
 %$     data = transpose(1:100);
-%$     ts = dseries(data,1950,{'A1'},{'A_1'});
+%$     ts = dseries(data,'1950Y',{'A1'},{'A_1'});
 %$     ts = ts.ydiff;
 %$     t(1) = 1;
 %$ catch

matlab/@dynTimeIndex/subsasgn.m

@@ -17,4 +17,4 @@ function val = subsasgn(val, idx, rhs)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-error('dynTimeIndex::subsasgn: Members of dynDate class are private!')
+error('dynTimeIndex::subsasgn: Members of dynTimeIndex class are private!')

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

@@ -180,17 +180,17 @@ 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);
@@ -74,9 +74,9 @@ 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
@@ -272,4 +272,4 @@ if options_.forecast
     pm3(endo_nbr,horizon+maxlag,ifil6,B,'Forecasted variables (total)',...
         M_.endo_names(SelecVariables),M_.endo_names,'tit_tex',M_.endo_names,...
         'names2','smooth',MetropolisFolder,'_forc_total')
-end
+end

matlab/PosteriorIRF.m

@@ -1,11 +1,11 @@
 function PosteriorIRF(type)
-% Builds posterior IRFs after the MH algorithm. 
+% Builds posterior IRFs after the MH algorithm.
-% 
+%
-% INPUTS 
+% INPUTS
 %   o type       [char]     string specifying the joint density of the
-%                           deep parameters ('prior','posterior'). 
+%                           deep parameters ('prior','posterior').
-%  
+%
-% OUTPUTS 
+% OUTPUTS
 %   None                    (oo_ and plots).
 %
 % SPECIAL REQUIREMENTS
@@ -34,15 +34,16 @@ function PosteriorIRF(type)
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 
-global options_ estim_params_ oo_ M_ bayestopt_ dataset_
+global options_ estim_params_ oo_ M_ bayestopt_ dataset_ dataset_info
+
 % Set the number of periods
-if isempty(options_.irf) || ~options_.irf 
+if isempty(options_.irf) || ~options_.irf
     options_.irf = 40;
 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);
@@ -58,7 +59,7 @@ end
 
 % Get index of shocks for requested IRFs
 irf_shocks_indx = getIrfShocksIndx();
-    
+
 % Set various parameters & Check or create directories
 nvx  = estim_params_.nvx;
 nvn  = estim_params_.nvn;
@@ -68,8 +69,8 @@ np   = estim_params_.np ;
 npar = nvx+nvn+ncx+ncn+np;
 offset = npar-np; clear('nvx','nvn','ncx','ncn','np');
 
-nvobs = dataset_.info.nvobs;
+nvobs = dataset_.vobs;
-gend = dataset_.info.ntobs;
+gend = dataset_.nobs;
 MaxNumberOfPlotPerFigure = 9;
 nn = sqrt(MaxNumberOfPlotPerFigure);
 MAX_nirfs_dsge = ceil(options_.MaxNumberOfBytes/(options_.irf*nvar*M_.exo_nbr)/8);
@@ -111,14 +112,14 @@ else% type = 'prior'
     B = options_.prior_draws;
     options_.B = B;
 end
-try 
+try
     delete([MhDirectoryName filesep M_.fname '_irf_dsge*.mat'])
-catch 
+catch
     disp('No _IRFs (dsge) files to be deleted!')
 end
-try 
+try
     delete([MhDirectoryName filesep M_.fname '_irf_bvardsge*.mat'])
-catch 
+catch
     disp('No _IRFs (bvar-dsge) files to be deleted!')
 end
 irun = 0;
@@ -144,10 +145,6 @@ if MAX_nirfs_dsgevar
     else
         stock_irf_bvardsge = zeros(options_.irf,nvobs,M_.exo_nbr,B);
     end
-    [mYY,mXY,mYX,mXX,Ydata,Xdata] = ...
-        var_sample_moments(options_.first_obs,options_.first_obs+options_.nobs-1,...
-                           options_.dsge_varlag,-1,options_.datafile,options_.varobs,...
-                           options_.xls_sheet,options_.xls_range);
     NumberOfLags = options_.dsge_varlag;
     NumberOfLagsTimesNvobs = NumberOfLags*nvobs;
     if options_.noconstant
@@ -174,7 +171,7 @@ localVars.npar = npar;
 
 localVars.type=type;
 if strcmpi(type,'posterior')
-    while b<B 
+    while b<B
         b = b + 1;
         x(b,:) = GetOneDraw(type);
     end
@@ -192,7 +189,7 @@ if options_.dsge_var
     localVars.NumberOfLags = options_.dsge_varlag;
     localVars.NumberOfLagsTimesNvobs = NumberOfLags*nvobs;
     localVars.Companion_matrix = diag(ones(nvobs*(NumberOfLags-1),1),-nvobs);
-end 
+end
 localVars.nvar=nvar;
 localVars.IndxVariables=IndxVariables;
 localVars.MAX_nirfs_dsgevar=MAX_nirfs_dsgevar;
@@ -225,14 +222,15 @@ else
     localVars.NumberOfIRFfiles_dsge=NumberOfIRFfiles_dsge;
     localVars.NumberOfIRFfiles_dsgevar=NumberOfIRFfiles_dsgevar;
     localVars.ifil2=ifil2;
-    
+
     globalVars = struct('M_',M_, ...
                         'options_', options_, ...
                         'bayestopt_', bayestopt_, ...
                         'estim_params_', estim_params_, ...
                         'oo_', oo_, ...
-                        'dataset_',dataset_);
+                        'dataset_',dataset_, ...
-    
+                        'dataset_info',dataset_info);
+
     % which files have to be copied to run remotely
     NamFileInput(1,:) = {'',[M_.fname '_static.m']};
     NamFileInput(2,:) = {'',[M_.fname '_dynamic.m']};
@@ -244,13 +242,13 @@ else
     for j=1:length(fout),
         nosaddle = nosaddle + fout(j).nosaddle;
     end
-    
+
 end
 
 % END first parallel section!
 
 if nosaddle
-    disp(['PosteriorIRF :: Percentage of discarded posterior draws = ' num2str(nosaddle/(B+nosaddle))]) 
+    disp(['PosteriorIRF :: Percentage of discarded posterior draws = ' num2str(nosaddle/(B+nosaddle))])
 end
 
 ReshapeMatFiles('irf_dsge',type)
@@ -323,7 +321,7 @@ if MAX_nirfs_dsgevar
     MedianIRFdsgevar = zeros(options_.irf,nvar,M_.exo_nbr);
     VarIRFdsgevar = zeros(options_.irf,nvar,M_.exo_nbr);
     DistribIRFdsgevar = zeros(options_.irf,9,nvar,M_.exo_nbr);
-    HPDIRFdsgevar = zeros(options_.irf,2,nvar,M_.exo_nbr);    
+    HPDIRFdsgevar = zeros(options_.irf,2,nvar,M_.exo_nbr);
     fprintf('Estimation::mcmc: Posterior (bvar-dsge) IRFs...\n');
     tit(M_.exo_names_orig_ord,:) = M_.exo_names;
     kdx = 0;
@@ -341,7 +339,7 @@ if MAX_nirfs_dsgevar
         end
         kdx = kdx + size(STOCK_IRF_BVARDSGE,1);
     end
-    clear STOCK_IRF_BVARDSGE; 
+    clear STOCK_IRF_BVARDSGE;
     for i = irf_shocks_indx
         for j = 1:nvar
             name = [deblank(M_.endo_names(IndxVariables(j),:)) '_' deblank(tit(i,:))];
@@ -384,7 +382,7 @@ localVars.MaxNumberOfPlotPerFigure=MaxNumberOfPlotPerFigure;
 if options_.dsge_var
     localVars.HPDIRFdsgevar=HPDIRFdsgevar;
     localVars.MeanIRFdsgevar = MeanIRFdsgevar;
-end    
+end
 
 % The files .TeX are genereted in sequential way always!
 
@@ -394,14 +392,14 @@ if options_.TeX
     fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
     fprintf(fidTeX,' \n');
     titTeX(M_.exo_names_orig_ord,:) = M_.exo_names_tex;
-    
+
     for i=irf_shocks_indx
         NAMES = [];
         TEXNAMES = [];
-        
+
         for j=1:nvar
-            if max(abs(MeanIRF(:,j,i))) > options_.impulse_responses.plot_threshold  
+            if max(abs(MeanIRF(:,j,i))) > options_.impulse_responses.plot_threshold
-                
+
                 name = deblank(varlist(j,:));
                 texname = deblank(varlist_TeX(j,:));
 
@@ -413,7 +411,7 @@ if options_.TeX
                     TEXNAMES = char(TEXNAMES,['$' texname '$']);
                 end
             end
-            
+
         end
         fprintf(fidTeX,'\\begin{figure}[H]\n');
         for jj = 1:size(TEXNAMES,1)
@@ -430,10 +428,10 @@ if options_.TeX
         fprintf(fidTeX,'\\end{figure}\n');
         fprintf(fidTeX,' \n');
     end
-    
+
     fprintf(fidTeX,'%% End of TeX file.\n');
     fclose(fidTeX);
-    
+
 end
 
 % The others file format are generated in parallel by PosteriorIRF_core2!
@@ -453,7 +451,7 @@ if ~isoctave
         else
             globalVars = struct('M_',M_, ...
                 'options_', options_);
-            
+
             [fout] = masterParallel(options_.parallel, 1, M_.exo_nbr,NamFileInput,'PosteriorIRF_core2', localVars, globalVars, options_.parallel_info);
         end
     end
@@ -463,4 +461,4 @@ end
 % END parallel code!
 
 
-fprintf('Estimation::mcmc: Posterior IRFs, done!\n');
+fprintf('Estimation::mcmc: Posterior IRFs, done!\n');

matlab/PosteriorIRF_core1.m

@@ -41,7 +41,7 @@ function myoutput=PosteriorIRF_core1(myinputs,fpar,B,whoiam, ThisMatlab)
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 
-global options_ estim_params_ oo_ M_ bayestopt_ dataset_
+global options_ estim_params_ oo_ M_ bayestopt_ dataset_ dataset_info
 
 if nargin<4,
     whoiam=0;
@@ -189,24 +189,24 @@ while fpar<B
     end
     if MAX_nirfs_dsgevar
         IRUN = IRUN+1;
-        [fval,junk1,junk2,cost_flag,info,PHI,SIGMAu,iXX] =  dsge_var_likelihood(deep',dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+        [fval,junk1,junk2,cost_flag,info,PHI,SIGMAu,iXX] =  dsge_var_likelihood(deep',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
         dsge_prior_weight = M_.params(strmatch('dsge_prior_weight',M_.param_names));
-        DSGE_PRIOR_WEIGHT = floor(dataset_.info.ntobs*(1+dsge_prior_weight));
+        DSGE_PRIOR_WEIGHT = floor(dataset_.nobs*(1+dsge_prior_weight));
-        SIGMA_inv_upper_chol = chol(inv(SIGMAu*dataset_.info.ntobs*(dsge_prior_weight+1)));
+        SIGMA_inv_upper_chol = chol(inv(SIGMAu*dataset_.nobs*(dsge_prior_weight+1)));
         explosive_var  = 1;
         while explosive_var
             % draw from the marginal posterior of SIGMA
-            SIGMAu_draw = rand_inverse_wishart(dataset_.info.nvobs, DSGE_PRIOR_WEIGHT-NumberOfParametersPerEquation, ...
+            SIGMAu_draw = rand_inverse_wishart(dataset_.vobs, DSGE_PRIOR_WEIGHT-NumberOfParametersPerEquation, ...
                                                SIGMA_inv_upper_chol);
             % draw from the conditional posterior of PHI
-            PHI_draw = rand_matrix_normal(NumberOfParametersPerEquation,dataset_.info.nvobs, PHI, ...
+            PHI_draw = rand_matrix_normal(NumberOfParametersPerEquation,dataset_.vobs, PHI, ...
                                           chol(SIGMAu_draw)', chol(iXX)');
-            Companion_matrix(1:dataset_.info.nvobs,:) = transpose(PHI_draw(1:NumberOfLagsTimesNvobs,:));
+            Companion_matrix(1:dataset_.vobs,:) = transpose(PHI_draw(1:NumberOfLagsTimesNvobs,:));
             % Check for stationarity
             explosive_var = any(abs(eig(Companion_matrix))>1.000000001);
         end
         % Get the mean
-        mu = zeros(1,dataset_.info.nvobs);
+        mu = zeros(1,dataset_.vobs);
         % Get rotation
         if dsge_prior_weight > 0
             Atheta(oo_.dr.order_var,M_.exo_names_orig_ord) = oo_.dr.ghu*sqrt(M_.Sigma_e);
@@ -216,24 +216,24 @@ while fpar<B
         SIGMAu_chol = chol(SIGMAu_draw)';
         SIGMAtrOMEGA = SIGMAu_chol*OMEGAstar';
         PHIpower = eye(NumberOfLagsTimesNvobs);
-        irfs = zeros (options_.irf,dataset_.info.nvobs*M_.exo_nbr);
+        irfs = zeros (options_.irf,dataset_.vobs*M_.exo_nbr);
-        tmp3 = PHIpower(1:dataset_.info.nvobs,1:dataset_.info.nvobs)*SIGMAtrOMEGA;
+        tmp3 = PHIpower(1:dataset_.vobs,1:dataset_.vobs)*SIGMAtrOMEGA;
         irfs(1,:) = tmp3(:)';
         for t = 2:options_.irf
             PHIpower = Companion_matrix*PHIpower;
-            tmp3 = PHIpower(1:dataset_.info.nvobs,1:dataset_.info.nvobs)*SIGMAtrOMEGA;
+            tmp3 = PHIpower(1:dataset_.vobs,1:dataset_.vobs)*SIGMAtrOMEGA;
             irfs(t,:)  = tmp3(:)'+kron(ones(1,M_.exo_nbr),mu);
         end
         tmp_dsgevar = kron(ones(options_.irf,1),mu);
-        for j = 1:(dataset_.info.nvobs*M_.exo_nbr)
+        for j = 1:(dataset_.vobs*M_.exo_nbr)
             if max(irfs(:,j)) - min(irfs(:,j)) > 1e-10
                 tmp_dsgevar(:,j) = (irfs(:,j));
             end
         end
         if IRUN < MAX_nirfs_dsgevar
-            stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,dataset_.info.nvobs,M_.exo_nbr);
+            stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,dataset_.vobs,M_.exo_nbr);
         else
-            stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,dataset_.info.nvobs,M_.exo_nbr);
+            stock_irf_bvardsge(:,:,:,IRUN) = reshape(tmp_dsgevar,options_.irf,dataset_.vobs,M_.exo_nbr);
             instr = [MhDirectoryName '/' M_.fname '_irf_bvardsge' ...
                      int2str(NumberOfIRFfiles_dsgevar) '.mat stock_irf_bvardsge;'];,
             eval(['save ' instr]);
@@ -242,7 +242,7 @@ while fpar<B
             end
             NumberOfIRFfiles_dsgevar = NumberOfIRFfiles_dsgevar+1;
             IRUN =0;
-            stock_irf_dsgevar = zeros(options_.irf,dataset_.info.nvobs,M_.exo_nbr,MAX_nirfs_dsgevar);
+            stock_irf_dsgevar = zeros(options_.irf,dataset_.vobs,M_.exo_nbr,MAX_nirfs_dsgevar);
         end
     end
     if irun == MAX_nirfs_dsge || irun == B || fpar == B
@@ -279,20 +279,6 @@ while fpar<B
         ifil2 = ifil2 + 1;
         irun2 = 0;
     end
-%     if isoctave
-%         if (whoiam==0),
-%             printf(['Posterior IRF  %3.f%% done\r'],(fpar/B*100));
-%         end
-%     elseif ~whoiam,
-%         waitbar(fpar/B,h);
-%     end
-%     if whoiam,
-%         if ~isoctave
-%             fprintf('Done! \n');
-%         end
-%         waitbarString = [ 'Subdraw ' int2str(fpar) '/' int2str(B) ' done.'];
-%         fMessageStatus((fpar-fpar0)/(B-fpar0),whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab));
-%     end
     dyn_waitbar((fpar-fpar0)/(B-fpar0),h);
 end
 
@@ -310,9 +296,5 @@ end
 myoutput.OutputFileName = [OutputFileName_dsge;
                     OutputFileName_param;
                     OutputFileName_bvardsge];
-                
+
 myoutput.nosaddle = nosaddle;
-
-
-
-

matlab/PosteriorIRF_core2.m

@@ -5,7 +5,7 @@ function myoutput=PosteriorIRF_core2(myinputs,fpar,npar,whoiam, ThisMatlab)
 % Perform in parallel execution a portion of the PosteriorIRF.m code.
 % See also the comment in random_walk_metropolis_hastings_core.m funtion.
 %
-% INPUTS 
+% INPUTS
 %   See the comment in random_walk_metropolis_hastings_core.m funtion.
 %
 % OUTPUTS
@@ -13,8 +13,8 @@ function myoutput=PosteriorIRF_core2(myinputs,fpar,npar,whoiam, ThisMatlab)
 %  Contained:
 %  OutputFileName (i.e. the figures without the file .txt).
 %
-% ALGORITHM 
+% ALGORITHM
-%   Portion of PosteriorIRF.m function code. Specifically the last 'for' cycle.       
+%   Portion of PosteriorIRF.m function code. Specifically the last 'for' cycle.
 %
 % SPECIAL REQUIREMENTS.
 %   None.
@@ -36,7 +36,7 @@ function myoutput=PosteriorIRF_core2(myinputs,fpar,npar,whoiam, ThisMatlab)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-global options_  M_ 
+global options_  M_
 
 if nargin<4,
     whoiam=0;
@@ -87,7 +87,7 @@ OutputFileName={};
 subplotnum = 0;
 for i=fpar:npar,
     figunumber = 0;
-    
+
     for j=1:nvar
         if max(abs(MeanIRF(:,j,i))) > options_.impulse_responses.plot_threshold
             subplotnum = subplotnum+1;
@@ -96,7 +96,7 @@ for i=fpar:npar,
             elseif subplotnum == 1 && ~options_.relative_irf
                 hh = dyn_figure(options_,'Name',['Orthogonalized shock to ' tit(i,:)]);
             end
-            
+
             set(0,'CurrentFigure',hh)
             subplot(nn,nn,subplotnum);
             if ~MAX_nirfs_dsgevar
@@ -108,12 +108,12 @@ for i=fpar:npar,
                 set(h2,'FaceColor',[1 1 1]);
                 set(h2,'BaseValue',min(HPDIRF(:,1,j,i)));
                 plot(1:options_.irf,MeanIRF(:,j,i),'-k','linewidth',3)
-                % plot([1 options_.irf],[0 0],'-r','linewidth',0.5);          
+                % plot([1 options_.irf],[0 0],'-r','linewidth',0.5);
                 box on
                 axis tight
                 xlim([1 options_.irf]);
                 hold off
-            else    
+            else
                 h1 = area(1:options_.irf,HPDIRF(:,2,j,i));
                 set(h1,'FaceColor',[.9 .9 .9]);
                 set(h1,'BaseValue',min([min(HPDIRF(:,1,j,i)),min(HPDIRFdsgevar(:,1,j,i))]));
@@ -136,9 +136,9 @@ for i=fpar:npar,
         else
             if options_.debug
                 fprintf('POSTERIOR_IRF: The IRF of %s to %s is smaller than the irf_plot_threshold of %4.3f and will not be displayed.\n',deblank(varlist(j,:)),tit(i,:),options_.impulse_responses.plot_threshold)
-            end                
+            end
         end
-        
+
         if subplotnum == MaxNumberOfPlotPerFigure || (j == nvar  && subplotnum> 0)
             figunumber = figunumber+1;
             dyn_saveas(hh,[DirectoryName '/'  M_.fname '_Bayesian_IRF_' deblank(tit(i,:)) '_' int2str(figunumber)],options_);
@@ -154,9 +154,8 @@ for i=fpar:npar,
 %         fMessageStatus((i-fpar+1)/(npar-fpar+1),whoiam,waitbarString, waitbarTitle, Parallel(ThisMatlab));
         dyn_waitbar((i-fpar+1)/(npar-fpar+1),[],waitbarString);
     end
-end% loop over exo_var  
+end% loop over exo_var
 
 
 
 myoutput.OutputFileName = OutputFileName;
-

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

@@ -122,7 +122,7 @@ for i = 1:ny
     dyn_graph=dynare_graph(dyn_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
 
 dyn_saveas(dyn_graph.fh,[OutputDirectoryName '/' M_.fname '_BVAR_forecast_',num2str(nlags)],options_)
@@ -146,8 +146,8 @@ if ~isempty(forecast_data.realized_val)
     
     fprintf('RMSE of BVAR(%d):\n', nlags);
     
-    for i = 1:size(options_.varobs, 1)
+    for i = 1:length(options_.varobs)
-        fprintf('%s: %10.4f\n', options_.varobs(i, :), rmse(i));
+        fprintf('%s: %10.4f\n', options_.varobs{i}, rmse(i));
     end 
 end
 
@@ -162,8 +162,8 @@ if ~isdir(DirectoryName)
 end
 save([ DirectoryName '/simulations.mat'], 'sims_no_shock', 'sims_with_shocks');
 
-for i = 1:size(options_.varobs, 1)
+for i = 1:length(options_.varobs)
-    name = options_.varobs(i, :);
+    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_dsge_var_model.m

@@ -20,25 +20,25 @@ function check_dsge_var_model(Model, EstimatedParameters, BayesInfo)
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
 if EstimatedParameters.nvn
-    error('DsgeVarLikelihood:: Measurement errors are not allowed!')
+    error('Estimation::DsgeVarLikelihood: Measurement errors are not allowed!')
 end
 
 if EstimatedParameters.ncn
-    error('DsgeVarLikelihood:: Measurement errors are not allowed!')
+    error('Estimation::DsgeVarLikelihood: Measurement errors are not allowed!')
 end
 
 if any(vec(Model.H))
-    error('DsgeVarLikelihood:: Measurement errors are not allowed!')
+    error('Estimation::DsgeVarLikelihood: Measurement errors are not allowed!')
 end
 
 if EstimatedParameters.ncx
-    error('DsgeVarLikelihood:: Structural innovations cannot be correlated using Dynare''s interface! Introduce the correlations in the model block instead.')
+    error('Estimation::DsgeVarLikelihood: Structural innovations cannot be correlated using Dynare''s interface! Introduce the correlations in the model block instead.')
 end
 
 if Model.exo_nbr>1 && any(vec(tril(Model.Sigma_e,-1)))
-    error('DsgeVarLikelihood:: Structural innovations cannot be correlated using Dynare''s interface! Introduce the correlations in the model block instead.')
+    error('Estimation::DsgeVarLikelihood: Structural innovations cannot be correlated using Dynare''s interface! Introduce the correlations in the model block instead.')
 end
 
 if isequal(BayesInfo.with_trend,1)
-    error('DsgeVarLikelihood :: Linear trend is not yet implemented!')
+    error('Estimation::DsgeVarLikelihood: Linear trend is not yet implemented!')
 end

matlab/check_file_extension.m

@@ -19,6 +19,10 @@ function b = check_file_extension(file,type)
 
 % AUTHOR(S) stephane DOT adjemian AT univ DASH lemans DOT fr
 
+% Clean-up path
+file = strrep(file, '../', '');
+file = strrep(file, './', '');
+
 remain = file;
 while ~isempty(remain)
     [ext, remain] = strtok(remain,'.');

matlab/check_list_of_variables.m

@@ -46,7 +46,7 @@ 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
@@ -55,7 +55,7 @@ if options_.dsge_var && options_.bayesian_irf
             skipline()
         end
     end
-    varlist = options_.varobs;
+    varlist = char(options_.varobs);
     return
 end
 
@@ -128,7 +128,7 @@ elseif isempty(varlist) && isempty(options_.endo_vars_for_moment_computations_in
                 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
@@ -138,15 +138,14 @@ elseif isempty(varlist) && isempty(options_.endo_vars_for_moment_computations_in
                 end
             end
         end
+        if isnan(varlist)
+            edit([M_.fname '.mod'])
+        end
+        skipline()
     end
-    if isnan(varlist)
-        edit([M_.fname '.mod'])
-    end
-    skipline()
 end
 
 
-
 function format_text(remain, max_number_of_words_per_line)
 index = 0;
 line_of_text = [];

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/display_estimation_results_table.m

@@ -105,7 +105,7 @@ if nvx
     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)};
         if strcmp(field_name,'posterior')           
             fprintf('%-*s %7.3f %8.4f %7.4f %4s %6.4f \n', ...
                      header_width,name,bayestopt_.p1(ip), ...
@@ -276,7 +276,7 @@ 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,:)), ...
@@ -469,7 +469,7 @@ elseif all(bayestopt_.pshape == 0) && options_.TeX %% MLE and GMM Latex output
         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,'$%s$ & %8.4f & %7.4f & %7.4f \\\\ \n',...
                     deblank(M_.endo_names_tex(idx,:)), ...
                     xparam1(ip),...

matlab/dsge_likelihood.m

@@ -1,4 +1,4 @@
-function [fval,DLIK,Hess,exit_flag,ys,trend_coeff,info,Model,DynareOptions,BayesInfo,DynareResults] = dsge_likelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults,derivatives_info)
+function [fval,DLIK,Hess,exit_flag,ys,trend_coeff,info,Model,DynareOptions,BayesInfo,DynareResults] = dsge_likelihood(xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults,derivatives_info)
 % Evaluates the posterior kernel of a dsge model using the specified
 % kalman_algo; the resulting posterior includes the 2*pi constant of the
 % likelihood function
@@ -295,7 +295,7 @@ BayesInfo.mf = BayesInfo.mf1;
 
 % Define the constant vector of the measurement equation.
 if DynareOptions.noconstant
-    constant = zeros(DynareDataset.info.nvobs,1);
+    constant = zeros(DynareDataset.vobs,1);
 else
     if DynareOptions.loglinear
         constant = log(SteadyState(BayesInfo.mfys));
@@ -306,28 +306,28 @@ end
 
 % Define the deterministic linear trend of the measurement equation.
 if BayesInfo.with_trend
-    trend_coeff = zeros(DynareDataset.info.nvobs,1);
+    trend_coeff = zeros(DynareDataset.vobs,1);
     t = DynareOptions.trend_coeffs;
     for i=1:length(t)
         if ~isempty(t{i})
             trend_coeff(i) = evalin('base',t{i});
         end
     end
-    trend = repmat(constant,1,DynareDataset.info.ntobs)+trend_coeff*[1:DynareDataset.info.ntobs];
+    trend = repmat(constant,1,DynareDataset.nobs)+trend_coeff*[1:DynareDataset.nobs];
 else
-    trend = repmat(constant,1,DynareDataset.info.ntobs);
+    trend = repmat(constant,1,DynareDataset.nobs);
 end
 
 % Get needed informations for kalman filter routines.
 start = DynareOptions.presample+1;
-Z = BayesInfo.mf; % old mf
+Z = BayesInfo.mf;
-no_missing_data_flag = ~DynareDataset.missing.state;
+no_missing_data_flag = ~DatasetInfo.missing.state;
-mm = length(T); % old np
+mm = length(T);
-pp = DynareDataset.info.nvobs;
+pp = DynareDataset.vobs;
 rr = length(Q);
 kalman_tol = DynareOptions.kalman_tol;
 riccati_tol = DynareOptions.riccati_tol;
-Y   = DynareDataset.data-trend;
+Y = transpose(DynareDataset.data)-trend;
 
 %------------------------------------------------------------------------------
 % 3. Initial condition of the Kalman filter
@@ -406,7 +406,7 @@ switch DynareOptions.lik_init
                                                        kalman_tol, riccati_tol, DynareOptions.presample, ...
                                                        T,R,Q,H,Z,mm,pp,rr);
         else
-            [dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations, ...
+            [dLIK,dlik,a,Pstar] = missing_observations_kalman_filter_d(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations, ...
                                                               Y, 1, size(Y,2), ...
                                                               zeros(mm,1), Pinf, Pstar, ...
                                                               kalman_tol, riccati_tol, DynareOptions.presample, ...
@@ -441,9 +441,9 @@ switch DynareOptions.lik_init
         % no need to test again for correlation elements
         correlated_errors_have_been_checked = 1;
 
-        [dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DynareDataset.missing.aindex,...
+        [dLIK,dlik,a,Pstar] = univariate_kalman_filter_d(DatasetInfo.missing.aindex,...
-                                                        DynareDataset.missing.number_of_observations,...
+                                                        DatasetInfo.missing.number_of_observations,...
-                                                        DynareDataset.missing.no_more_missing_observations, ...
+                                                        DatasetInfo.missing.no_more_missing_observations, ...
                                                         Y, 1, size(Y,2), ...
                                                         zeros(mmm,1), Pinf, Pstar, ...
                                                         kalman_tol, riccati_tol, DynareOptions.presample, ...
@@ -523,7 +523,7 @@ if analytic_derivation,
     DLIK = [];
     AHess = [];
     iv = DynareResults.dr.restrict_var_list;
-    if nargin<8 || isempty(derivatives_info)
+    if nargin<9 || isempty(derivatives_info)
         [A,B,nou,nou,Model,DynareOptions,DynareResults] = dynare_resolve(Model,DynareOptions,DynareResults);
         if ~isempty(EstimatedParameters.var_exo)
             indexo=EstimatedParameters.var_exo(:,1);
@@ -655,10 +655,10 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
         end
     else
         if 0 %DynareOptions.block
-            [err, LIK,lik] = block_kalman_filter(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations,...
+            [err, LIK,lik] = block_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,...
                                                                  T,R,Q,H,Pstar,Y,start,Z,kalman_tol,riccati_tol, Model.nz_state_var, Model.n_diag, Model.nobs_non_statevar);
         else
-            [LIK,lik] = missing_observations_kalman_filter(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
+            [LIK,lik] = missing_observations_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
                                                a, Pstar, ...
                                                kalman_tol, DynareOptions.riccati_tol, ...
                                                DynareOptions.presample, ...
@@ -733,7 +733,7 @@ if (kalman_algo==2) || (kalman_algo==4)
         end
     end
 
-    [LIK, lik] = univariate_kalman_filter(DynareDataset.missing.aindex,DynareDataset.missing.number_of_observations,DynareDataset.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
+    [LIK, lik] = univariate_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
                                        a,Pstar, ...
                                        DynareOptions.kalman_tol, ...
                                        DynareOptions.riccati_tol, ...

matlab/dsge_var_likelihood.m

@@ -1,4 +1,4 @@
-function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function [fval,grad,hess,exit_flag,info,PHI,SIGMAu,iXX,prior] = dsge_var_likelihood(xparam1,DynareDataset,DynareInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
 % Evaluates the posterior kernel of the bvar-dsge model.
 %
 % INPUTS
@@ -55,7 +55,11 @@ end
 nx = EstimatedParameters.nvx + EstimatedParameters.np;
 
 % Get the number of observed variables in the VAR model.
-NumberOfObservedVariables = DynareDataset.info.nvobs;
+NumberOfObservedVariables = DynareDataset.vobs;
+
+% Get the number of observations.
+NumberOfObservations = DynareDataset.nobs;
+
 
 % Get the number of lags in the VAR model.
 NumberOfLags = DynareOptions.dsge_varlag;
@@ -110,8 +114,8 @@ Model.Sigma_e = Q;
 dsge_prior_weight = Model.params(dsge_prior_weight_idx);
 
 % Is the dsge prior proper?
-if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/DynareDataset.info.ntobs;
+if dsge_prior_weight<(NumberOfParameters+NumberOfObservedVariables)/NumberOfObservations;
-    fval = objective_function_penalty_base+abs(DynareDataset.info.ntobs*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
+    fval = objective_function_penalty_base+abs(NumberOfObservations*dsge_prior_weight-(NumberOfParameters+NumberOfObservedVariables));
     exit_flag = 0;
     info = 51;
     return
@@ -197,9 +201,9 @@ assignin('base','GXX',GXX);
 assignin('base','GYX',GYX);
 
 if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is finite.
-    tmp0 = dsge_prior_weight*DynareDataset.info.ntobs*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
+    tmp0 = dsge_prior_weight*NumberOfObservations*TheoreticalAutoCovarianceOfTheObservedVariables(:,:,1) + mYY ;
-    tmp1 = dsge_prior_weight*DynareDataset.info.ntobs*GYX + mYX;
+    tmp1 = dsge_prior_weight*NumberOfObservations*GYX + mYX;
-    tmp2 = inv(dsge_prior_weight*DynareDataset.info.ntobs*GXX+mXX);
+    tmp2 = inv(dsge_prior_weight*NumberOfObservations*GXX+mXX);
     SIGMAu = tmp0 - tmp1*tmp2*tmp1'; clear('tmp0');
     [SIGMAu_is_positive_definite, penalty] = ispd(SIGMAu);
     if ~SIGMAu_is_positive_definite
@@ -208,28 +212,28 @@ if ~isinf(dsge_prior_weight)% Evaluation of the likelihood of the dsge-var model
         exit_flag = 0;
         return;
     end
-    SIGMAu = SIGMAu / (DynareDataset.info.ntobs*(1+dsge_prior_weight));
+    SIGMAu = SIGMAu / (NumberOfObservations*(1+dsge_prior_weight));
     PHI = tmp2*tmp1'; clear('tmp1');
-    prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*DynareDataset.info.ntobs- ...
+    prodlng1 = sum(gammaln(.5*((1+dsge_prior_weight)*NumberOfObservations- ...
                                NumberOfObservedVariables*NumberOfLags ...
                                +1-(1:NumberOfObservedVariables)')));
-    prodlng2 = sum(gammaln(.5*(dsge_prior_weight*DynareDataset.info.ntobs- ...
+    prodlng2 = sum(gammaln(.5*(dsge_prior_weight*NumberOfObservations- ...
                                NumberOfObservedVariables*NumberOfLags ...
                                +1-(1:NumberOfObservedVariables)')));
-    lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*DynareDataset.info.ntobs*GXX+mXX)) ...
+    lik = .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX+mXX)) ...
-          + .5*((dsge_prior_weight+1)*DynareDataset.info.ntobs-NumberOfParameters)*log(det((dsge_prior_weight+1)*DynareDataset.info.ntobs*SIGMAu)) ...
+          + .5*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters)*log(det((dsge_prior_weight+1)*NumberOfObservations*SIGMAu)) ...
-          - .5*NumberOfObservedVariables*log(det(dsge_prior_weight*DynareDataset.info.ntobs*GXX)) ...
+          - .5*NumberOfObservedVariables*log(det(dsge_prior_weight*NumberOfObservations*GXX)) ...
-          - .5*(dsge_prior_weight*DynareDataset.info.ntobs-NumberOfParameters)*log(det(dsge_prior_weight*DynareDataset.info.ntobs*(GYY-GYX*inv(GXX)*GYX'))) ...
+          - .5*(dsge_prior_weight*NumberOfObservations-NumberOfParameters)*log(det(dsge_prior_weight*NumberOfObservations*(GYY-GYX*inv(GXX)*GYX'))) ...
-          + .5*NumberOfObservedVariables*DynareDataset.info.ntobs*log(2*pi)  ...
+          + .5*NumberOfObservedVariables*NumberOfObservations*log(2*pi)  ...
-          - .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*DynareDataset.info.ntobs-NumberOfParameters) ...
+          - .5*log(2)*NumberOfObservedVariables*((dsge_prior_weight+1)*NumberOfObservations-NumberOfParameters) ...
-          + .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*DynareDataset.info.ntobs-NumberOfParameters) ...
+          + .5*log(2)*NumberOfObservedVariables*(dsge_prior_weight*NumberOfObservations-NumberOfParameters) ...
           - prodlng1 + prodlng2;
 else% Evaluation of the likelihood of the dsge-var model when the dsge prior weight is infinite.
     iGXX = inv(GXX);
     SIGMAu = GYY - GYX*iGXX*transpose(GYX);
     PHI = iGXX*transpose(GYX);
-    lik = DynareDataset.info.ntobs * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) +  ...
+    lik = NumberOfObservations * ( log(det(SIGMAu)) + NumberOfObservedVariables*log(2*pi) +  ...
-                   trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/DynareDataset.info.ntobs));
+                   trace(inv(SIGMAu)*(mYY - transpose(mYX*PHI) - mYX*PHI + transpose(PHI)*mXX*PHI)/NumberOfObservations));
     lik = .5*lik;% Minus likelihood
 end
 
@@ -254,7 +258,7 @@ if (nargout==9)
     iGXX = inv(GXX);
     prior.SIGMAstar = GYY - GYX*iGXX*GYX';
     prior.PHIstar = iGXX*transpose(GYX);
-    prior.ArtificialSampleSize = fix(dsge_prior_weight*DynareDataset.info.ntobs);
+    prior.ArtificialSampleSize = fix(dsge_prior_weight*NumberOfObservations);
     prior.DF = prior.ArtificialSampleSize - NumberOfParameters - NumberOfObservedVariables;
     prior.iGXX = iGXX;
 end

matlab/dsgevar_posterior_density.m

@@ -1,4 +1,4 @@
-function bvar = dsgevar_posterior_density(deep,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function bvar = dsgevar_posterior_density(deep,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
 % This function characterizes the posterior distribution of a bvar with
 % a dsge prior (as in Del Negro and Schorfheide 2003) for a given value
 % of the deep parameters (structural parameters + the size of the
@@ -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
@@ -47,7 +47,7 @@ if ~options_.noconstant
         bvar.NumberOfVariables;
 end
 
-[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] =  dsge_var_likelihood(deep',DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+[fval,cost_flag,info,PHI,SIGMAu,iXX,prior] =  dsge_var_likelihood(deep',DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
 
 % Conditionnal posterior density of the lagged matrices (given Sigma) ->
 % Matric-variate normal distribution.

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)
+        for i=1:length(var_obs)
-            tmp = strmatch(var_obs(i,:),endo_names(i_var,:),'exact');
+            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

@@ -105,7 +105,7 @@ 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;
@@ -125,7 +125,7 @@ if nnobs > 1 && horizon > 0
 
     IdObs    = zeros(n_varobs,1);
     for j=1:n_varobs
-        iobs = strmatch(options_.varobs(j,:),var_list,'exact');
+        iobs = strmatch(options_.varobs{j},var_list,'exact');
         if ~isempty(iobs)
             IdObs(j,1) = iobs;
         end

matlab/dynare_estimation_1.m

@@ -29,7 +29,7 @@ function dynare_estimation_1(var_list_,dname)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-global M_ options_ oo_ estim_params_ bayestopt_ dataset_
+global M_ options_ oo_ estim_params_ bayestopt_ dataset_ dataset_info
 
 % Set particle filter flag.
 if options_.order > 1
@@ -78,10 +78,20 @@ else
     objective_function = str2func('dsge_var_likelihood');
 end
 
-[dataset_,xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_] = dynare_estimation_init(var_list_, dname, [], M_, options_, oo_, estim_params_, bayestopt_);
+[dataset_,dataset_info, xparam1, hh, M_, options_, oo_, estim_params_, bayestopt_] = ...
+    dynare_estimation_init(var_list_, dname, [], M_, options_, oo_, estim_params_, bayestopt_);
 
 if options_.dsge_var
     check_dsge_var_model(M_, estim_params_, bayestopt_);
+    if dataset_info.missing.state
+        error('Estimation::DsgeVarLikelihood: I cannot estimate a DSGE-VAR model with missing observations!')
+    end
+    if options_.noconstant
+        var_sample_moments(options_.dsge_varlag, -1, dataset_);
+    else
+        % The steady state is non zero ==> a constant in the VAR is needed!
+        var_sample_moments(options_.dsge_varlag, 0, dataset_);
+    end
 end
 
 %check for calibrated covariances before updating parameters
@@ -114,14 +124,14 @@ if estim_params_.ncx || any(nnz(tril(M_.Correlation_matrix,-1))) || isfield(esti
 end
 
 data = dataset_.data;
-rawdata = dataset_.rawdata;
+rawdata = dataset_.data;
-data_index = dataset_.missing.aindex;
+data_index = dataset_info.missing.aindex;
-missing_value = dataset_.missing.state;
+missing_value = dataset_info.missing.state;
 
 % Set number of observations
-gend = options_.nobs;
+gend = dataset_.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).
@@ -154,29 +164,10 @@ if ~isempty(estim_params_)
     M_ = set_all_parameters(xparam1,estim_params_,M_);
 end
 
-% compute sample moments if needed (bvar-dsge)
-if options_.dsge_var
-    if dataset_.missing.state
-        error('I cannot estimate a DSGE-VAR model with missing observations!')
-    end
-    if options_.noconstant
-        evalin('base',...
-               ['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
-                'var_sample_moments(options_.first_obs,' ...
-                'options_.first_obs+options_.nobs-1,options_.dsge_varlag,-1,' ...
-                'options_.datafile, options_.varobs,options_.xls_sheet,options_.xls_range);'])
-    else% The steady state is non zero ==> a constant in the VAR is needed!
-        evalin('base',['[mYY,mXY,mYX,mXX,Ydata,Xdata] = ' ...
-                       'var_sample_moments(options_.first_obs,' ...
-                       'options_.first_obs+options_.nobs-1,options_.dsge_varlag,0,' ...
-                       'options_.datafile, options_.varobs,options_.xls_sheet,options_.xls_range);'])
-    end
-end
-
 
 %% perform initial estimation checks;
 try
-    oo_ = initial_estimation_checks(objective_function,xparam1,dataset_,M_,estim_params_,options_,bayestopt_,oo_);
+    oo_ = initial_estimation_checks(objective_function,xparam1,dataset_,dataset_info,M_,estim_params_,options_,bayestopt_,oo_);
 catch % if check fails, provide info on using calibration if present
     e = lasterror();
     if full_calibration_detected %calibrated model present and no explicit starting values
@@ -246,7 +237,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
             optim_options = optimset(optim_options,'GradObj','on','TolX',1e-7);
         end
         [xparam1,fval,exitflag,output,lamdba,grad,hessian_fmincon] = ...
-            fmincon(objective_function,xparam1,[],[],[],[],lb,ub,[],optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+            fmincon(objective_function,xparam1,[],[],[],[],lb,ub,[],optim_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
       case 2
         error('ESTIMATION: mode_compute=2 option (Lester Ingber''s Adaptive Simulated Annealing) is no longer available')
       case 3
@@ -264,10 +255,10 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
             optim_options = optimset(optim_options,'GradObj','on');
         end
         if ~isoctave
-            [xparam1,fval,exitflag] = fminunc(objective_function,xparam1,optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+            [xparam1,fval,exitflag] = fminunc(objective_function,xparam1,optim_options,dataset_,dataset_info_,options_,M_,estim_params_,bayestopt_,oo_);
         else
             % Under Octave, use a wrapper, since fminunc() does not have a 4th arg
-            func = @(x) objective_function(x, dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+            func = @(x) objective_function(x, dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
             [xparam1,fval,exitflag] = fminunc(func,xparam1,optim_options);
         end
       case 4
@@ -306,7 +297,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
         end
         % Call csminwell.
         [fval,xparam1,grad,hessian_csminwel,itct,fcount,retcodehat] = ...
-            csminwel1(objective_function, xparam1, H0, analytic_grad, crit, nit, numgrad, epsilon, dataset_, options_, M_, estim_params_, bayestopt_, oo_);
+            csminwel1(objective_function, xparam1, H0, analytic_grad, crit, nit, numgrad, epsilon, dataset_, dataset_info, options_, M_, estim_params_, bayestopt_, oo_);
         % Disp value at the mode.
         disp(sprintf('Objective function at mode: %f',fval))
       case 5
@@ -334,7 +325,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
         else
             nit=1000;
         end
-        [xparam1,hh,gg,fval,invhess] = newrat(objective_function,xparam1,analytic_grad,crit,nit,flag,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+        [xparam1,hh,gg,fval,invhess] = newrat(objective_function,xparam1,analytic_grad,crit,nit,flag,dataset_, dataset_info, options_,M_,estim_params_,bayestopt_,oo_);
         if options_.analytic_derivation,
             options_.analytic_derivation = ana_deriv;
         end
@@ -384,7 +375,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
             end
         end
         % Evaluate the objective function.
-        fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+        fval = feval(objective_function,xparam1,dataset_, dataset_info, options_,M_,estim_params_,bayestopt_,oo_);
         OldMode = fval;
         if ~exist('MeanPar','var')
             MeanPar = xparam1;
@@ -416,8 +407,8 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
                     flag = 'LastCall';
                 end
                 [xparam1,PostVar,Scale,PostMean] = ...
-                    gmhmaxlik(objective_function,xparam1,[lb ub],gmhmaxlikOptions,Scale,flag,MeanPar,CovJump,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+                    gmhmaxlik(objective_function,xparam1,[lb ub],gmhmaxlikOptions,Scale,flag,MeanPar,CovJump,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
-                fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+                fval = feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
                 options_.mh_jscale = Scale;
                 mouvement = max(max(abs(PostVar-OldPostVar)));
                 skipline()
@@ -436,11 +427,11 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
                 end
                 [xparam1,PostVar,Scale,PostMean] = ...
                     gmhmaxlik(objective_function,xparam1,[lb ub],...
-                              gmhmaxlikOptions,Scale,flag,PostMean,PostVar,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+                              gmhmaxlikOptions,Scale,flag,PostMean,PostVar,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
-                fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+                fval = feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
                 options_.mh_jscale = Scale;
                 mouvement = max(max(abs(PostVar-OldPostVar)));
-                fval = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+                fval = feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
                 skipline()
                 disp('========================================================== ')
                 disp(['   Change in the covariance matrix = ' num2str(mouvement) '.'])
@@ -473,7 +464,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
         if isfield(options_,'optim_opt')
             eval(['optim_options = optimset(optim_options,' options_.optim_opt ');']);
         end
-        [xparam1,fval,exitflag] = fminsearch(objective_function,xparam1,optim_options,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+        [xparam1,fval,exitflag] = fminsearch(objective_function,xparam1,optim_options,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
       case 8
         % Dynare implementation of the simplex algorithm.
         simplexOptions = options_.simplex;
@@ -498,7 +489,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
                 end
             end
         end
-        [xparam1,fval,exitflag] = simplex_optimization_routine(objective_function,xparam1,simplexOptions,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+        [xparam1,fval,exitflag] = simplex_optimization_routine(objective_function,xparam1,simplexOptions,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
       case 9
         % Set defaults
         H0 = 1e-4*ones(nx,1);
@@ -523,7 +514,7 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
         end
         warning('off','CMAES:NonfinitenessRange');
         warning('off','CMAES:InitialSigma');
-        [x, fval, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = cmaes(func2str(objective_function),xparam1,H0,cmaesOptions,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+        [x, fval, COUNTEVAL, STOPFLAG, OUT, BESTEVER] = cmaes(func2str(objective_function),xparam1,H0,cmaesOptions,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
         xparam1=BESTEVER.x;
         disp(sprintf('\n Objective function at mode: %f',fval))
       case 10
@@ -554,16 +545,16 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
         end
         simpsaOptionsList = options2cell(simpsaOptions);
         simpsaOptions = simpsaset(simpsaOptionsList{:});
-        [xparam1, fval, exitflag] = simpsa(func2str(objective_function),xparam1,lb,ub,simpsaOptions,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+        [xparam1, fval, exitflag] = simpsa(func2str(objective_function),xparam1,lb,ub,simpsaOptions,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
       case 11
          options_.cova_compute = 0 ;
-         [xparam1,stdh,lb_95,ub_95,med_param] = online_auxiliary_filter(xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_) ;
+         [xparam1,stdh,lb_95,ub_95,med_param] = online_auxiliary_filter(xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_) ;
       case 101
         myoptions=soptions;
         myoptions(2)=1e-6; %accuracy of argument
         myoptions(3)=1e-6; %accuracy of function (see Solvopt p.29)
         myoptions(5)= 1.0;
-        [xparam1,fval]=solvopt(xparam1,objective_function,[],myoptions,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+        [xparam1,fval]=solvopt(xparam1,objective_function,[],myoptions,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
       case 102
         %simulating annealing
         %        LB=zeros(size(xparam1))-20;
@@ -600,10 +591,10 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
         disp(['c vector   ' num2str(c')]);
 
         [xparam1, fval, nacc, nfcnev, nobds, ier, t, vm] = sa(objective_function,xparam1,maxy,rt_,epsilon,ns,nt ...
-                                                              ,neps,maxevl,LB,UB,c,idisp ,t,vm,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+                                                              ,neps,maxevl,LB,UB,c,idisp ,t,vm,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
       otherwise
         if ischar(options_.mode_compute)
-            [xparam1, fval, retcode ] = feval(options_.mode_compute,objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+            [xparam1, fval, retcode ] = feval(options_.mode_compute,objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
         else
             error(['dynare_estimation:: mode_compute = ' int2str(options_.mode_compute) ' option is unknown!'])
         end
@@ -614,11 +605,11 @@ if ~isequal(options_.mode_compute,0) && ~options_.mh_posterior_mode_estimation
                 ana_deriv = options_.analytic_derivation;
                 options_.analytic_derivation = 2;
                 [junk1, junk2, hh] = feval(objective_function,xparam1, ...
-                    dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+                    dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
                 options_.analytic_derivation = ana_deriv;
             else
                 hh = reshape(hessian(objective_function,xparam1, ...
-                    options_.gstep,dataset_,options_,M_,estim_params_,bayestopt_,oo_),nx,nx);
+                    options_.gstep,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_),nx,nx);
             end
         end
     end
@@ -698,7 +689,7 @@ end
 if options_.mode_check.status == 1 && ~options_.mh_posterior_mode_estimation
     ana_deriv = options_.analytic_derivation;
     options_.analytic_derivation = 0;
-    mode_check(objective_function,xparam1,hh,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+    mode_check(objective_function,xparam1,hh,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
     options_.analytic_derivation = ana_deriv;
 end
 
@@ -725,14 +716,14 @@ if ~options_.cova_compute
 end
 
 if any(bayestopt_.pshape > 0) && ~options_.mh_posterior_mode_estimation
-    %% display results table and store parameter estimates and standard errors in results
+    % display results table and store parameter estimates and standard errors in results
     oo_=display_estimation_results_table(xparam1,stdh,M_,options_,estim_params_,bayestopt_,oo_,pnames,'Posterior','posterior');
-    %% Laplace approximation to the marginal log density:
+    % Laplace approximation to the marginal log density:
     if options_.cova_compute
         estim_params_nbr = size(xparam1,1);
         scale_factor = -sum(log10(diag(invhess)));
         log_det_invhess = -estim_params_nbr*log(scale_factor)+log(det(scale_factor*invhess));
-        likelihood = feval(objective_function,xparam1,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+        likelihood = feval(objective_function,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
         oo_.MarginalDensity.LaplaceApproximation = .5*estim_params_nbr*log(2*pi) + .5*log_det_invhess - likelihood;
         skipline()
         disp(sprintf('Log data density [Laplace approximation] is %f.',oo_.MarginalDensity.LaplaceApproximation))
@@ -779,7 +770,7 @@ if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
         ana_deriv = options_.analytic_derivation;
         options_.analytic_derivation = 0;
         if options_.cova_compute
-            feval(options_.posterior_sampling_method,objective_function,options_.proposal_distribution,xparam1,invhess,bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+            feval(options_.posterior_sampling_method,objective_function,options_.proposal_distribution,xparam1,invhess,bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
         else
             error('I Cannot start the MCMC because the Hessian of the posterior kernel at the mode was not computed.')
         end
@@ -827,7 +818,7 @@ if (any(bayestopt_.pshape  >0 ) && options_.mh_replic) || ...
             if error_flag
                 error('Estimation::mcmc: I cannot compute the posterior statistics!')
             end
-            prior_posterior_statistics('posterior',dataset_);
+            prior_posterior_statistics('posterior',dataset_,dataset_info);
         end
         xparam = get_posterior_parameters('mean');
         M_ = set_all_parameters(xparam,estim_params_,M_);
@@ -842,7 +833,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
                                                       > 0) && options_.load_mh_file)) ...
     || ~options_.smoother ) && options_.partial_information == 0  % to be fixed
     %% ML estimation, or posterior mode without metropolis-hastings or metropolis without bayesian smooth variable
-    [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,dataset_.info.ntobs,dataset_.data,dataset_.missing.aindex,dataset_.missing.state);
+    [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,dataset_.nobs,transpose(dataset_.data),dataset_info.missing.aindex,dataset_info.missing.state);
     oo_.Smoother.SteadyState = ys;
     oo_.Smoother.TrendCoeffs = trend_coeff;
     oo_.Smoother.Variance = P;
@@ -949,8 +940,11 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
     %%  Smooth observational errors...
     %%
     if options_.prefilter == 1
-        yf = atT(bayestopt_.mf,:)+repmat(dataset_.descriptive.mean',1,gend);
+        yf = atT(bayestopt_.mf,:)+repmat(dataset_info.descriptive.mean',1,gend);
     elseif options_.loglinear == 1
+        gend
+        bayestopt_.mfys
+        ys
         yf = atT(bayestopt_.mf,:)+repmat(log(ys(bayestopt_.mfys)),1,gend)+...
              trend_coeff*[1:gend];
     else
@@ -965,8 +959,7 @@ 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,:)) ...
+            eval(['oo_.SmoothedMeasurementErrors.' options_.varobs{i} ' = measurement_error(i,:)'';']);
-                  ' = measurement_error(i,:)'';']);
         end
         if ~options_.nograph
             [nbplt,nr,nc,lr,lc,nstar] = pltorg(number_of_plots_to_draw);
@@ -995,7 +988,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
                     hold on
                     plot(1:gend,measurement_error(index(k),:),marker_string{2,1},'linewidth',1)
                     hold off
-                    name = deblank(options_.varobs(index(k),:));
+                    name = options_.varobs{index(k)};
                     if gend>1
                         xlim([1 gend])
                     end
@@ -1009,7 +1002,7 @@ 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) ' $'];
@@ -1070,7 +1063,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
             hold on
             plot(1:gend,rawdata(:,k),marker_string{2,1},'linewidth',1)
             hold off
-            name = deblank(options_.varobs(k,:));
+            name = options_.varobs{k};
             if isempty(NAMES)
                 NAMES = name;
             else
@@ -1084,7 +1077,7 @@ if (~((any(bayestopt_.pshape > 0) && options_.mh_replic) || (any(bayestopt_.psha
                 xlim([1 gend])
             end
             if options_.TeX
-                idx = strmatch(options_.varobs(k,:),M_.endo_names,'exact');
+                idx = strmatch(options_.varobs{k},M_.endo_names,'exact');
                 texname = M_.endo_names_tex(idx,:);
                 if isempty(TeXNAMES)
                     TeXNAMES = ['$ ' deblank(texname) ' $'];

matlab/dynare_estimation_init.m

@@ -1,4 +1,4 @@
-function [dataset_, xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_, fake] = dynare_estimation_init(var_list_, dname, gsa_flag, M_, options_, oo_, estim_params_, bayestopt_)
+function [dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_, fake] = dynare_estimation_init(var_list_, dname, gsa_flag, M_, options_, oo_, estim_params_, bayestopt_)
 
 % function dynare_estimation_init(var_list_, gsa_flag)
 % preforms initialization tasks before estimation or
@@ -41,47 +41,61 @@ hh = [];
 
 if isempty(gsa_flag)
     gsa_flag = 0;
-else% Decide if a DSGE or DSGE-VAR has to be estimated.
+else
+    % Decide if a DSGE or DSGE-VAR has to be estimated.
     if ~isempty(strmatch('dsge_prior_weight',M_.param_names))
         options_.dsge_var = 1;
     end
+    % Get the list of the endogenous variables for which posterior statistics wil be computed
     var_list_ = check_list_of_variables(options_, M_, var_list_);
     options_.varlist = var_list_;
 end
 
-% Get the indices of the observed variables in M_.endo_names.
+% Set the number of observed variables.
-options_.lgyidx2varobs = zeros(size(M_.endo_names,1),1);
+options_.number_of_observed_variables = length(options_.varobs);
-for i = 1:size(M_.endo_names,1)
+
-    tmp = strmatch(deblank(M_.endo_names(i,:)),options_.varobs,'exact');
+% Test if observed variables are declared.
-    if ~isempty(tmp)
+if ~options_.number_of_observed_variables
-        if length(tmp)>1
+    error('VAROBS is missing!')
-            skipline()
+end
-            error(['Multiple declarations of ' deblank(M_.endo_names(i,:)) ' as an observed variable is not allowed!'])
+
-        end
+% Check that each declared observed variable is also an endogenous variable.
-        options_.lgyidx2varobs(i) = tmp;
+for i = 1:options_.number_of_observed_variables
+    id = strmatch(options_.varobs{i}, M_.endo_names, 'exact');
+    if isempty(id)
+        error(['Unknown variable (' options_.varobs{i} ')!'])
     end
 end
 
+% Check that a variable is not declared as observed more than once.
+if ~isequal(options_.varobs,unique(options_.varobs))
+    for i = 1:options_.number_of_observed_variables
+        if length(strmatch(options_.varobs{i},options_.varobs))>1
+            error(['A variable cannot be declared as observed more than once (' options_.varobs{i} ')!'])
+        end
+    end
+end
+
+% Check the perturbation order (nonlinear filters with third order perturbation, or higher order, are not yet implemented).
 if options_.order>2
     error(['I cannot estimate a model with a ' int2str(options_.order) ' order approximation of the model!'])
 end
 
-% Set options_.lik_init equal to 3 if diffuse filter is used or
+% Set options_.lik_init equal to 3 if diffuse filter is used or kalman_algo refers to a diffuse filter algorithm.
-% kalman_algo refers to a diffuse filter algorithm.
+if isequal(options_.diffuse_filter,1) || (options_.kalman_algo>2)
-if (options_.diffuse_filter==1) || (options_.kalman_algo > 2)
+    if isequal(options_.lik_init,2)
-    if 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
 end
 
 % If options_.lik_init == 1
-%  set by default options_.qz_criterium to 1-1e-6
+%     set by default options_.qz_criterium to 1-1e-6
-%  and check options_.qz_criterium < 1-eps if options_.lik_init == 1
+%     and check options_.qz_criterium < 1-eps if options_.lik_init == 1
-% Else set by default options_.qz_criterium to 1+1e-6
+% Else
-if options_.lik_init == 1
+%     set by default options_.qz_criterium to 1+1e-6
+if isequal(options_.lik_init,1)
     if isempty(options_.qz_criterium)
         options_.qz_criterium = 1-1e-6;
     elseif options_.qz_criterium > 1-eps
@@ -111,9 +125,6 @@ else
     M_.dname = dname;
 end
 
-% Set the number of observed variables.
-n_varobs = size(options_.varobs,1);
-
 % Set priors over the estimated parameters.
 if ~isempty(estim_params_)
     [xparam1,estim_params_,bayestopt_,lb,ub,M_] = set_prior(estim_params_,M_,options_);
@@ -297,7 +308,10 @@ end
 
 % storing prior parameters in results
 oo_.prior.mean = bayestopt_.p1;
+oo_.prior.mode = bayestopt_.p5;
 oo_.prior.variance = diag(bayestopt_.p2.^2);
+oo_.prior.hyperparameters.first = bayestopt_.p6;
+oo_.prior.hyperparameters.second = bayestopt_.p7;
 
 % Is there a linear trend in the measurement equation?
 if ~isfield(options_,'trend_coeffs') % No!
@@ -307,7 +321,7 @@ else% Yes!
     bayestopt_.trend_coeff = {};
     trend_coeffs = options_.trend_coeffs;
     nt = length(trend_coeffs);
-    for i=1:n_varobs
+    for i=1:options_.number_of_observed_variables
         if i > length(trend_coeffs)
             bayestopt_.trend_coeff{i} = '0';
         else
@@ -326,17 +340,12 @@ nstatic = M_.nstatic;          % Number of static variables.
 npred = M_.nspred;             % Number of predetermined variables.
 nspred = M_.nspred;            % Number of predetermined variables in the state equation.
 
-% Test if observed variables are declared.
-if isempty(options_.varobs)
-    error('VAROBS is missing')
-end
-
 % Setting resticted state space (observed + predetermined variables)
 var_obs_index = [];
 k1 = [];
-for i=1:n_varobs
+for i=1:options_.number_of_observed_variables
-    var_obs_index = [var_obs_index; strmatch(deblank(options_.varobs(i,:)),M_.endo_names(dr.order_var,:),'exact')];
+    var_obs_index = [var_obs_index; strmatch(options_.varobs{i},M_.endo_names(dr.order_var,:),'exact')];
-    k1 = [k1; strmatch(deblank(options_.varobs(i,:)),M_.endo_names, 'exact')];
+    k1 = [k1; strmatch(options_.varobs{i},M_.endo_names, 'exact')];
 end
 
 % Define union of observed and state variables
@@ -359,10 +368,8 @@ k3 = [];
 k3p = [];
 if options_.selected_variables_only
     for i=1:size(var_list_,1)
-        k3 = [k3; strmatch(var_list_(i,:),M_.endo_names(dr.order_var,:), ...
+        k3 = [k3; strmatch(var_list_(i,:),M_.endo_names(dr.order_var,:), 'exact')];
-                           'exact')];
+        k3p = [k3; strmatch(var_list_(i,:),M_.endo_names, 'exact')];
-        k3p = [k3; strmatch(var_list_(i,:),M_.endo_names, ...
-                           'exact')];
     end
 else
     k3 = (1:M_.endo_nbr)';
@@ -383,14 +390,12 @@ if options_.block == 1
     bayestopt_.mf2  = var_obs_index;
     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_restrict_columns = ic;
-    [junk,bayestopt_.smoother_mf] = ismember(k1, ...
+    [junk,bayestopt_.smoother_mf] = ismember(k1, bayestopt_.smoother_var_list);
-                                             bayestopt_.smoother_var_list);
 else
     k2 = union(var_obs_index,[M_.nstatic+1:M_.nstatic+M_.nspred]', 'rows');
     % Set restrict_state to postion of observed + state variables in expanded state vector.
@@ -404,13 +409,11 @@ else
     bayestopt_.mfys = k1;
     [junk,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_restrict_columns = ic;
-    [junk,bayestopt_.smoother_mf] = ismember(var_obs_index, ...
+    [junk,bayestopt_.smoother_mf] = ismember(var_obs_index, bayestopt_.smoother_var_list);
-                                             bayestopt_.smoother_var_list);
 end;
 
 if options_.analytic_derivation,
@@ -440,37 +443,12 @@ if options_.analytic_derivation,
     end
 end
 
-% Test if the data file is declared.
-if isempty(options_.datafile)
-    if gsa_flag
-        dataset_ = [];
-%         rawdata = [];
-%         data_info = [];
-        return
-    else
-        error('datafile option is missing')
-    end
-end
-
 % If jscale isn't specified for an estimated parameter, use global option options_.jscale, set to 0.2, by default.
 k = find(isnan(bayestopt_.jscale));
 bayestopt_.jscale(k) = options_.mh_jscale;
 
-% Load and transform data.
+% Build the dataset
-transformation = [];
+[dataset_, dataset_info] = makedataset(options_, options_.dsge_var*options_.dsge_varlag, gsa_flag);
-if options_.loglinear && ~options_.logdata
-    transformation = @log;
-end
-xls.sheet = options_.xls_sheet;
-xls.range = options_.xls_range;
-
-if ~isfield(options_,'nobs')
-    options_.nobs = [];
-end
-
-dataset_ = initialize_dataset(options_.datafile,options_.varobs,options_.first_obs,options_.nobs,transformation,options_.prefilter,xls);
-
-options_.nobs = dataset_.info.ntobs;
 
 % setting steadystate_check_flag option
 if options_.diffuse_filter
@@ -479,6 +457,7 @@ else
     steadystate_check_flag = 1;
 end
 
+% If the steady state of the observed variables is non zero, set noconstant equal 0 ()
 M = M_;
 nvx = estim_params_.nvx;
 ncx = estim_params_.ncx;
@@ -502,5 +481,4 @@ else
         disp('variables in the model block, or drop the prefiltering.')
         error('The option "prefilter" is inconsistent with the non-zero mean measurement equations in the model.')
     end
-end
+end
-

matlab/dynare_identification.m

@@ -128,11 +128,11 @@ options_ = set_default_option(options_,'datafile','');
 options_.mode_compute = 0;
 options_.plot_priors = 0;
 options_.smoother=1;
-[dataset_,xparam1,hh, M_, options_, oo_, estim_params_,bayestopt_]=dynare_estimation_init(M_.endo_names,fname_,1, M_, options_, oo_, estim_params_, bayestopt_);
+[dataset_,dataset_info,xparam1,hh, M_, options_, oo_, estim_params_,bayestopt_]=dynare_estimation_init(M_.endo_names,fname_,1, M_, options_, oo_, estim_params_, bayestopt_);
 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
@@ -278,7 +269,7 @@ if iload <=0,
         disp('Testing current parameter values')
     end
     [idehess_point, idemoments_point, idemodel_point, idelre_point, derivatives_info_point, info] = ...
-        identification_analysis(params,indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
+        identification_analysis(params,indx,indexo,options_ident,dataset_, dataset_info, prior_exist, name_tex,1);
     if info(1)~=0,
         skipline()
         disp('----------- ')
@@ -321,7 +312,7 @@ if iload <=0,
                 kk=kk+1;
                 params = prior_draw();
                 [idehess_point, idemoments_point, idemodel_point, idelre_point, derivatives_info_point, info] = ...
-                    identification_analysis(params,indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
+                    identification_analysis(params,indx,indexo,options_ident,dataset_,dataset_info, prior_exist, name_tex,1);
             end
         end
         if info(1)
@@ -375,7 +366,7 @@ if iload <=0,
             params = prior_draw();
         end
         [dum1, ideJ, ideH, ideGP, dum2 , info] = ...
-            identification_analysis(params,indx,indexo,options_MC,dataset_, prior_exist, name_tex,0);
+            identification_analysis(params,indx,indexo,options_MC,dataset_, dataset_info, prior_exist, name_tex,0);
         if iteration==0 && info(1)==0,
             MAX_tau   = min(SampleSize,ceil(MaxNumberOfBytes/(size(ideH.siH,1)*nparam)/8));
             stoH = zeros([size(ideH.siH,1),nparam,MAX_tau]);
@@ -546,7 +537,7 @@ if SampleSize > 1,
                 disp(['Testing ',tittxt, '. Press ENTER']), pause(5),
                 if ~iload,
                     [idehess_max, idemoments_max, idemodel_max, idelre_max, derivatives_info_max] = ...
-                        identification_analysis(pdraws(jmax,:),indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
+                        identification_analysis(pdraws(jmax,:),indx,indexo,options_ident,dataset_,dataset_info, prior_exist, name_tex,1);
                     save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idehess_max', 'idemoments_max','idemodel_max', 'idelre_max', 'jmax', '-append');
                 end
                 disp_identification(pdraws(jmax,:), idemodel_max, idemoments_max, name,1);
@@ -561,7 +552,7 @@ if SampleSize > 1,
                 disp(['Testing ',tittxt, '. Press ENTER']), pause(5),
                 if ~iload,
                     [idehess_min, idemoments_min, idemodel_min, idelre_min, derivatives_info_min] = ...
-                        identification_analysis(pdraws(jmin,:),indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
+                        identification_analysis(pdraws(jmin,:),indx,indexo,options_ident,dataset_, dataset_info, prior_exist, name_tex,1);
                     save([IdentifDirectoryName '/' M_.fname '_identif.mat'], 'idehess_min', 'idemoments_min','idemodel_min', 'idelre_min', 'jmin', '-append');
                 end
                 disp_identification(pdraws(jmin,:), idemodel_min, idemoments_min, name,1);
@@ -576,7 +567,7 @@ if SampleSize > 1,
                     disp(['Testing ',tittxt, '. Press ENTER']), pause(5),
                     if ~iload,
                         [idehess_(j), idemoments_(j), idemodel_(j), idelre_(j), derivatives_info_(j)] = ...
-                            identification_analysis(pdraws(jcrit(j),:),indx,indexo,options_ident,dataset_, prior_exist, name_tex,1);
+                            identification_analysis(pdraws(jcrit(j),:),indx,indexo,options_ident,dataset_, dataset_info, prior_exist, name_tex,1);
                     end
                     disp_identification(pdraws(jcrit(j),:), idemodel_(j), idemoments_(j), name,1);
                     close all,

matlab/dynare_sensitivity.m

@@ -84,7 +84,7 @@ if ~isempty(options_gsa.datafile) || isempty(bayestopt_) || options_gsa.rmse,
     options_.mode_compute = 0;
     options_.filtered_vars = 1;
     options_.plot_priors = 0;
-    [dataset_,xparam1,hh, M_, options_, oo_, estim_params_,bayestopt_]=dynare_estimation_init(M_.endo_names,fname_,1, M_, options_, oo_, estim_params_, bayestopt_);
+    [dataset_,dataset_info,xparam1,hh, M_, options_, oo_, estim_params_,bayestopt_]=dynare_estimation_init(M_.endo_names,fname_,1, M_, options_, oo_, estim_params_, bayestopt_);
     % computes a first linear solution to set up various variables
 else
     if isempty(options_.qz_criterium)
@@ -154,7 +154,7 @@ options_gsa = set_default_option(options_gsa,'namlagendo',[]);
 options_gsa = set_default_option(options_gsa,'namexo',[]);
 % RMSE mapping
 options_gsa = set_default_option(options_gsa,'lik_only',0);
-options_gsa = set_default_option(options_gsa,'var_rmse',options_.varobs);
+options_gsa = set_default_option(options_gsa,'var_rmse',char(options_.varobs));
 options_gsa = set_default_option(options_gsa,'pfilt_rmse',0.1);
 options_gsa = set_default_option(options_gsa,'istart_rmse',options_.presample+1);
 options_gsa = set_default_option(options_gsa,'alpha_rmse',0.001);
@@ -343,7 +343,7 @@ if options_gsa.rmse,
             end
             
         end
-        prior_posterior_statistics('gsa',dataset_);
+        prior_posterior_statistics('gsa',dataset_, dataset_info);
         if options_.bayesian_irf
             PosteriorIRF('gsa');
         end
@@ -366,7 +366,7 @@ if options_gsa.rmse,
     end
     clear a;
 %     filt_mc_(OutputDirectoryName,data_info);
-    filt_mc_(OutputDirectoryName,options_gsa,dataset_);
+    filt_mc_(OutputDirectoryName,options_gsa,dataset_,dataset_info);
 end
 options_.opt_gsa = options_gsa;
 
@@ -405,9 +405,9 @@ if options_gsa.glue,
     Obs.data = data;
     Obs.time = [1:gend];
     Obs.num  = gend;
-    for j=1:size(options_.varobs,1)
+    for j=1:length(options_.varobs)
-        Obs.name{j} = deblank(options_.varobs(j,:));
+        Obs.name{j} = options_.varobs{j};
-        vj=deblank(options_.varobs(j,:));
+        vj = options_.varobs{j};
         
         jxj = strmatch(vj,lgy_(dr_.order_var,:),'exact');
         js = strmatch(vj,lgy_,'exact');
@@ -445,7 +445,7 @@ 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;
@@ -470,10 +470,10 @@ if options_gsa.glue,
         Exo(j).name = deblank(tit(j,:));    
     end
     if ~options_gsa.ppost
-        Lik(size(options_.varobs,1)+1).name = 'logpo';
+        Lik(length(options_.varobs)+1).name = 'logpo';
-        Lik(size(options_.varobs,1)+1).ini  = 'yes';
+        Lik(length(options_.varobs)+1).ini  = 'yes';
-        Lik(size(options_.varobs,1)+1).isam = 1;
+        Lik(length(options_.varobs)+1).isam = 1;
-        Lik(size(options_.varobs,1)+1).data = -logpo2;
+        Lik(length(options_.varobs)+1).data = -logpo2;
     end
     Sam.name = bayestopt_.name;
     Sam.dim  = [size(x) 0];

matlab/evaluate_likelihood.m

@@ -37,7 +37,7 @@ function [llik,parameters] = evaluate_likelihood(parameters)
 
 global options_ M_ bayestopt_ oo_ estim_params_
 
-persistent dataset
+persistent dataset dataset_info
 
 if nargin==0
     parameters = 'posterior mode';
@@ -67,22 +67,9 @@ if ischar(parameters)
 end
 
 if isempty(dataset)
-    % Load and transform data.
+    [dataset, dataset_info] = makedataset(options_);
-    transformation = [];
-    if options_.loglinear && ~options_.logdata
-        transformation = @log;
-    end
-    xls.sheet = options_.xls_sheet;
-    xls.range = options_.xls_range;
-
-    if ~isfield(options_,'nobs')
-        options_.nobs = [];
-    end
-
-    dataset = initialize_dataset(options_.datafile,options_.varobs,options_.first_obs,options_.nobs,transformation,options_.prefilter,xls);
 end
 
-llik = -dsge_likelihood(parameters,dataset,options_,M_,estim_params_,bayestopt_,oo_);
+llik = -dsge_likelihood(parameters,dataset,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
 ldens = evaluate_prior(parameters);
-llik = llik - ldens;
+llik = llik - ldens;
-

matlab/evaluate_smoother.m

@@ -44,14 +44,14 @@ function oo_=evaluate_smoother(parameters,var_list)
 
 global options_ M_ bayestopt_ oo_ estim_params_   % estim_params_ may be emty
 
-persistent dataset_
+persistent dataset_ dataset_info
 
 if ischar(parameters) && strcmp(parameters,'calibration')
     options_.smoother=1;
 end
 
 if isempty(dataset_) || isempty(bayestopt_)
-    [dataset_,xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_] = dynare_estimation_init(var_list, M_.fname, [], M_, options_, oo_, estim_params_, bayestopt_);
+    [dataset_,dataset_info,xparam1, hh, M_, options_, oo_, estim_params_,bayestopt_] = dynare_estimation_init(var_list, M_.fname, [], M_, options_, oo_, estim_params_, bayestopt_);
 end
 
 if nargin==0
@@ -88,7 +88,7 @@ if ischar(parameters)
 end
 
 [atT,innov,measurement_error,updated_variables,ys,trend_coeff,aK,T,R,P,PK,decomp] = ...
-    DsgeSmoother(parameters,dataset_.info.ntobs,dataset_.data,dataset_.missing.aindex,dataset_.missing.state);
+    DsgeSmoother(parameters,dataset_.nobs,transpose(dataset_.data),dataset_info.missing.aindex,dataset_info.missing.state);
 
 oo_.Smoother.SteadyState = ys;
 oo_.Smoother.TrendCoeffs = trend_coeff;

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_file_extension.m

@@ -0,0 +1,42 @@
+function ext = get_file_extension(file)
+
+% returns the extension of a file.
+%
+% INPUTS 
+%  o file      string, name of the file
+%
+% OUTPUTS 
+%  o ext       string, extension.
+%
+% REMARKS 
+%  If the provided file name has no extension, the routine will return an empty array.
+    
+% Copyright (C) 2013 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+% Clean-up path
+file = strrep(file, '../', '');
+file = strrep(file, './', '');
+
+remain = file;
+while ~isempty(remain)
+    [ext, remain] = strtok(remain,'.');
+end
+
+if strcmp(ext,file)
+    ext = [];
+end

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,:));
+    name1 = options_.varobs{k1};
-    name2 = deblank(options_.varobs(k2,:));
+    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),:));

matlab/get_variables_list.m

@@ -35,7 +35,7 @@ function [ivar,vartan,options_] = get_variables_list(options_,M_)
 
 varlist = options_.varlist;
 if isempty(varlist)
-    varlist = options_.varobs;
+    varlist = char(options_.varobs);
     options_.varlist = varlist;
 end
 nvar = rows(varlist);

matlab/global_initialization.m

@@ -33,6 +33,7 @@ global oo_ M_ options_ estim_params_ bayestopt_ estimation_info ex0_ ys0_
 estim_params_ = [];
 bayestopt_ = [];
 options_.datafile = '';
+options_.dataset = [];
 options_.verbosity = 1;
 options_.terminal_condition = 0;
 options_.rplottype = 0;
@@ -349,11 +350,13 @@ estimation_info.structural_innovation_corr_prior_index = {};
 estimation_info.structural_innovation_corr_options_index = {};
 estimation_info.structural_innovation_corr.range_index = {};
 options_.initial_period = dates(1,1);
-options_.dataset.firstobs = options_.initial_period;
+options_.dataset.file = [];
-options_.dataset.lastobs = NaN;
+options_.dataset.series = [];
+options_.dataset.firstobs = dates();
+options_.dataset.lastobs = dates();
 options_.dataset.nobs = NaN;
-options_.dataset.xls_sheet = NaN;
+options_.dataset.xls_sheet = [];
-options_.dataset.xls_range = NaN;
+options_.dataset.xls_range = [];
 options_.Harvey_scale_factor = 10;
 options_.MaxNumberOfBytes = 1e6;
 options_.MaximumNumberOfMegaBytes = 111;
@@ -364,7 +367,8 @@ options_.bayesian_th_moments = 0;
 options_.diffuse_filter = 0;
 options_.filter_step_ahead = [];
 options_.filtered_vars = 0;
-options_.first_obs = 1;
+options_.first_obs = NaN;
+options_.nobs = NaN;
 options_.kalman_algo = 0;
 options_.fast_kalman = 0;
 options_.kalman_tol = 1e-10;

matlab/gsa/filt_mc_.m

@@ -1,4 +1,4 @@
-function [rmse_MC, ixx] = filt_mc_(OutDir,options_gsa_,dataset_)
+function [rmse_MC, ixx] = filt_mc_(OutDir,options_gsa_,dataset_,dataset_info)
 % function [rmse_MC, ixx] = filt_mc_(OutDir)
 % inputs (from opt_gsa structure)
 % vvarvecm = options_gsa_.var_rmse;
@@ -120,10 +120,10 @@ if ~loadSA,
         ys_mean=steady_(M_,options_,oo_);
     end
     %   eval(options_.datafile)
-    Y = dataset_.data;
+    Y = transpose(dataset_.data);
-    gend = dataset_.info.ntobs;
+    gend = dataset_.nobs;
-    data_index = dataset_.missing.aindex;
+    data_index = dataset_info.missing.aindex;
-    missing_value = dataset_.missing.state;
+    missing_value = dataset_info.missing.state;
     for jx=1:gend, data_indx(jx,data_index{jx})=true; end
     %stock_gend=data_info.gend;
     %stock_data = data_info.data;

matlab/identification_analysis.m

@@ -1,4 +1,4 @@
-function [ide_hess, ide_moments, ide_model, ide_lre, derivatives_info, info] = identification_analysis(params,indx,indexo,options_ident,data_info, prior_exist, name_tex, init)
+function [ide_hess, ide_moments, ide_model, ide_lre, derivatives_info, info] = identification_analysis(params,indx,indexo,options_ident,dataset_,dataset_info, prior_exist, name_tex, init)
 % function [ide_hess, ide_moments, ide_model, ide_lre, derivatives_info, info] = identification_analysis(params,indx,indexo,options_ident,data_info, prior_exist, name_tex, init)
 % given the parameter vector params, wraps all identification analyses
 %
@@ -134,17 +134,18 @@ if info(1)==0,
             options_.noprint = 1;
             options_.order = 1;
             options_.SpectralDensity.trigger = 0;
-            options_.periods = data_info.info.ntobs+100;
+            options_.periods = dataset_.nobs+100;
             if options_.kalman_algo > 2,
                 options_.kalman_algo = 1;
             end
             analytic_derivation = options_.analytic_derivation;
             options_.analytic_derivation = -2;
-            info = stoch_simul(options_.varobs);
+            info = stoch_simul(char(options_.varobs));
-            data_info.data=oo_.endo_simul(options_.varobs_id,100+1:end);
+            dataset_ = dseries(oo_.endo_simul(options_.varobs_id,100+1:end),dataset_.dates(1),dataset_.names,dataset_.tex);
+            %data_info.data=oo_.endo_simul(options_.varobs_id,100+1:end);
             %                         datax=data;
             derivatives_info.no_DLIK=1;
-            [fval,DLIK,AHess,cost_flag,ys,trend_coeff,info,M_,options_,bayestopt_,oo_] = dsge_likelihood(params',data_info,options_,M_,estim_params_,bayestopt_,oo_,derivatives_info);
+            [fval,DLIK,AHess,cost_flag,ys,trend_coeff,info,M_,options_,bayestopt_,oo_] = dsge_likelihood(params',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_,derivatives_info);
 %                 fval = DsgeLikelihood(xparam1,data_info,options_,M_,estim_params_,bayestopt_,oo_);
             options_.analytic_derivation = analytic_derivation;
             AHess=-AHess;

matlab/imcforecast.m

@@ -102,30 +102,13 @@ if estimated_model
             error('imcforecast:: The dimension of the vector of parameters doesn''t match the number of estimated parameters!')
         end
     end
-
     set_parameters(xparam);
-
+    [dataset_,dataset_info] = makedataset(options_);
-    % Load and transform data.
+    data = transpose(dataset_.data);
-    transformation = [];
+    data_index = dataset_info.missing.aindex;
-    if options_.loglinear && ~options_.logdata
+    gend = dataset_.nobs;
-        transformation = @log;
+    missing_value = dataset_info.missing.state;
-    end
-    xls.sheet = options_.xls_sheet;
-    xls.range = options_.xls_range;
-    
-    if ~isfield(options_,'nobs')
-        options_.nobs = [];
-    end
-    
-    dataset_ = initialize_dataset(options_.datafile,options_.varobs,options_.first_obs,options_.nobs,transformation,options_.prefilter,xls);
-
-    data = dataset_.data;
-    data_index = dataset_.missing.aindex;
-    gend = options_.nobs;
-    missing_value = dataset_.missing.state;
-
     [atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff] = DsgeSmoother(xparam,gend,data,data_index,missing_value);
-
     trend = repmat(ys,1,options_cond_fcst.periods+1);
     for i=1:M_.endo_nbr
         j = strmatch(deblank(M_.endo_names(i,:)),options_.varobs,'exact');
@@ -134,7 +117,6 @@ if estimated_model
         end
     end
     trend = trend(oo_.dr.order_var,:);
-
     InitState(:,1) = atT(:,end);
 else
     InitState(:,1) = zeros(M_.endo_nbr,1);

matlab/initial_estimation_checks.m

@@ -1,11 +1,12 @@
-function DynareResults = initial_estimation_checks(objective_function,xparam1,DynareDataset,Model,EstimatedParameters,DynareOptions,BayesInfo,DynareResults)
+function DynareResults = initial_estimation_checks(objective_function,xparam1,DynareDataset,DatasetInfo,Model,EstimatedParameters,DynareOptions,BayesInfo,DynareResults)
 % function initial_estimation_checks(xparam1,gend,data,data_index,number_of_observations,no_more_missing_observations)
 % Checks data (complex values, ML evaluation, initial values, BK conditions,..)
 %
 % INPUTS
 %   objective_function  [function handle] of the objective function
 %   xparam1:            [vector] of parameters to be estimated
-%   DynareDataset:      [structure] storing the dataset information
+%   DynareDataset:      [dseries] object storing the dataset
+%   DataSetInfo:        [structure] storing informations about the sample.
 %   Model:              [structure] decribing the model
 %   EstimatedParameters [structure] characterizing parameters to be estimated
 %   DynareOptions       [structure] describing the options
@@ -35,11 +36,11 @@ function DynareResults = initial_estimation_checks(objective_function,xparam1,Dy
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-if DynareDataset.info.nvobs>Model.exo_nbr+EstimatedParameters.nvn
+if DynareDataset.vobs>Model.exo_nbr+EstimatedParameters.nvn
     error(['initial_estimation_checks:: Estimation can''t take place because there are less declared shocks than observed variables!'])
 end
- 
+
-if DynareDataset.info.nvobs>length(find(diag(Model.Sigma_e)))+EstimatedParameters.nvn
+if DynareDataset.vobs>length(find(diag(Model.Sigma_e)))+EstimatedParameters.nvn
     error(['initial_estimation_checks:: Estimation can''t take place because too many shocks have been calibrated with a zero variance!'])
 end
 
@@ -72,7 +73,7 @@ end
 % Evaluate the likelihood.
 ana_deriv = DynareOptions.analytic_derivation;
 DynareOptions.analytic_derivation=0;
-[fval,junk1,junk2,a,b,c,d] = feval(objective_function,xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+[fval,junk1,junk2,a,b,c,d] = feval(objective_function,xparam1,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
 DynareOptions.analytic_derivation=ana_deriv;
 
 if DynareOptions.dsge_var || strcmp(func2str(objective_function),'non_linear_dsge_likelihood')

matlab/load_m_file_data.m

@@ -36,7 +36,7 @@ function [freq,init,data,varlist,tex] = load_m_file_data(file)
 if isoctave
     run(file);
 else
-    [basename, ext] = strtok(file,'.');
+    basename = file(1:end-2);
     run(basename);
 end
 

matlab/metropolis_hastings_initialization.m

@@ -1,7 +1,7 @@
 function [ ix2, ilogpo2, ModelName, MetropolisFolder, fblck, fline, npar, nblck, nruns, NewFile, MAX_nruns, d ] = ...
-    metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_)
+    metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
 %function [ ix2, ilogpo2, ModelName, MhDirectoryName, fblck, fline, npar, nblck, nruns, NewFile, MAX_nruns, d ] = 
-%    metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds, dataset_,options_,M_,estim_params_,bayestopt_,oo_)
+%    metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds, dataset_,dataset_info,,options_,M_,estim_params_,bayestopt_,oo_)
 % Metropolis-Hastings initialization.
 % 
 % INPUTS 
@@ -110,7 +110,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
                 candidate = rand_multivariate_normal( transpose(xparam1), d * options_.mh_init_scale, npar);
                 if all(candidate(:) > mh_bounds(:,1)) && all(candidate(:) < mh_bounds(:,2)) 
                     ix2(j,:) = candidate;
-                    ilogpo2(j) = - feval(TargetFun,ix2(j,:)',dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+                    ilogpo2(j) = - feval(TargetFun,ix2(j,:)',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
                     if ~isfinite(ilogpo2(j)) % if returned log-density is
                                              % Inf or Nan (penalized value)
                         validate = 0;
@@ -152,7 +152,7 @@ if ~options_.load_mh_file && ~options_.mh_recover
         candidate = transpose(xparam1(:));%
         if all(candidate(:) > mh_bounds(:,1)) && all(candidate(:) < mh_bounds(:,2)) 
             ix2 = candidate;
-            ilogpo2 = - feval(TargetFun,ix2',dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+            ilogpo2 = - feval(TargetFun,ix2',dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
             disp('Estimation::mcmc: Initialization at the posterior mode.')
             skipline()
             fprintf(fidlog,['    Blck ' int2str(1) 'params:\n']);

matlab/mode_check.m

@@ -1,4 +1,4 @@
-function mode_check(fun,x,hessian,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function mode_check(fun,x,hessian,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
 % Checks the estimated ML mode or Posterior mode.
 
 %@info:
@@ -62,7 +62,7 @@ if ~isempty(hessian);
     [ s_min, k ] = min(diag(hessian));
 end
 
-fval = feval(fun,x,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+fval = feval(fun,x,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
 
 if ~isempty(hessian);
     skipline()
@@ -138,7 +138,7 @@ for plt = 1:nbplt,
         end
         for i=1:length(z)
             xx(kk) = z(i);
-            [fval, junk1, junk2, exit_flag] = feval(fun,xx,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+            [fval, junk1, junk2, exit_flag] = feval(fun,xx,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
             if exit_flag
                 y(i,1) = fval;
             else

matlab/mr_gstep.m

@@ -1,9 +1,17 @@
-function [f0, x, ig] = mr_gstep(h1,x,func0,htol0,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function [f0, x, ig] = mr_gstep(h1,x,func0,htol0,varargin)
 % function [f0, x, ig] = mr_gstep(h1,x,func0,htol0,varargin)
 %
 % Gibbs type step in optimisation
+%
+% varargin{1} --> DynareDataset
+% varargin{2} --> DatasetInfo
+% varargin{3} --> DynareOptions
+% varargin{4} --> Model
+% varargin{5} --> EstimatedParameters
+% varargin{6} --> BayesInfo
+% varargin{1} --> DynareResults
 
-% Copyright (C) 2006-2012 Dynare Team
+% Copyright (C) 2006-2014 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -22,7 +30,7 @@ function [f0, x, ig] = mr_gstep(h1,x,func0,htol0,DynareDataset,DynareOptions,Mod
 
 n=size(x,1);
 if isempty(h1),
-    h1=DynareOptions.gradient_epsilon*ones(n,1);
+    h1=varargin{3}.gradient_epsilon*ones(n,1);
 end
 
 
@@ -31,7 +39,7 @@ if isempty(htol0)
 else
     htol = htol0;
 end
-f0=feval(func0,x,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+f0=feval(func0,x,varargin{:});
 
 xh1=x;
 f1=zeros(size(f0,1),n);
@@ -45,10 +53,10 @@ while i<n
     hcheck=0;
     dx=[];
     xh1(i)=x(i)+h1(i);
-    fx = feval(func0,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+    fx = feval(func0,xh1,varargin{:});
     f1(:,i)=fx;
     xh1(i)=x(i)-h1(i);
-    fx = feval(func0,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+    fx = feval(func0,xh1,varargin{:});
     f_1(:,i)=fx;
     if hcheck && htol<1
         htol=min(1,max(min(abs(dx))*2,htol*10));
@@ -61,9 +69,9 @@ while i<n
         gg(i)=(f1(i)'-f_1(i)')./(2.*h1(i));
         hh(i) = 1/max(1.e-9,abs( (f1(i)+f_1(i)-2*f0)./(h1(i)*h1(i)) ));
         if gg(i)*(hh(i)*gg(i))/2 > htol
-            [f0 x fc retcode] = csminit1(func0,x,f0,gg,0,diag(hh),DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+            [f0 x fc retcode] = csminit1(func0,x,f0,gg,0,diag(hh),varargin{:});
             ig(i)=1;
-            fprintf(['Done for param %s = %8.4f\n'],BayesInfo.name{i},x(i))
+            fprintf(['Done for param %s = %8.4f\n'],varargin{6}.name{i},x(i))
         end
         xh1=x;
     end

matlab/mr_hessian.m

@@ -1,10 +1,10 @@
-function [hessian_mat, gg, htol1, ihh, hh_mat0, hh1] = mr_hessian(init,x,func,hflag,htol0,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function [hessian_mat, gg, htol1, ihh, hh_mat0, hh1] = mr_hessian(init,x,func,hflag,htol0,varargin)
 %  [hessian_mat, gg, htol1, ihh, hh_mat0, hh1] = mr_hessian(init,x,func,hflag,htol0,varargin)
 %
 %  numerical gradient and Hessian, with 'automatic' check of numerical
 %  error
 %
-% adapted from Michel Juillard original rutine hessian.m
+% adapted from Michel Juillard original routine hessian.m
 %
 %  func =  function handle. The function must give two outputs:
 %    - the log-likelihood AND the single contributions at times t=1,...,T
@@ -22,9 +22,17 @@ function [hessian_mat, gg, htol1, ihh, hh_mat0, hh1] = mr_hessian(init,x,func,hf
 %  htol0 = 'precision' of increment of function values for numerical
 %  derivatives
 %
-%  varargin: other parameters of func
+%  varargin{1} --> DynareDataset
+%  varargin{2} --> DatasetInfo
+%  varargin{3} --> DynareOptions
+%  varargin{4} --> Model
+%  varargin{5} --> EstimatedParameters
+%  varargin{6} --> BayesInfo
+%  varargin{1} --> DynareResults
 
-% Copyright (C) 2004-2012 Dynare Team
+
+
+% Copyright (C) 2004-2014 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -45,16 +53,16 @@ persistent h1 htol
 
 n=size(x,1);
 if init
-    gstep_=DynareOptions.gstep;
+    gstep_=varargin{3}.gstep;
     htol = 1.e-4;
-    h1=DynareOptions.gradient_epsilon*ones(n,1);
+    h1=varargin{3}.gradient_epsilon*ones(n,1);
     return
 end
 
-[f0, ff0]=feval(func,x,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+[f0, ff0]=feval(func,x,varargin{:});
-h2=BayesInfo.ub-BayesInfo.lb;
+h2=varargin{6}.ub-varargin{6}.lb;
-hmax=BayesInfo.ub-x;
+hmax=varargin{6}.ub-x;
-hmax=min(hmax,x-BayesInfo.lb);
+hmax=min(hmax,x-varargin{6}.lb);
 if isempty(ff0),
     outer_product_gradient=0;
 else
@@ -83,7 +91,7 @@ while i<n
     hcheck=0;
     xh1(i)=x(i)+h1(i);
     try
-        [fx, ffx]=feval(func,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+        [fx, ffx]=feval(func,xh1,varargin{:});
     catch
         fx=1.e8;
     end
@@ -104,7 +112,7 @@ while i<n
             h1(i) = max(h1(i),1.e-10);
             xh1(i)=x(i)+h1(i);
             try
-                [fx, ffx]=feval(func,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+                [fx, ffx]=feval(func,xh1,varargin{:});
             catch
                 fx=1.e8;
             end
@@ -113,21 +121,21 @@ while i<n
             h1(i)= htol/abs(dx(it))*h1(i);
             xh1(i)=x(i)+h1(i);
             try
-                [fx, ffx]=feval(func,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+                [fx, ffx]=feval(func,xh1,varargin{:});
             catch
                 fx=1.e8;
             end
             while (fx-f0)==0
                 h1(i)= h1(i)*2;
                 xh1(i)=x(i)+h1(i);
-                [fx, ffx]=feval(func,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+                [fx, ffx]=feval(func,xh1,varargin{:});
                 ic=1;
             end
         end
         it=it+1;
         dx(it)=(fx-f0);
         h0(it)=h1(i);
-        if (h1(i)<1.e-12*min(1,h2(i)) && h1(i)<0.5*hmax(i))% || (icount==10 &&  abs(dx(it))>(3*htol)),
+        if (h1(i)<1.e-12*min(1,h2(i)) && h1(i)<0.5*hmax(i))
             ic=1;
             hcheck=1;
         end
@@ -141,7 +149,7 @@ while i<n
         end
     end
     xh1(i)=x(i)-h1(i);
-    [fx, ffx]=feval(func,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+    [fx, ffx]=feval(func,xh1,varargin{:});
     f_1(:,i)=fx;
     if outer_product_gradient,
         if any(isnan(ffx)) || isempty(ffx),
@@ -181,8 +189,8 @@ if outer_product_gradient,
                 xh1(j)=x(j)+h_1(j);
                 xh_1(i)=x(i)-h1(i);
                 xh_1(j)=x(j)-h_1(j);
-                temp1 = feval(func,xh1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+                temp1 = feval(func,xh1,varargin{:});
-                temp2 = feval(func,xh_1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+                temp2 = feval(func,xh_1,varargin{:});
                 hessian_mat(:,(i-1)*n+j)=-(-temp1 -temp2+temp(:,i)+temp(:,j))./(2*h1(i)*h_1(j));
                 xh1(i)=x(i);
                 xh1(j)=x(j);
@@ -203,7 +211,7 @@ if outer_product_gradient,
             end
         end
     end
-    
+
     gga=ggh.*kron(ones(size(ff1)),2.*h1');  % re-scaled gradient
     hh_mat=gga'*gga;  % rescaled outer product hessian
     hh_mat0=ggh'*ggh;  % outer product hessian
@@ -222,7 +230,7 @@ if outer_product_gradient,
         sd=sqrt(diag(ihh));   %standard errors
         sdh=sqrt(1./diag(hh));   %diagonal standard errors
         for j=1:length(sd)
-            sd0(j,1)=min(BayesInfo.p2(j), sd(j));  %prior std
+            sd0(j,1)=min(varargin{6}.p2(j), sd(j));  %prior std
             sd0(j,1)=10^(0.5*(log10(sd0(j,1))+log10(sdh(j,1))));
         end
         ihh=ihh./(sd*sd').*(sd0*sd0');  %inverse outer product with modified std's
@@ -239,7 +247,7 @@ if outer_product_gradient,
     if hflag<2
         hessian_mat=hh_mat0(:);
     end
-    
+
     if any(isnan(hessian_mat))
         hh_mat0=eye(length(hh_mat0));
         ihh=hh_mat0;

matlab/ms-sbvar/ms_sbvar_setup.m

@@ -122,7 +122,7 @@ pctindx = [];
 % Select the variable to use and rearrange columns if desired
 %vlist = [3 1 2];
 %options_.ms.vlist = [1 2 3];
-options_.ms.vlist = 1:size(options_.varobs,1);
+options_.ms.vlist = 1:length(options_.varobs);
 vlist1=options_.ms.vlist;
 
 %==========================================================================

matlab/ms-sbvar/ms_write_markov_file.m

@@ -28,7 +28,7 @@ function ms_write_markov_file(fname, options)
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
     n_chains = length(options.ms.ms_chain);
-    nvars = size(options.varobs,1);
+    nvars = length(options.varobs);
     
     fh = fopen(fname,'w');
     %/******************************************************************************/

matlab/ms-sbvar/msstart_setup.m

@@ -48,16 +48,16 @@ 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:size(options_.varobs,1)];    % 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.varlist=cellstr(options_.varobs');
-options_.ms.log_var = sort(varlist_indices(options_.ms.vlistlog,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.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);
 %options_.ms.restriction_fname='ftd_upperchol3v';   %Only used by msstart2.m.
 ylab = options_.ms.varlist;
 xlab = options_.ms.varlist;
 
 %----------------
-nvar = size(options_.varobs,1);   % number of endogenous variables
+nvar = length(options_.varobs);   % number of endogenous variables
 nlogeno = length(options_.ms.log_var);  % number of endogenous variables in options_.ms.log_var
 npereno = length(options_.ms.percent_var);  % number of endogenous variables in options_.ms.percent_var
 if (nvar~=(nlogeno+npereno))

matlab/newrat.m

@@ -1,4 +1,4 @@
-function [xparam1, hh, gg, fval, igg] = newrat(func0, x, analytic_derivation, ftol0, nit, flagg, DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults)
+function [xparam1, hh, gg, fval, igg] = newrat(func0, x, analytic_derivation, ftol0, nit, flagg, varargin)
 %  [xparam1, hh, gg, fval, igg] = newrat(func0, x, hh, gg, igg, ftol0, nit, flagg, varargin)
 %
 %  Optimiser with outer product gradient and with sequences of univariate steps
@@ -21,9 +21,16 @@ function [xparam1, hh, gg, fval, igg] = newrat(func0, x, analytic_derivation, ft
 %             with correlation structure as from outer product gradient,
 %  flagg = 2, full numerical Hessian
 %
-%  varargin = list of parameters for func0
+%  varargin{1} --> DynareDataset
+%  varargin{2} --> DatasetInfo
+%  varargin{3} --> DynareOptions
+%  varargin{4} --> Model
+%  varargin{5} --> EstimatedParameters
+%  varargin{6} --> BayesInfo
+%  varargin{1} --> DynareResults
 
-% Copyright (C) 2004-2013 Dynare Team
+
+% Copyright (C) 2004-2014 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -52,19 +59,19 @@ ftol=ftol0;
 gtol=1.e-3;
 htol=htol_base;
 htol0=htol_base;
-gibbstol=length(BayesInfo.pshape)/50; %25;
+gibbstol=length(varargin{6}.pshape)/50; %25;
 
 % func0 = str2func([func2str(func0),'_hh']);
 % func0 = func0;
-[fval0,gg,hh]=feval(func0,x,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+[fval0,gg,hh]=feval(func0,x,varargin{:});
 fval=fval0;
 
 % initialize mr_gstep and mr_hessian
 
 outer_product_gradient=1;
 if isempty(hh)
-    mr_hessian(1,x,[],[],[],DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+    mr_hessian(1,x,[],[],[],varargin{:});
-    [dum, gg, htol0, igg, hhg, h1]=mr_hessian(0,x,func0,flagit,htol,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+    [dum, gg, htol0, igg, hhg, h1]=mr_hessian(0,x,func0,flagit,htol,varargin{:});
     if isempty(dum),
         outer_product_gradient=0;
         igg = 1e-4*eye(nx);
@@ -111,9 +118,9 @@ while norm(gg)>gtol && check==0 && jit<nit
     objective_function_penalty_base = fval0(icount);
     disp([' '])
     disp(['Iteration ',num2str(icount)])
-    [fval,x0,fc,retcode] = csminit1(func0,xparam1,fval0(icount),gg,0,H,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+    [fval,x0,fc,retcode] = csminit1(func0,xparam1,fval0(icount),gg,0,H,varargin{:});
     if igrad
-        [fval1,x01,fc,retcode1] = csminit1(func0,x0,fval,gg,0,inx,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+        [fval1,x01,fc,retcode1] = csminit1(func0,x0,fval,gg,0,inx,varargin{:});
         if (fval-fval1)>1
             disp('Gradient step!!')
         else
@@ -122,28 +129,26 @@ while norm(gg)>gtol && check==0 && jit<nit
         fval=fval1;
         x0=x01;
     end
-%     if icount==1 || (icount>1 && (fval0(icount-1)-fval0(icount))>1) || ((fval0(icount)-fval)<1.e-2*(gg'*(H*gg))/2 && igibbs),
+    if length(find(ig))<nx
-        if length(find(ig))<nx
+        ggx=ggx*0;
-            ggx=ggx*0;
+        ggx(find(ig))=gg(find(ig));
-            ggx(find(ig))=gg(find(ig));
+        if analytic_derivation,
-            if analytic_derivation,
+            hhx=hh;
-                hhx=hh;
+        else
-            else
+            hhx = reshape(dum,nx,nx);
-                hhx = reshape(dum,nx,nx);
-            end
-            iggx=eye(length(gg));
-            iggx(find(ig),find(ig)) = inv( hhx(find(ig),find(ig)) );
-            [fvala,x0,fc,retcode] = csminit1(func0,x0,fval,ggx,0,iggx,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
         end
-        [fvala, x0, ig] = mr_gstep(h1,x0,func0,htol,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+        iggx=eye(length(gg));
-        nig=[nig ig];
+        iggx(find(ig),find(ig)) = inv( hhx(find(ig),find(ig)) );
-        disp('Sequence of univariate steps!!')
+        [fvala,x0,fc,retcode] = csminit1(func0,x0,fval,ggx,0,iggx,varargin{:});
-        fval=fvala;
+    end
-%     end
+    [fvala, x0, ig] = mr_gstep(h1,x0,func0,htol,varargin{:});
+    nig=[nig ig];
+    disp('Sequence of univariate steps!!')
+    fval=fvala;
     if (fval0(icount)-fval)<ftol && flagit==0
         disp('Try diagonal Hessian')
         ihh=diag(1./(diag(hhg)));
-        [fval2,x0,fc,retcode2] = csminit1(func0,x0,fval,gg,0,ihh,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+        [fval2,x0,fc,retcode2] = csminit1(func0,x0,fval,gg,0,ihh,varargin{:});
         if (fval-fval2)>=ftol
             disp('Diagonal Hessian successful')
         end
@@ -152,7 +157,7 @@ while norm(gg)>gtol && check==0 && jit<nit
     if (fval0(icount)-fval)<ftol && flagit==0
         disp('Try gradient direction')
         ihh0=inx.*1.e-4;
-        [fval3,x0,fc,retcode3] = csminit1(func0,x0,fval,gg,0,ihh0,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+        [fval3,x0,fc,retcode3] = csminit1(func0,x0,fval,gg,0,ihh0,varargin{:});
         if (fval-fval3)>=ftol
             disp('Gradient direction successful')
         end
@@ -165,14 +170,14 @@ while norm(gg)>gtol && check==0 && jit<nit
         disp('No further improvement is possible!')
         check=1;
         if analytic_derivation,
-            [fvalx,gg,hh]=feval(func0,xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+            [fvalx,gg,hh]=feval(func0,xparam1,varargin{:});
             hhg=hh;
-            H = inv(hh);            
+            H = inv(hh);
         else
         if flagit==2
             hh=hh0;
         elseif flagg>0
-            [dum, gg, htol0, igg, hhg,h1]=mr_hessian(0,xparam1,func0,flagg,ftol0,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+            [dum, gg, htol0, igg, hhg,h1]=mr_hessian(0,xparam1,func0,flagg,ftol0,varargin{:});
             if flagg==2
                 hh = reshape(dum,nx,nx);
                 ee=eig(hh);
@@ -208,7 +213,7 @@ while norm(gg)>gtol && check==0 && jit<nit
             catch
                 save m1.mat x fval0 nig
             end
-            [dum, gg, htol0, igg, hhg, h1]=mr_hessian(0,xparam1,func0,flagit,htol,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+            [dum, gg, htol0, igg, hhg, h1]=mr_hessian(0,xparam1,func0,flagit,htol,varargin{:});
             if isempty(dum),
                 outer_product_gradient=0;
             end
@@ -237,7 +242,7 @@ while norm(gg)>gtol && check==0 && jit<nit
                 H = igg;
             end
         elseif analytic_derivation,
-            [fvalx,gg,hh]=feval(func0,xparam1,DynareDataset,DynareOptions,Model,EstimatedParameters,BayesInfo,DynareResults);
+            [fvalx,gg,hh]=feval(func0,xparam1,varargin{:});
             hhg=hh;
             H = inv(hh);
         end
@@ -276,6 +281,4 @@ if check==1,
     disp(['Estimation successful.'])
 end
 
-return
+return
-
-

matlab/osr.m

@@ -41,7 +41,6 @@ function osr_res = osr(var_list,params,i_var,W)
 global M_ options_ oo_  
 
 options_.order = 1;
-options_ = set_default_option(options_,'simul',0);
 
 if isempty(options_.qz_criterium)
     options_.qz_criterium = 1+1e-6;

matlab/particle/solve_model_for_online_filter.m

@@ -201,9 +201,10 @@ end
 
 % Get the off-diagonal elements of the covariance matrix for the measurement errors. Test if H is positive definite.
 if EstimatedParameters.ncn
+    corrn_observable_correspondence = EstimatedParameters.corrn_observable_correspondence;
     for i=1:EstimatedParameters.ncn
-        k1 = DynareOptions.lgyidx2varobs(EstimatedParameters.corrn(i,1));
+        k1 = corrn_observable_correspondence(i,1);
-        k2 = DynareOptions.lgyidx2varobs(EstimatedParameters.corrn(i,2));
+        k2 = corrn_observable_correspondence(i,2);
         H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
         H(k2,k1) = H(k1,k2);
     end

matlab/prior_posterior_statistics.m

@@ -1,4 +1,4 @@
-function prior_posterior_statistics(type,dataset)
+function prior_posterior_statistics(type,dataset,dataset_info)
 
 % function prior_posterior_statistics(type,dataset)
 % Computes Monte Carlo filter smoother and forecasts
@@ -40,11 +40,11 @@ global options_ estim_params_ oo_ M_ bayestopt_
 
 localVars=[];
 
-Y = dataset.data;
+Y = transpose(dataset.data);
-gend = dataset.info.ntobs;
+gend = dataset.nobs;
-data_index = dataset.missing.aindex;
+data_index = dataset_info.missing.aindex;
-missing_value = dataset.missing.state;
+missing_value = dataset_info.missing.state;
-bayestopt_.mean_varobs = dataset.descriptive.mean';
+bayestopt_.mean_varobs = dataset_info.descriptive.mean';
 
 nvx  = estim_params_.nvx;
 nvn  = estim_params_.nvn;
@@ -58,7 +58,7 @@ naK = length(options_.filter_step_ahead);
 MaxNumberOfBytes=options_.MaxNumberOfBytes;
 endo_nbr=M_.endo_nbr;
 exo_nbr=M_.exo_nbr;
-nvobs     = size(options_.varobs,1);
+nvobs     = length(options_.varobs);
 iendo = 1:endo_nbr;
 horizon = options_.forecast;
 filtered_vars = options_.filtered_vars;
@@ -97,7 +97,7 @@ MAX_ninno = min(B,ceil(MaxNumberOfBytes/(exo_nbr*gend)/8));
 MAX_nerro = min(B,ceil(MaxNumberOfBytes/(size(options_.varobs,1)*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
 
@@ -106,7 +106,7 @@ if horizon
     MAX_nforc2 = min(B,ceil(MaxNumberOfBytes/((endo_nbr)*(horizon+maxlag))/ ...
                             8));
     IdObs    = bayestopt_.mfys;
-    
+
 end
 MAX_momentsno = min(B,ceil(MaxNumberOfBytes/(get_moments_size(options_)*8)));
 
@@ -232,7 +232,7 @@ else
                         'options_', options_, ...
                         'bayestopt_', bayestopt_, ...
                         'estim_params_', estim_params_, ...
-                        'oo_', oo_);    
+                        'oo_', oo_);
     % which files have to be copied to run remotely
     NamFileInput(1,:) = {'',[M_.fname '_static.m']};
     NamFileInput(2,:) = {'',[M_.fname '_dynamic.m']};
@@ -240,11 +240,10 @@ else
         NamFileInput(length(NamFileInput)+1,:)={'',[M_.fname '_steadystate.m']};
     end
     [fout] = masterParallel(options_.parallel, 1, B,NamFileInput,'prior_posterior_statistics_core', localVars,globalVars, options_.parallel_info);
-    
+
 end
 ifil = fout(end).ifil;
 
-% ??????????
 stock_gend=gend;
 stock_data=Y;
 save([DirectoryName '/' M_.fname '_data.mat'],'stock_gend','stock_data');
@@ -301,4 +300,4 @@ if ~isnumeric(options_.parallel),
     if leaveSlaveOpen == 0,
         closeSlave(options_.parallel,options_.parallel_info.RemoteTmpFolder),
     end
-end
+end

matlab/prior_posterior_statistics_core.m

@@ -133,14 +133,14 @@ end
 if run_smoother
   stock_smooth=NaN(endo_nbr,gend,MAX_nsmoo);
   stock_update=NaN(endo_nbr,gend,MAX_nsmoo);
-  stock_innov=NaN(M_.exo_nbr,gend,MAX_ninno);  
+  stock_innov=NaN(M_.exo_nbr,gend,MAX_ninno);
   if horizon
       stock_forcst_mean= NaN(endo_nbr,horizon+maxlag,MAX_nforc1);
       stock_forcst_point = NaN(endo_nbr,horizon+maxlag,MAX_nforc2);
   end
 end
 if nvn
-  stock_error = NaN(size(varobs,1),gend,MAX_nerro);
+  stock_error = NaN(length(varobs),gend,MAX_nerro);
 end
 if naK
     stock_filter_step_ahead =NaN(length(options_.filter_step_ahead),endo_nbr,gend+max(options_.filter_step_ahead),MAX_naK);
@@ -150,10 +150,6 @@ stock_logpo = NaN(MAX_nruns,1);
 stock_ys = NaN(MAX_nruns,endo_nbr);
 
 for b=fpar:B
-
-    %    [deep, logpo] = GetOneDraw(type);
-    %    set_all_parameters(deep);
-    %    dr = resol(oo_.steady_state,0);
     if strcmpi(type,'prior')
 
         [deep, logpo] = GetOneDraw(type);
@@ -304,22 +300,7 @@ for b=fpar:B
         end
         irun(7) = 1;
     end
-
-    % if moments_varendo && (irun(8) > MAX_momentsno || b == B)
-    %    stock = stock_moments(1:irun(8)-1);
-    %    ifil(8) = ifil(8) + 1;
-    %    save([DirectoryName '/' M_.fname '_moments' int2str(ifil(8)) '.mat'],'stock');
-    %    if RemoteFlag==1,
-    %    OutputFileName_moments = [OutputFileName_moments; {[DirectoryName filesep], [M_.fname '_moments' int2str(ifil(8)) '.mat']}];
-    %    end
-    %    irun(8) = 1;
-    % end
-
-    %   DirectoryName=TempPath;
-
-
     dyn_waitbar((b-fpar+1)/(B-fpar+1),h);
-
 end
 
 myoutput.ifil=ifil;
@@ -332,7 +313,6 @@ if RemoteFlag==1,
                         OutputFileName_param;
                         OutputFileName_forc_mean;
                         OutputFileName_forc_point];
-    % OutputFileName_moments];
 end
 
 dyn_waitbar_close(h);

matlab/random_walk_metropolis_hastings.m

@@ -1,4 +1,4 @@
-function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_)
+function random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
 %function record=random_walk_metropolis_hastings(TargetFun,ProposalFun,xparam1,vv,mh_bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_)
 % Random walk Metropolis-Hastings algorithm. 
 % 
@@ -60,7 +60,7 @@ objective_function_penalty_base = Inf;
 
 % Initialization of the random walk metropolis-hastings chains.
 [ ix2, ilogpo2, ModelName, MetropolisFolder, fblck, fline, npar, nblck, nruns, NewFile, MAX_nruns, d ] = ...
-    metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+    metropolis_hastings_initialization(TargetFun, xparam1, vv, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
 
 InitSizeArray = min([repmat(MAX_nruns,nblck,1) fline+nruns-1],[],2);
 
@@ -101,6 +101,7 @@ localVars =   struct('TargetFun', TargetFun, ...
                      'InitSizeArray',InitSizeArray, ...
                      'record', record, ...
                      'dataset_', dataset_, ...
+                     'dataset_info', dataset_info, ...
                      'options_', options_, ...
                      'M_',M_, ...
                      'bayestopt_', bayestopt_, ...

matlab/random_walk_metropolis_hastings_core.m

@@ -88,6 +88,7 @@ d=myinputs.d;
 InitSizeArray=myinputs.InitSizeArray;
 record=myinputs.record;
 dataset_ = myinputs.dataset_;
+dataset_info = myinputs.dataset_info;
 bayestopt_ = myinputs.bayestopt_;
 estim_params_ = myinputs.estim_params_;
 options_ = myinputs.options_;
@@ -164,7 +165,7 @@ for b = fblck:nblck,
         par = feval(ProposalFun, ix2(b,:), proposal_covariance_Cholesky_decomposition, n);
         if all( par(:) > mh_bounds(:,1) ) && all( par(:) < mh_bounds(:,2) )
             try
-                logpost = - feval(TargetFun, par(:),dataset_,options_,M_,estim_params_,bayestopt_,oo_);
+                logpost = - feval(TargetFun, par(:),dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
             catch
                 logpost = -inf;
             end

matlab/read_variables.m

@@ -42,7 +42,7 @@ if ~isempty(directory)
 end
 
 dyn_size_01 = size(dyn_data_01,1);
-var_size_01 = size(var_names_01,1);
+var_size_01 = length(var_names_01);
 
 % Auto-detect extension if not provided
 if isempty(extension)
@@ -71,7 +71,7 @@ switch (extension)
     case '.m'
         eval(basename);
         for dyn_i_01=1:var_size_01
-            dyn_tmp_01 = eval(var_names_01(dyn_i_01,:));
+            dyn_tmp_01 = eval(var_names_01{dyn_i_01});
             if length(dyn_tmp_01) > dyn_size_01 && dyn_size_01 > 0
                 cd(old_pwd)
                 error('data size is too large')
@@ -81,7 +81,7 @@ switch (extension)
     case '.mat'
         s = load(basename);
         for dyn_i_01=1:var_size_01
-            dyn_tmp_01 = s.(deblank(var_names_01(dyn_i_01,:)));
+            dyn_tmp_01 = s.(var_names_01{dyn_i_01});
             if length(dyn_tmp_01) > dyn_size_01 && dyn_size_01 > 0
                 cd(old_pwd)
                 error('data size is too large')
@@ -106,9 +106,8 @@ switch (extension)
         end
     case '.csv'
         [freq,init,data,varlist] = load_csv_file_data(fullname);
-        %var_names_01 = deblank(var_names_01);
         for dyn_i_01=1:var_size_01
-            iv = strmatch(strtrim(var_names_01(dyn_i_01,:)),varlist,'exact');
+            iv = strmatch(var_names_01{dyn_i_01},varlist,'exact');
             if ~isempty(iv)
                 dyn_tmp_01 = [data(:,iv)]';
                 if length(dyn_tmp_01) > dyn_size_01 && dyn_size_01 > 0
@@ -118,7 +117,7 @@ switch (extension)
                 dyn_data_01(:,dyn_i_01) = dyn_tmp_01;
             else
                 cd(old_pwd)
-                error([strtrim(var_names_01(dyn_i_01,:)) ' not found in ' fullname])
+                error([var_names_01{dyn_i_01} ' not found in ' fullname])
             end
         end
     otherwise

matlab/set_default_option.m

@@ -33,6 +33,17 @@ function options=set_default_option(options,field,default)
 
 if ~isfield(options,field)
     options.(field) = default;
+    return
+end
+
+if isempty(options.(field))
+    options.(field) = default;
+    return
+end
+
+if isnan(options.(field))
+    options.(field) = default;
+    return
 end
 
 % 06/07/03 MJ added ; to eval expression

matlab/set_prior.m

@@ -76,12 +76,12 @@ end
 if nvn
     estim_params_.nvn_observable_correspondence=NaN(nvn,1); % stores number of corresponding observable
     if isequal(M_.H,0) %if no previously set measurement error, initialize H
-        nvarobs = size(options_.varobs,1);
+        nvarobs = length(options_.varobs);
         M_.H = zeros(nvarobs,nvarobs);
         M_.Correlation_matrix_ME = eye(nvarobs);
     end
     for i=1:nvn
-        obsi_ = strmatch(deblank(M_.endo_names(estim_params_.var_endo(i,1),:)),deblank(options_.varobs),'exact');
+        obsi_ = strmatch(deblank(M_.endo_names(estim_params_.var_endo(i,1),:)),options_.varobs,'exact');
         if isempty(obsi_)
             error(['The variable ' deblank(M_.endo_names(estim_params_.var_endo(i,1),:)) ' has to be declared as observable since you assume a measurement error on it.'])
         end
@@ -96,7 +96,7 @@ if nvn
     bayestopt_.p3 = [ bayestopt_.p3; estim_params_.var_endo(:,8)]; %take generalized distribution into account
     bayestopt_.p4 = [ bayestopt_.p4; estim_params_.var_endo(:,9)]; %take generalized distribution into account
     bayestopt_.jscale = [ bayestopt_.jscale; estim_params_.var_endo(:,10)];
-    bayestopt_.name = [ bayestopt_.name; cellstr(options_.varobs(estim_params_.nvn_observable_correspondence,:))];
+    bayestopt_.name = [ bayestopt_.name; transpose(options_.varobs(estim_params_.nvn_observable_correspondence))];
 end
 if ncx
     xparam1 = [xparam1; estim_params_.corrx(:,3)];
@@ -115,7 +115,7 @@ end
 if ncn
     estim_params_.corrn_observable_correspondence=NaN(ncn,2);
     if isequal(M_.H,0)
-        nvarobs = size(options_.varobs,1);
+        nvarobs = length(options_.varobs);
         M_.H = zeros(nvarobs,nvarobs);
         M_.Correlation_matrix_ME = eye(nvarobs);
     end
@@ -134,8 +134,8 @@ if ncn
     for i=1:ncn
         k1 = estim_params_.corrn(i,1);
         k2 = estim_params_.corrn(i,2);
-        obsi1 = strmatch(deblank(M_.endo_names(k1,:)),deblank(options_.varobs),'exact'); %find correspondence to varobs to construct H in set_all_paramters
+        obsi1 = strmatch(deblank(M_.endo_names(k1,:)),options_.varobs,'exact'); %find correspondence to varobs to construct H in set_all_paramters
-        obsi2 = strmatch(deblank(M_.endo_names(k2,:)),deblank(options_.varobs),'exact');
+        obsi2 = strmatch(deblank(M_.endo_names(k2,:)),options_.varobs,'exact');
         estim_params_.corrn_observable_correspondence(i,:)=[obsi1,obsi2]; %save correspondence
     end
 end

matlab/simplex_optimization_routine.m

@@ -11,8 +11,15 @@ function [x,fval,exitflag] = simplex_optimization_routine(objective_function,x,o
 %  o objective_function     [string]                  Name of the objective function to be minimized.
 %  o x                      [double]                  n*1 vector, starting guess of the optimization routine.
 %  o options                [structure]               Options of this implementation of the simplex algorithm.
-%  o varargin               [cell of structures]      Structures to be passed to the objective function: dataset_,
+%  o varargin               [cell of structures]      Structures to be passed to the objective function.
-%                                                     options_, M_, estim_params_, bayestopt_, and oo_.
+%
+%     varargin{1} --> DynareDataset
+%     varargin{2} --> DatasetInfo
+%     varargin{3} --> DynareOptions
+%     varargin{4} --> Model
+%     varargin{5} --> EstimatedParameters
+%     varargin{6} --> BayesInfo
+%     varargin{1} --> DynareResults
 %
 % OUTPUTS 
 %  o x                      [double]                  n*1 vector, estimate of the optimal inputs.
@@ -187,7 +194,7 @@ else
         disp(['Current parameter values: '])
         fprintf(1,'%s: \t\t\t %s \t\t\t %s \t\t\t %s \t\t\t %s \t\t\t %s \n','Names','Best point', 'Worst point', 'Mean values', 'Min values', 'Max values');
         for i=1:number_of_variables
-            fprintf(1,'%s: \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \n',varargin{5}.name{i},v(i,1), v(i,end), mean(v(i,:),2), min(v(i,:),[],2), max(v(i,:),[],2));
+            fprintf(1,'%s: \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \n',varargin{6}.name{i},v(i,1), v(i,end), mean(v(i,:),2), min(v(i,:),[],2), max(v(i,:),[],2));
         end
         skipline()
     end
@@ -399,7 +406,7 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
         disp(['Current parameter values: '])
         fprintf(1,'%s: \t\t\t %s \t\t\t %s \t\t\t %s \t\t\t %s \t\t\t %s \n','Names','Best point', 'Worst point', 'Mean values', 'Min values', 'Max values');
         for i=1:number_of_variables
-            fprintf(1,'%s: \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \n',varargin{5}.name{i}, v(i,1), v(i,end), mean(v(i,:),2), min(v(i,:),[],2), max(v(i,:),[],2));
+            fprintf(1,'%s: \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \t\t\t %+8.6f \n',varargin{6}.name{i}, v(i,1), v(i,end), mean(v(i,:),2), min(v(i,:),[],2), max(v(i,:),[],2));
         end
         skipline()
     end
@@ -425,7 +432,7 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
                 disp(['values for the control variables. '])
                 disp(['New value of delta (size of the new simplex) is: '])
                 for i=1:number_of_variables
-                    fprintf(1,'%s: \t\t\t %+8.6f \n',varargin{5}.name{i}, delta(i));
+                    fprintf(1,'%s: \t\t\t %+8.6f \n',varargin{6}.name{i}, delta(i));
                 end
             end
             % Reset counters
@@ -472,7 +479,7 @@ while (func_count < max_func_calls) && (iter_count < max_iterations) && (simplex
                 disp(['values for the control variables. '])
                 disp(['New value of delta (size of the new simplex) is: '])
                 for i=1:number_of_variables
-                    fprintf(1,'%s: \t\t\t %+8.6f \n',varargin{5}.name{i}, delta(i));
+                    fprintf(1,'%s: \t\t\t %+8.6f \n',varargin{6}.name{i}, delta(i));
                 end
             end
             % Reset counters

matlab/simulated_moment_uncertainty.m

@@ -30,7 +30,7 @@ for j=1:replic;
     options_.noprint = 1;
     options_.order = 1;
     options_.periods = periods;
-    info = stoch_simul(options_.varobs);
+    info = stoch_simul(char(options_.varobs));
     dum=[oo_.mean; dyn_vech(oo_.var)];
     sd = sqrt(diag(oo_.var));
     for i=1:options_.ar;

matlab/stoch_simul.m

@@ -115,7 +115,7 @@ if ~options_.noprint
         skipline()
 
         if isfield(options_,'varobs')&& ~isempty(options_.varobs)
-            PCL_varobs=options_.varobs;
+            PCL_varobs=char(options_.varobs);
             disp('OBSERVED VARIABLES')
         else
             PCL_varobs=M_.endo_names;

matlab/subset.m

@@ -20,8 +20,6 @@ function jndx = subset()
 global options_ estim_params_ M_
 
 ExcludedParamNames = options_.ExcludedParams;
-VarObs = options_.varobs;
-VarExo = M_.exo_names;
 info   = options_.ParamSubSet;
 
 nvx = estim_params_.nvx;

matlab/utilities/dataset/compute_corr.m

@@ -1,59 +0,0 @@
-function dataset_ = compute_corr(dataset_) 
-% Computes the correlation matrix of the sample (possibly with missing observations).
-
-%@info:
-%! @deftypefn {Function File} {@var{dataset_} =} compute_corr(@var{dataset_})
-%! @anchor{compute_corr}
-%! This function computes covariance matrix of the sample (possibly with missing observations).
-%!
-%! @strong{Inputs}
-%! @table @var
-%! @item dataset_
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
-%! @end table
-%!
-%! @strong{Outputs}
-%! @table @var
-%! @item dataset_
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
-%! @end table
-%! 
-%! @strong{This function is called by:} 
-%! @ref{descriptive_statistics}.
-%! 
-%! @strong{This function calls:}
-%! @ref{ndim}, @ref{compute_cova}. 
-%!    
-%! @strong{Remark 1.} On exit, a new field is appended to the structure: @code{dataset_.descriptive.corr} is a 
-%! @tex{n\times n} vector (where @tex{n} is the number of observed variables as defined by @code{dataset_.info.nvobs}).
-%!
-%! @strong{Remark 2.} If @code{dataset_.descriptive.cova} does not exist, the covariance matrix is computed prior to the 
-%! computation of the correlation matrix.
-%!    
-%! @end deftypefn
-%@eod:
-
-% Copyright (C) 2011-2012 Dynare Team
-%    
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
-
-% Original author: stephane DOT adjemian AT univ DASH lemans DOT fr
-
-if ~isfield(dataset_.descriptive,'cova')
-    dataset_ = compute_cova(dataset_);
-end
-normalization_matrix = diag(1./sqrt(diag(dataset_.descriptive.cova)));
-dataset_.descriptive.corr = normalization_matrix*dataset_.descriptive.cova*normalization_matrix;

matlab/utilities/dataset/compute_cova.m

@@ -1,67 +0,0 @@
-function dataset_ = compute_cova(dataset_) 
-% Computes the covariance matrix of the sample (possibly with missing observations).
-
-%@info:
-%! @deftypefn {Function File} {@var{dataset_} =} compute_corr(@var{dataset_})
-%! @anchor{compute_corr}
-%! This function computes covariance matrix of the sample (possibly with missing observations).
-%!
-%! @strong{Inputs}
-%! @table @var
-%! @item dataset_
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
-%! @end table
-%!
-%! @strong{Outputs}
-%! @table @var
-%! @item dataset_
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
-%! @end table
-%! 
-%! @strong{This function is called by:} 
-%! @ref{descriptive_statistics}.
-%! 
-%! @strong{This function calls:}
-%! @ref{ndim}, @ref{demean}, @ref{nandemean}.
-%!    
-%! @strong{Remark 1.} On exit, a new field is appended to the structure: @code{dataset_.descriptive.cova} is a 
-%! @tex{n\times n} vector (where @tex{n} is the number of observed variables as defined by @code{dataset_.info.nvobs}).
-%!    
-%! @end deftypefn
-%@eod:
-    
-% Copyright (C) 2011-2012 Dynare Team
-%    
-% This file is part of Dynare.
-%
-% Dynare is free software: you can redistribute it and/or modify
-% it under the terms of the GNU General Public License as published by
-% the Free Software Foundation, either version 3 of the License, or
-% (at your option) any later version.
-%
-% Dynare is distributed in the hope that it will be useful,
-% but WITHOUT ANY WARRANTY; without even the implied warranty of
-% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-% GNU General Public License for more details.
-%
-% You should have received a copy of the GNU General Public License
-% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
-
-% Original author: stephane DOT adjemian AT univ DASH lemans DOT fr
-
-dataset_.descriptive.cova = zeros(dataset_.nvobs);
-
-data = transpose(dataset_.data);
-
-for i=1:dataset_.info.nvobs
-    for j=i:dataset_.info.nvobs
-        if dataset_.missing.state
-            dataset_.descriptive.cova(i,j) = nanmean(nandemean(data(:,i)).*nandemean(data(:,j)));
-        else
-            dataset_.descriptive.cova(i,j) = mean(demean(data(:,i)).*demean(data(:,j)));
-        end
-        if j>i
-            dataset_.descriptive.cova(j,i) = dataset_.descriptive.cova(i,j);
-        end
-    end
-end

matlab/utilities/dataset/describe_missing_data.m

@@ -26,9 +26,9 @@ function [i,n,s,j] = describe_missing_data(data)
 %!
 %! @end deftypefn
 %@eod:
-    
+
-% Copyright (C) 2008-2012 Dynare Team
+% Copyright (C) 2008-2014 Dynare Team
-%    
+%
 % This file is part of Dynare.
 %
 % Dynare is free software: you can redistribute it and/or modify
@@ -85,7 +85,7 @@ end
 
 
 %@test:1
-%$ % Define a data set.    
+%$ % Define a data set.
 %$ A = [ 1    1   ;   ...
 %$       1    NaN ;   ...
 %$       NaN  1   ;   ...
@@ -99,7 +99,7 @@ end
 %$       1    1  ];
 %$
 %$ % Define expected results.
-%$ eB = cell(1,11);   
+%$ eB = cell(1,11);
 %$ eB(1)  = { transpose(1:2) };
 %$ eB(2)  = { 1 };
 %$ eB(3)  = { 2 };
@@ -107,7 +107,7 @@ end
 %$ eB(5)  = { [] };
 %$ eB(6)  = { 1 };
 %$ eB(7)  = { 1 };
-%$ eB(8)  = { transpose(1:2) };    
+%$ eB(8)  = { transpose(1:2) };
 %$ eB(9)  = { transpose(1:2) };
 %$ eB(10) = { transpose(1:2) };
 %$ eB(11) = { transpose(1:2) };
@@ -117,7 +117,7 @@ end
 %$ eE(1) = { [1; 2; 4; transpose(6:11)] };
 %$ eE(2) = { [1; 3; 4; transpose(8:11)] };
 %$
-%$ % Call the tested routine.    
+%$ % Call the tested routine.
 %$ [B,C,D,E] = describe_missing_data(transpose(A));
 %$
 %$ % Check the results.

matlab/utilities/dataset/initialize_dataset.m

@@ -1,4 +1,4 @@
-function dataset_ = initialize_dataset(datafile,varobs,first,nobs,transformation,prefilter,xls)
+function dataset_ = initialize_dataset(datafile,varobs,first,nobs,logged_data_flag,prefilter,xls)
 % Initializes a structure describing the dataset.
 
 % Copyright (C) 2011-2013 Dynare Team
@@ -18,8 +18,6 @@ function dataset_ = initialize_dataset(datafile,varobs,first,nobs,transformation
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-% Original author: stephane DOT adjemian AT univ DASH lemans DOT fr
-
 if isempty(datafile)
     error('Estimation::initialize_dataset: You have to declare a dataset file!')
 end
@@ -43,7 +41,7 @@ end
 
 % Fill the dataset structure
 dataset_.info.ntobs = nobs;
-dataset_.info.nvobs = rows(varobs);
+dataset_.info.nvobs = length(varobs);
 dataset_.info.varobs = varobs;
 
 % Test the number of variables in the database.
@@ -61,9 +59,9 @@ if size(rawdata,1)~=dataset_.info.ntobs
 end
 rawdata = rawdata(first:(first+dataset_.info.ntobs-1),:);
 
-% Take the log (or anything else) of the variables if needed
+% Take the log of the variables if needed
-if isempty(transformation)
+if logged_data_flag
-    dataset_.rawdata = rawdata;
+    dataset_.rawdata = log(rawdata);
 else
     if isequal(transformation,@log)
         if ~isreal(rawdata)
@@ -101,4 +99,4 @@ if prefilter == 1
     dataset_.data = transpose(bsxfun(@minus,dataset_.rawdata,dataset_.descriptive.mean));
 else
     dataset_.data = transpose(dataset_.rawdata);
-end
+end

matlab/utilities/dataset/makedataset.m

@@ -0,0 +1,241 @@
+function [DynareDataset, DatasetInfo] = makedataset(DynareOptions, initialconditions, gsa_flag)
+
+% Initialize a dataset as a dseries object.
+%
+%
+% INPUTS
+% ======
+%
+%     DynareOptions [struct] Structure of options built by Dynare's preprocessor.
+%
+%
+% OUTPUTS
+% =======
+%
+%     DynareDataset [dseries]  The dataset.
+%     DatasetInfo   [struct]   Various informations about the dataset (descriptive statistics and missing observations).
+%
+% EXAMPLE
+% =======
+%
+%     [dataset_, dataset_info] = makedataset(options_) ;
+%
+%
+% See also dynare_estimation_init
+
+if nargin<3
+    gsa_flag = 0;
+end
+
+if nargin<2 || isempty(initialconditions)
+    % If a the sample is to be used for the estimation of a VAR or DSGE-VAR model
+    % the second argument must be a strictly positive integer (the number of lags).
+    initialconditions = 0;
+end
+
+if isempty(DynareOptions.datafile) && isempty(DynareOptions.dataset.file) && isempty(DynareOptions.dataset.series)
+    if gsa_flag
+        DynareDataset = dseries();
+        DatasetInfo = [];
+        return
+    else
+        error('makedataset: datafile option is missing!')
+    end
+end
+
+if isempty(DynareOptions.datafile) && ~isempty(DynareOptions.dataset.file)
+    datafile = DynareOptions.dataset.file;
+    newdatainterface = 1;
+elseif isempty(DynareOptions.datafile) && ~isempty(DynareOptions.dataset.series)
+    try
+        dseriesobjectforuserdataset = evalin('base', DynareOptions.dataset.series);
+    catch
+        error(sprintf('makedataset: %s is unknown!', DynareOptions.dataset.series))
+    end
+    if ~isdseries(dseriesobjectforuserdataset)
+        error(sprintf('makedataset: %s has to be a dseries object!', DynareOptions.dataset.series))
+    end
+    datafile = [];
+    newdatainterface = 1;
+elseif ~isempty(DynareOptions.datafile) && isempty(DynareOptions.dataset.file)
+    datafile = DynareOptions.datafile;
+    newdatainterface = 0;
+elseif isempty(DynareOptions.datafile) && ~isempty(DynareOptions.dataset.file)
+    error('You cannot use simultaneously the data command and the datafile option (in the estimation command)!')
+else
+    error('You have to specify the datafile!')
+end
+
+% Check extension.
+if ~isempty(datafile)
+    allowed_extensions = {'m','mat','csv','xls','xlsx'};
+    datafile_extension = get_file_extension(datafile);
+    if isempty(datafile_extension)
+        available_extensions = {}; j = 1;
+        for i=1:length(allowed_extensions)
+            if exist([datafile '.' allowed_extensions{i}])
+                available_extensions(j) = {allowed_extensions{i}};
+                j = j+1;
+            end
+        end
+        if isempty(available_extensions)
+            error(['I can''t find a datafile (with allowed extension)!'])
+        end
+        if length(available_extensions)>1
+            error(sprintf(['You did not specify an extension for the datafile, but more than one candidate ' ...
+                           'are available in the designed folder!\nPlease, add an extension to the datafile ' ...
+                           '(m, mat, csv, xls or xlsx are legal extensions).']));
+        end
+        datafile = [datafile '.' available_extensions{1}];
+    end
+end
+
+% Load the data in a dseries object.
+if ~isempty(datafile)
+    DynareDataset = dseries(datafile);
+else
+    DynareDataset = dseriesobjectforuserdataset;
+    clear('dseriesobjectforuserdataset');
+end
+
+% Select a subset of the variables.
+DynareDataset = DynareDataset{DynareOptions.varobs{:}};
+
+% Apply log function if needed.
+if DynareOptions.loglinear && ~DynareOptions.logdata
+    DynareDataset = DynareDataset.log();
+end
+
+% Test if an initial period (different from its default value) is explicitely defined in the datafile.
+if isequal(DynareDataset.init, dates(1,1))
+    dataset_default_initial_period = 1;
+else
+    dataset_default_initial_period = 0;
+end
+
+%  Test if an initial period (different from its default value) is explicitely defined in the mod file with the set_time command.
+if isequal(DynareOptions.initial_period, dates(1,1))
+    set_time_default_initial_period = 1;
+else
+    set_time_default_initial_period = 0;
+end
+
+if ~set_time_default_initial_period && dataset_default_initial_period
+    % Overwrite the initial period in dataset (it was set to default).
+    % Note that the updates of freq and time members are auto-magically
+    % done by dseries::subsasgn overloaded method.
+    DynareDataset.init = DynareOptions.initial_period;
+end
+
+if set_time_default_initial_period && ~dataset_default_initial_period
+    % Overwrite the global initial period defined by set_time (it was set to default).
+    DynareOptions.initial_period = DynareDataset.init;
+end
+
+if ~set_time_default_initial_period && ~dataset_default_initial_period
+    % Check if dataset.init and options_.initial_period are identical.
+    if DynareOptions.initial_period>DynareDataset.init
+        %if  ~isequal(DynareOptions.initial_period, DynareDataset.init)
+        error('The date as defined by the set_time command is not consistent with the initial period in the database!')
+    end
+end
+
+% Set firstobs, lastobs and nobs
+if newdatainterface
+    if isempty(DynareOptions.dataset.firstobs)
+        % first_obs option was not used in the data command.
+        firstobs = DynareDataset.init;
+    else
+        firstobs = DynareOptions.dataset.firstobs;
+    end
+    if isnan(DynareOptions.dataset.nobs)
+        % nobs option was not used in the data command.
+        if isempty(DynareOptions.dataset.lastobs)
+            % last_obs option was not used in the data command.
+            nobs = DynareDataset.nobs;
+            lastobs = DynareDataset.dates(end);
+        else
+            lastobs = DynareOptions.dataset.lastobs;
+            nobs = lastobs-firstobs+1;
+        end
+    else
+        nobs = DynareOptions.dataset.nobs;
+        if isempty(DynareOptions.dataset.lastobs)
+            % last_obs option was not used in the data command.
+            lastobs = firstobs+(nobs-1);
+        else
+            % last_obs and nobs were used in the data command. Check that they are consistent (with firstobs).
+            if ~isequal(lastobs,firstobs+(nobs-1))
+                error(sprintf('Options last_obs (%s), first_obs (%s) and nobs (%s) are not consistent!',char(lastobs),char(firstobs),num2str(nobs)));
+            end
+        end
+    end
+else
+    if isnan(DynareOptions.first_obs)
+        firstobs = DynareDataset.init;
+    else
+        firstobs = DynareDataset.dates(DynareOptions.first_obs);
+    end
+    if isnan(DynareOptions.nobs)
+        lastobs = DynareDataset.dates(end);
+        nobs = lastobs-firstobs+1;
+    else
+        nobs = DynareOptions.nobs;
+        lastobs = firstobs+(nobs-1);
+    end
+end
+
+% Add initial conditions if needed
+FIRSTOBS = firstobs-initialconditions;
+
+% Check that firstobs belongs to DynareDataset.dates
+if firstobs<DynareDataset.init
+    error(sprintf('first_obs (%s) cannot be less than the first date in the dataset (%s)!',char(firstobs),char(DynareDataset.init)))
+end
+
+% Check that FIRSTOBS belongs to DynareDataset.dates
+if initialconditions && FIRSTOBS<DynareDataset.init
+    error(sprintf('first_obs (%s) - %i cannot be less than the first date in the dataset (%s)!\nReduce the number of lags in the VAR model or increase the value of first_obs.', char(firstobs), initialconditions, char(DynareDataset.init)));
+end
+
+% Check that lastobs belongs to DynareDataset.dates...
+if newdatainterface
+    if lastobs>DynareDataset.dates(end)
+        error(sprintf('last_obs (%s) cannot be greater than the last date in the dataset (%s)!',char(lastobs),char(DynareDataset.dates(end))))
+    end
+else
+    % ...  or check that nobs is smaller than the number of observations in DynareDataset.
+    if nobs>DynareDataset.nobs
+        error(sprintf('nobs (%s) cannot be greater than the last date in the dataset (%s)!', num2str(nobs), num2str(nobs)))
+    end
+end
+
+% Select a subsample.
+DynareDataset = DynareDataset(FIRSTOBS:lastobs);
+
+% Initialize DatasetInfo structure.
+DatasetInfo = struct('missing', struct('state', NaN, 'aindex', [], 'vindex', [], 'number_of_observations', NaN, 'no_more_missing_observations', NaN), ...
+                     'descriptive', struct('mean', [], 'covariance', [], 'correlation', [], 'autocovariance', []));
+
+% Fill DatasetInfo.missing if some observations are missing
+DatasetInfo.missing.state = isanynan(DynareDataset.data);
+if DatasetInfo.missing.state
+    [DatasetInfo.missing.aindex, DatasetInfo.missing.number_of_observations, DatasetInfo.missing.no_more_missing_observations, DatasetInfo.missing.vindex] = ...
+        describe_missing_data(DynareDataset.data);
+else
+    DatasetInfo.missing.aindex = num2cell(transpose(repmat(1:DynareDataset.vobs,DynareDataset.nobs,1)),1);
+    DatasetInfo.missing.no_more_missing_observations = 1;
+end
+
+% Compute the empirical mean of the observed variables.
+DatasetInfo.descriptive.mean = nanmean(DynareDataset.data);
+
+% Compute the empirical covariance matrix of the observed variables.
+DatasetInfo.descriptive.covariance = nancovariance(DynareDataset.data);
+
+% Compute the empirical correlation matrix of the observed variables.
+normalization_matrix = diag(1./sqrt(diag(DatasetInfo.descriptive.covariance)));
+DatasetInfo.descriptive.correlation = normalization_matrix*DatasetInfo.descriptive.covariance*normalization_matrix;
+
+% Compute autocorrelation function.
+DatasetInfo.descriptive.autocovariance = nanautocovariance(DynareDataset.data, DynareOptions.ar);

matlab/utilities/dataset/nanautocovariance.m

@@ -1,4 +1,4 @@
-function dataset_ = compute_acov(dataset_) 
+function autocov = nanautocovariance(data,order)
 % Computes the (multivariate) auto-covariance function of the sample (possibly with missing observations).
 
 %@info:
@@ -8,36 +8,36 @@ function dataset_ = compute_acov(dataset_)
 %!
 %! @strong{Inputs}
 %! @table @var
-%! @item dataset_
+%! @item data
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
+%! T*N array of real numbers.
-%! @item nlag
+%! @item order
-%! Integer scalar. The maximum number of lags of the autocovariance function.    
+%! Integer scalar. The maximum number of lags of the autocovariance function.
 %! @end table
 %!
 %! @strong{Outputs}
 %! @table @var
-%! @item dataset_
+%! @item autocov
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
+%! A N*N*order array of real numbers.
 %! @end table
-%! 
+%!
-%! @strong{This function is called by:} 
+%! @strong{This function is called by:}
 %! @ref{descriptive_statistics}.
-%! 
+%!
 %! @strong{This function calls:}
-%! @ref{ndim}, @ref{compute_cova}, @ref{demean}, @ref{nandemean}.
+%! @ref{ndim}, @ref{nancovariance}, @ref{demean}, @ref{nandemean}.
-%!    
+%!
-%! @strong{Remark 1.} On exit, a new field is appended to the structure: @code{dataset_.descriptive.acov} is a 
+%! @strong{Remark 1.} On exit, a new field is appended to the structure: @code{dataset_.descriptive.acov} is a
-%! @tex{n\times n\times p} array (where @tex{n} is the number of observed variables as defined by @code{dataset_.info.nvobs}, 
+%! @tex{n\times n\times p} array (where @tex{n} is the number of observed variables as defined by @code{dataset_.info.nvobs},
 %! and @tex{n} is the maximum number of lags given by the second input @code{nlag}).
-%!    
+%!
-%! @strong{Remark 2.} If @code{dataset_.descriptive.cova} does not exist, the covariance matrix is computed prior to the 
+%! @strong{Remark 2.} If @code{dataset_.descriptive.cova} does not exist, the covariance matrix is computed prior to the
 %! computation of the auto-covariance function.
 %!
 %! @end deftypefn
 %@eod:
 
-% Copyright (C) 2011-2012 Dynare Team
+% Copyright (C) 2011-2014 Dynare Team
-%    
+%
 % This file is part of Dynare.
 %
 % Dynare is free software: you can redistribute it and/or modify
@@ -53,22 +53,23 @@ function dataset_ = compute_acov(dataset_)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-% Original author: stephane DOT adjemian AT univ DASH lemans DOT fr
+n = size(data,2);
+missing = isanynan(data);
 
-if ~isfield(dataset_.descriptive,'cova')
+autocov = zeros(n, n, order);
-    dataset_ = compute_cova(dataset_);
-end
-dataset_.descriptive.acov = zeros(dataset_.nvobs,dataset_.nvobs,nlag);
 
-data = transpose(dataset_.data);
+for lag=1:order
-
+    if missing
-for lag=1:nlag
+        data = nandemean(data);
-    for i=1:dataset_.info.nvobs
+    else
-        for j=1:dataset_.info.nvobs
+        data = demean(data);
-            if dataset_.missing.state
+    end
-                dataset_.descriptive.acov(i,j,lag) = nanmean(nandemean(data(lag+1:end,i)).*nandemean(data(1:end-lag,j)));
+    for i=1:n
+        for j=1:n
+            if missing
+                autocov(i,j,lag) = nanmean(data((lag+1):end,i).*data(1:end-lag,j));
             else
-                dataset_.descriptive.acov(i,j,lag) = mean(demean(data(lag+1:end,i)).*demean(data(1:end-lag,j)));
+                autocov(i,j,lag) = mean(data((lag+1):end,i).*data(1:end-lag,j));
             end
         end
     end

matlab/utilities/dataset/nancovariance.m

@@ -0,0 +1,101 @@
+function CovarianceMatrix = nancovariance(data)
+% Computes the covariance matrix of a sample (possibly with missing observations).
+
+%@info:
+%! @deftypefn {Function File} {@var{CovarianceMatrix} =} compute_corr(@var{data})
+%! @anchor{compute_cova}
+%! This function computes covariance matrix of a sample defined by a dseries object (possibly with missing observations).
+%!
+%! @strong{Inputs}
+%! @table @var
+%! @item data
+%! a T*N array of real numbers.
+%! @end table
+%!
+%! @strong{Outputs}
+%! @table @var
+%! @item CovarianceMatrix
+%! Array of real numbers.
+%! @end table
+%!
+%! @strong{This function is called by:}
+%! @ref{descriptive_statistics}.
+%!
+%! @strong{This function calls:}
+%! @ref{ndim}, @ref{demean}, @ref{nandemean}.
+%!
+%! @strong{Remark 1.} On exit, a new field is appended to the structure: @code{dataset_.descriptive.cova} is a
+%! @tex{n\times n} vector (where @tex{n} is the number of observed variables as defined by @code{dataset_.info.nvobs}).
+%!
+%! @end deftypefn
+%@eod:
+
+% Copyright (C) 2011-2014 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+% Initialize the output.
+CovarianceMatrix = zeros(size(data,2));
+
+if isanynan(data)
+    data = bsxfun(@minus,data,nanmean(data));
+    for i=1:size(data,2)
+        for j=i:size(data,2)
+            CovarianceMatrix(i,j) = nanmean(data(:,i).*data(:,j));
+            if j>i
+                CovarianceMatrix(j,i) = CovarianceMatrix(i,j);
+            end
+        end
+    end
+else
+    data = bsxfun(@minus,data,mean(data));
+    CovarianceMatrix = (transpose(data)*data)/size(data,1);
+end
+
+%@test:1
+%$
+%$ % Define a dataset.
+%$ data1 = randn(10000000,2);
+%$
+%$ % Same dataset with missing observations.
+%$ data2 = data1;
+%$ data2(45,1) = NaN;
+%$ data2(57,2) = NaN;
+%$ data2(367,:) = NaN(1,2);
+%$
+%$ t = zeros(2,1);
+%$
+%$ % Call the tested routine.
+%$ try
+%$     c1 = nancovariance(data1);
+%$     t(1) = 1;
+%$ catch
+%$     t(1) = 0;
+%$ end
+%$ try
+%$     c2 = nancovariance(data2);
+%$     t(2) = 1;
+%$ catch
+%$     t(2) = 0;
+%$ end
+%$
+%$ if t(1) && t(2)
+%$     t(3) = max(max(abs(c1-c2)))<1e-4;
+%$ end
+%$
+%$ % Check the results.
+%$ T = all(t);
+%@eof:1

matlab/utilities/dataset/nanmoments.m

@@ -0,0 +1,25 @@
+function m = nanmoments(data, n)
+% Compute centered marginal moments of order n (possibly with missing observations).
+
+% Copyright (C) 2014 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+if isanynan(data)
+    m = transpose(nanmean(bsxfun(@power,nandemean(data),n)));
+else
+    m = transpose(mean(bsxfun(@power,demean(data),n)));
+end

matlab/utilities/dataset/nanvariance.m

@@ -1,21 +1,21 @@
-function dataset_ = compute_stdv(dataset_) 
+function variances = nanvariance(data) 
 % Compute the standard deviation for each observed variable (possibly with missing observations).
 
 %@info:
-%! @deftypefn {Function File} {@var{dataset_} =} compute_stdv(@var{dataset_})
+%! @deftypefn {Function File} {@var{variances} =} nanvariance(@var{data})
-%! @anchor{compute_stdv}
+%! @anchor{nanvariance}
-%! This function computes the standard deviation of the observed variables (possibly with missing observations).
+%! This function computes the variances of the observed variables (possibly with missing observations).
 %!
 %! @strong{Inputs}
 %! @table @var
-%! @item dataset_
+%! @item datas
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
+%! A T*N array of real numbers.
 %! @end table
 %!
 %! @strong{Outputs}
 %! @table @var
-%! @item dataset_
+%! @item variances
-%! Dynare structure describing the dataset, built by @ref{initialize_dataset}
+%! A N*1 vector of real numbers
 %! @end table
 %! 
 %! @strong{This function is called by:} 
@@ -30,7 +30,7 @@ function dataset_ = compute_stdv(dataset_)
 %! @end deftypefn
 %@eod:
 
-% Copyright (C) 2011-2012 Dynare Team
+% Copyright (C) 2011-2014 Dynare Team
 %    
 % This file is part of Dynare.
 %
@@ -47,10 +47,8 @@ function dataset_ = compute_stdv(dataset_)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-% Original author: stephane DOT adjemian AT univ DASH lemans DOT fr
+if isanynan(data)
-
+    variances = transpose(nanmean(bsxfun(@power,nandemean(data),2)));
-if dataset_.missing.state
-    dataset_.descriptive.stdv = sqrt(nanmean(bsxfun(@power,nandemean(transpose(dataset_.data)),2)));
 else
-    dataset_.descriptive.stdv = sqrt(mean(bsxfun(@power,demean(transpose(dataset_.data)),2)));
+    variances = transpose(mean(bsxfun(@power,demean(data),2)));
 end

matlab/utilities/dates/isdate.m

@@ -25,15 +25,16 @@ function b = isdate(str)  % --*-- Unitary tests --*--
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-%if length(str)>1
+if isnumeric(str) && isscalar(str)
-    b = isquaterly(str) || isyearly(str) || ismonthly(str) || isweekly(str);
+    b = 1;
-    %else
+    return
-    %b = 0;
+end
-    %end
+
+b = isstringdate(str);
 
 %@test:1
 %$
-%$ date_1 = '1950M2';
+%$ date_1 = 1950;
 %$ date_2 = '1950m2';
 %$ date_3 = '-1950m2';
 %$ date_4 = '1950m52';

matlab/utilities/dates/isfreq.m

@@ -25,4 +25,15 @@ function B = isfreq(A)
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-B = (ischar(A) && ismember(upper(A),{'Y','A','Q','M','W'})) || ismember(A,[1 4 12 52]);
+B = 0;
+
+if ischar(A)
+    if isequal(length(A),1) && ismember(upper(A),{'Y','A','Q','M','W'})
+        B = 1;
+        return
+    end
+end
+
+if isnumeric(A) && isequal(length(A),1) && ismember(A,[1 4 12 52])
+    B = 1;
+end

matlab/utilities/dates/isstringdate.m

@@ -0,0 +1,54 @@
+function b = isstringdate(str)  % --*-- Unitary tests --*--
+
+% Tests if the input string can be interpreted as a date.
+%
+% INPUTS 
+%  o str     string.
+%
+% OUTPUTS 
+%  o b       integer scalar, equal to 1 if str can be interpreted as a date or 0 otherwise.
+
+% Copyright (C) 2013 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+if ischar(str)
+    b = isquaterly(str) || isyearly(str) || ismonthly(str) || isweekly(str);
+else
+    b = 0;
+end
+
+%@test:1
+%$
+%$ date_1 = '1950M2';
+%$ date_2 = '1950m2';
+%$ date_3 = '-1950m2';
+%$ date_4 = '1950m52';
+%$ date_5 = ' 1950';
+%$ date_6 = '1950Y';
+%$ date_7 = '-1950a';
+%$ date_8 = '1950m ';
+%$
+%$ t(1) = dyn_assert(isstringdate(date_1),1);
+%$ t(2) = dyn_assert(isstringdate(date_2),1);
+%$ t(3) = dyn_assert(isstringdate(date_3),1);
+%$ t(4) = dyn_assert(isstringdate(date_4),0);
+%$ t(5) = dyn_assert(isstringdate(date_5),0);
+%$ t(6) = dyn_assert(isstringdate(date_6),1);
+%$ t(7) = dyn_assert(isstringdate(date_7),1);
+%$ t(8) = dyn_assert(isstringdate(date_8),0);
+%$ T = all(t);
+%@eof:1

matlab/utilities/dseries/from.m

@@ -1,4 +1,4 @@
-function from(varargin)
+function from(varargin)   % --*-- Unitary tests --*--
 
 % Copyright (C) 2014 Dynare Team
 %
@@ -219,10 +219,10 @@ for i=1:number_of_variables
         end
         if i>1
             if ismember(var.name,variable_names)
-                error('dseries::from: All the dseries objects should contain variables with different names!')
+                % Locally change variable name.
-            else
+                var = var.rename(var.name{1},get_random_string(20));
-                variable_names(i) = {var.name{1}};
             end
+            variable_names(i) = {var.name{1}};
         else
             variable_names(i) = {var.name{1}};
         end
@@ -421,4 +421,18 @@ function i = isassignedvariable(var,expr)
             return
         end
     end
-    i = 0;
+    i = 0;
+
+%@test:1
+%$ try
+%$     y = dseries(zeros(400,1),dates('1950Q1')) ;
+%$     v = dseries(randn(400,1),dates('1950Q1')) ;
+%$     u = dseries(randn(400,1),dates('1950Q1')) ;
+%$     from 1950Q2 to 2049Q4 do y(t) = (1+.01*u(t))*y(t-1) + v(t)
+%$     t(1) = 1;
+%$ catch
+%$     t(1) = 0;
+%$ end
+%$
+%$ T = all(t);
+%@eof:1

matlab/utilities/general/isanynan.m

@@ -0,0 +1,21 @@
+function yes = isanynan(array)
+% Return one if the array contains at least one NaN, 0 otherwise.
+
+% Copyright (C) 2011-2014 Dynare Team
+%
+% This file is part of Dynare.
+%
+% Dynare is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% Dynare is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
+
+yes = any(isnan(array(:)));

matlab/var_sample_moments.m

@@ -1,29 +1,28 @@
-function [YtY,XtY,YtX,XtX,Y,X] = ...
+function var_sample_moments(nlag, var_trend_order, dataset_)%datafile,varobs,xls_sheet,xls_range)
-    var_sample_moments(FirstObservation,LastObservation,qlag,var_trend_order,datafile,varobs,xls_sheet,xls_range)
 % Computes the sample moments of a VAR model.
 %
 % The VAR(p) model is defined by:
 %
-%   y_t = \sum_{k=1}^p y_{t-k} A_k + z_t C + e_t  for t = 1,...,T  
+%   y_t = \sum_{k=1}^p y_{t-k} A_k + z_t C + e_t  for t = 1,...,T
 %
 % where y_t is a 1*m vector of observed endogenous variables, p is the
 % number of lags, A_k is an m*m real matrix, z_t is a 1*q vector of
 % exogenous (deterministic) variables, C is a q*m real matrix and
 % e_t is a vector of exogenous stochastic shocks. T is the number
-% of observations. The deterministic exogenous variables are assumed to 
+% of observations. The deterministic exogenous variables are assumed to
-% be a polynomial trend of order q = "var_trend_order".  
+% be a polynomial trend of order q = "var_trend_order".
 %
-% We define: 
+% We define:
 %
 %  <>  Y = (y_1',y_2',...,y_T')' a T*m matrix,
 %
-%  <>  x_t = (y_{t-1},y_{t-2},...,y_{t-p},z_t) a 1*(mp+q) row vector, 
+%  <>  x_t = (y_{t-1},y_{t-2},...,y_{t-p},z_t) a 1*(mp+q) row vector,
 %
-%  <>  X = (x_1',x_2',...,x_T')' a T*(mp+q) matrix, 
+%  <>  X = (x_1',x_2',...,x_T')' a T*(mp+q) matrix,
 %
 %  <>  E = (e_1',e_2',...,e_T')' a T*m matrix and
 %
-%  <>  A = (A_1',A_2',...,A_p',C')' an (mp+q)*m matrix of coefficients.   
+%  <>  A = (A_1',A_2',...,A_p',C')' an (mp+q)*m matrix of coefficients.
 %
 % So that we can equivalently write the VAR(p) model using the following
 % matrix representation:
@@ -31,18 +30,17 @@ function [YtY,XtY,YtX,XtX,Y,X] = ...
 %   Y = X * A +E
 %
 %
-% INPUTS 
+% INPUTS
-%   o FirstObservation    [integer] First observation.
+%   o nlag                [integer] Number of lags in the VAR model.
-%   o LastObservation     [integer] Last observation.
+%   o var_trend_order     [integer] Order of the polynomial exogenous trend:
-%   o qlag                [integer] Number of lags in the VAR model.
-%   o var_trend_order     [integer] Order of the polynomial exogenous trend: 
 %                                       = -1 no constant and no linear trend,
 %                                       =  0 constant and no linear trend,
 %                                       =  1 constant and linear trend.
+%   o dataset_            [dseries] The sample.
 %
-% OUTPUTS 
+% OUTPUTS
 %   o YtY                 [double]  Y'*Y an m*m matrix.
-%   o XtY                 [double]  X'*Y an (mp+q)*m matrix. 
+%   o XtY                 [double]  X'*Y an (mp+q)*m matrix.
 %   o YtX                 [double]  Y'*X an m*(mp+q) matrix.
 %   o XtX                 [double]  X'*X an (mp+q)*(mp+q) matrix.
 %   o Y                   [double]  Y a T*m matrix.
@@ -50,8 +48,11 @@ function [YtY,XtY,YtX,XtX,Y,X] = ...
 %
 % SPECIAL REQUIREMENTS
 %   None.
+%
+% REMARKS
+%   Outputs are saved in the base workspace (not returned by the function).
 
-% Copyright (C) 2007-2009 Dynare Team
+% Copyright (C) 2007-2014 Dynare Team
 %
 % This file is part of Dynare.
 %
@@ -68,42 +69,35 @@ function [YtY,XtY,YtX,XtX,Y,X] = ...
 % You should have received a copy of the GNU General Public License
 % along with Dynare.  If not, see <http://www.gnu.org/licenses/>.
 
-X = [];
+LastObservation = dataset_.dates(end);
-Y = [];
+FirstObservation = dataset_.dates(1)+nlag;
-YtY = [];
-YtX = [];
-XtY = [];
-XtX = [];
 
-data = read_variables(datafile,varobs,[],xls_sheet,xls_range);
+NumberOfObservations = LastObservation-FirstObservation+1;
+NumberOfVariables = dataset_.vobs;
 
-if qlag > FirstObservation
+if isequal(var_trend_order,-1)
-    error('VarSampleMoments :: not enough data to initialize! Try to increase FirstObservation.')
+    % No constant no linear trend case.
-    return
+    X = zeros(NumberOfObservations,NumberOfVariables*nlag);
-end
+elseif isequal(var_trend_order,0)
-
+    % Constant and no linear trend case.
-NumberOfObservations = LastObservation-FirstObservation+1;% This is T.
+    X = ones(NumberOfObservations,NumberOfVariables*nlag+1);
-NumberOfVariables = size(varobs,1);% This is m.
+    indx = NumberOfVariables*nlag+1;
-if var_trend_order == -1% No constant no linear trend case.
+elseif isequal(var_trend_order,1)
-    X = zeros(NumberOfObservations,NumberOfVariables*qlag);
+    % Constant and linear trend case.
-elseif var_trend_order == 0% Constant and no linear trend case.
+    X = ones(NumberOfObservations,NumberOfVariables*nlag+2);
-X = ones(NumberOfObservations,NumberOfVariables*qlag+1);
+    indx = NumberOfVariables*nlag+1:NumberOfVariables*nlag+2;
-indx = NumberOfVariables*qlag+1;
-elseif var_trend_order == 1;% Constant and linear trend case.
-X = ones(NumberOfObservations,NumberOfVariables*qlag+2);
-indx = NumberOfVariables*qlag+1:NumberOfVariables*qlag+2;
 else
-    error('var_sample_moments :: trend must be equal to -1,0 or 1!')
+    error('Estimation::var_sample_moments: trend must be equal to -1,0 or 1!')
     return
 end
 
-% I build matrices Y and X  
+% I build matrices Y and X
-Y = data(FirstObservation:LastObservation,:);
+Y = dataset_(FirstObservation:LastObservation).data;
 
 for t=1:NumberOfObservations
-    line = t + FirstObservation-1;
+    currentdate = FirstObservation+(t-1);
-    for lag = 1:qlag
+    for lag = 1:nlag
-        X(t,(lag-1)*NumberOfVariables+1:lag*NumberOfVariables) = data(line-lag,:);
+        X(t,(lag-1)*NumberOfVariables+1:lag*NumberOfVariables) = dataset_(currentdate-lag).data;
     end
 end
 
@@ -111,7 +105,9 @@ if (var_trend_order == 1)
     X(:,end) = transpose(1:NumberOfObservations)
 end
 
-YtY = Y'*Y;
+assignin('base', 'mYY', Y'*Y);
-YtX = Y'*X;
+assignin('base', 'mYX', Y'*X);
-XtY = X'*Y;
+assignin('base', 'mXY', X'*Y);
-XtX = X'*X;
+assignin('base', 'mXX', X'*X);
+assignin('base', 'Ydata', Y);
+assignin('base', 'Xdata', X);

preprocessor/ComputingTasks.cc

@@ -1876,9 +1876,17 @@ EstimationDataStatement::checkPass(ModFileStructure &mod_file_struct, WarningCon
         exit(EXIT_FAILURE);
       }
 
-  if (options_list.string_options.find("file") == options_list.string_options.end())
+  if ((options_list.string_options.find("file") == options_list.string_options.end()) &&
+      (options_list.string_options.find("series") == options_list.string_options.end()))
     {
-      cerr << "ERROR: The file option must be passed to the data statement." << endl;
+      cerr << "ERROR: The file or series option must be passed to the data statement." << endl;
+      exit(EXIT_FAILURE);
+    }
+
+  if ((options_list.string_options.find("file") != options_list.string_options.end()) &&
+      (options_list.string_options.find("series") != options_list.string_options.end()))
+    {
+      cerr << "ERROR: The file and series options cannot be used simultaneously in the data statement." << endl;
       exit(EXIT_FAILURE);
     }
 }
@@ -1887,8 +1895,8 @@ void
 EstimationDataStatement::writeOutput(ostream &output, const string &basename) const
 {
   options_list.writeOutput(output, "options_.dataset");
-  if (options_list.date_options.find("first_obs") == options_list.date_options.end())
+  //if (options_list.date_options.find("first_obs") == options_list.date_options.end())
-    output << "options_.dataset.firstobs = options_.initial_period;" << endl;
+  //  output << "options_.dataset.first_obs = options_.initial_period;" << endl;
 }
 
 SubsamplesStatement::SubsamplesStatement(const string &name1_arg,

preprocessor/DynareBison.yy

@@ -90,7 +90,7 @@ class ParsingDriver;
 %token BVAR_REPLIC BYTECODE ALL_VALUES_REQUIRED
 %token CALIB_SMOOTHER CHANGE_TYPE CHECK CONDITIONAL_FORECAST CONDITIONAL_FORECAST_PATHS CONF_SIG CONSTANT CONTROLLED_VAREXO CORR COVAR CUTOFF CYCLE_REDUCTION LOGARITHMIC_REDUCTION
 %token CONSIDER_ALL_ENDOGENOUS CONSIDER_ONLY_OBSERVED
-%token DATAFILE FILE DOUBLING DR_CYCLE_REDUCTION_TOL DR_LOGARITHMIC_REDUCTION_TOL DR_LOGARITHMIC_REDUCTION_MAXITER DR_ALGO DROP DSAMPLE DYNASAVE DYNATYPE CALIBRATION DIFFERENTIATE_FORWARD_VARS
+%token DATAFILE FILE SERIES DOUBLING DR_CYCLE_REDUCTION_TOL DR_LOGARITHMIC_REDUCTION_TOL DR_LOGARITHMIC_REDUCTION_MAXITER DR_ALGO DROP DSAMPLE DYNASAVE DYNATYPE CALIBRATION DIFFERENTIATE_FORWARD_VARS
 %token END ENDVAL EQUAL ESTIMATION ESTIMATED_PARAMS ESTIMATED_PARAMS_BOUNDS ESTIMATED_PARAMS_INIT EXTENDED_PATH ENDOGENOUS_PRIOR
 %token FILENAME FILTER_STEP_AHEAD FILTERED_VARS FIRST_OBS LAST_OBS SET_TIME
 %token <string_val> FLOAT_NUMBER DATES
@@ -833,7 +833,7 @@ restriction_expression : expression {driver.check_restriction_expression_constan
 
 restriction_expression_1 : restriction_elem_expression
                          | restriction_expression_1 restriction_elem_expression
-                         ;   
+                         ;
 
 restriction_elem_expression : COEFF '(' symbol COMMA INT_NUMBER ')'
                                  { driver.add_positive_restriction_element($3,$5);}
@@ -1366,9 +1366,10 @@ data_options_list : data_options_list COMMA data_options
                   ;
 
 data_options : o_file
-             | o_new_estimation_data_first_obs
+             | o_series
-             | o_last_obs
+             | o_data_first_obs
-             | o_new_estimation_data_nobs
+             | o_data_last_obs
+             | o_data_nobs
              | o_xls_sheet
              | o_xls_range
              ;
@@ -2493,6 +2494,7 @@ o_simul_seed : SIMUL_SEED EQUAL INT_NUMBER { driver.error("'simul_seed' option i
 o_qz_criterium : QZ_CRITERIUM EQUAL non_negative_number { driver.option_num("qz_criterium", $3); };
 o_qz_zero_threshold : QZ_ZERO_THRESHOLD EQUAL non_negative_number { driver.option_num("qz_zero_threshold", $3); };
 o_file : FILE EQUAL filename { driver.option_str("file", $3); };
+o_series : SERIES EQUAL symbol { driver.option_str("series", $3); };
 o_datafile : DATAFILE EQUAL filename { driver.option_str("datafile", $3); };
 o_nobs : NOBS EQUAL vec_int
          { driver.option_vec_int("nobs", $3); }
@@ -2505,12 +2507,9 @@ o_conditional_variance_decomposition : CONDITIONAL_VARIANCE_DECOMPOSITION EQUAL
                                        { driver.option_vec_int("conditional_variance_decomposition", $3); }
                                      ;
 o_first_obs : FIRST_OBS EQUAL INT_NUMBER { driver.option_num("first_obs", $3); };
-o_new_estimation_data_first_obs : FIRST_OBS EQUAL date_expr
+o_data_first_obs : FIRST_OBS EQUAL date_expr { driver.option_date("firstobs", $3); } ;
-                                  { driver.option_date("first_obs", $3); }
+o_data_last_obs : LAST_OBS EQUAL date_expr { driver.option_date("lastobs", $3); } ;
-                                ;
+o_data_nobs : NOBS EQUAL INT_NUMBER { driver.option_num("nobs", $3); };
-o_last_obs : LAST_OBS EQUAL date_expr
-             { driver.option_date("last_obs", $3); }
-           ;
 o_shift : SHIFT EQUAL signed_number { driver.option_num("shift", $3); };
 o_shape : SHAPE EQUAL prior_distribution { driver.prior_shape = $3; };
 o_mode : MODE EQUAL signed_number { driver.option_num("mode", $3); };
@@ -2523,7 +2522,6 @@ o_bounds : BOUNDS EQUAL vec_value_w_inf { driver.option_num("bounds", $3); };
 o_domain : DOMAINN EQUAL vec_value { driver.option_num("domain", $3); };
 o_interval : INTERVAL EQUAL vec_value { driver.option_num("interval", $3); };
 o_variance : VARIANCE EQUAL expression { driver.set_prior_variance($3); }
-o_new_estimation_data_nobs : NOBS EQUAL INT_NUMBER { driver.option_num("nobs", $3); };
 o_prefilter : PREFILTER EQUAL INT_NUMBER { driver.option_num("prefilter", $3); };
 o_presample : PRESAMPLE EQUAL INT_NUMBER { driver.option_num("presample", $3); };
 o_lik_algo : LIK_ALGO EQUAL INT_NUMBER { driver.option_num("lik_algo", $3); };

preprocessor/DynareFlex.ll

@@ -604,6 +604,7 @@ DATE -?[0-9]+([YyAa]|[Mm]([1-9]|1[0-2])|[Qq][1-4]|[Ww]([1-9]{1}|[1-4][0-9]|5[0-2
 <DYNARE_STATEMENT>simul_replic {return token::SIMUL_REPLIC;}
 <DYNARE_STATEMENT>xls_sheet {return token::XLS_SHEET;}
 <DYNARE_STATEMENT>xls_range {return token::XLS_RANGE;}
+<DYNARE_STATEMENT>series {return token::SERIES;}
 <DYNARE_STATEMENT>mh_recover {return token::MH_RECOVER;}
 <DYNARE_STATEMENT>planner_discount {return token::PLANNER_DISCOUNT;}
 <DYNARE_STATEMENT>calibration {return token::CALIBRATION;}

preprocessor/Statement.cc

@@ -135,7 +135,20 @@ OptionsList::writeOutput(ostream &output) const
 void
 OptionsList::writeOutput(ostream &output, const string &option_group) const
 {
-  output << option_group << " = struct();" << endl;
+  // Initialize option_group as an empty struct iff the field does not exist!
+  unsigned idx = option_group.find_last_of(".");
+  if (idx<UINT_MAX)
+    {
+      output << option_group << endl;
+      output << idx << endl;
+      output << "if ~isfield(" << option_group.substr(0,idx) << ",'" << option_group.substr(idx+1) << "')" << endl;
+      output << "    " << option_group << " = struct();" << endl;
+      output << "end" << endl;
+    }
+  else
+    {
+      output << option_group << " = struct();" << endl;
+    }
 
   for (num_options_t::const_iterator it = num_options.begin();
        it != num_options.end(); it++)

preprocessor/SymbolTable.cc

@@ -270,14 +270,14 @@ SymbolTable::writeOutput(ostream &output) const throw (NotYetFrozenException)
 
   if (observedVariablesNbr() > 0)
     {
-      output << "options_.varobs = [];" << endl;
+      int ic = 1;
+      output << "options_.varobs = cell(1);" << endl;
       for (vector<int>::const_iterator it = varobs.begin();
            it != varobs.end(); it++)
-        if (it == varobs.begin())
+	{
-          output << "options_.varobs = '" << getName(*it) << "';" << endl;
+	  output << "options_.varobs(" << ic << ")  = {'" << getName(*it) << "'};" << endl;
-        else
+	  ic++;
-          output << "options_.varobs = char(options_.varobs, '" << getName(*it) << "');" << endl;
+	}
-
       output << "options_.varobs_id = [ ";
       for (vector<int>::const_iterator it = varobs.begin();
            it != varobs.end(); it++)

tests/Makefile.am

@@ -80,6 +80,7 @@ MODFILES = \
 	fs2000/fs2000e.mod \
 	fs2000/fs2000_cmaes.mod \
 	fs2000/fs2000_calib.mod \
+	fs2000/fs2000_calib_dseries.mod \
 	fs2000/fs2000_analytic_derivation.mod \
 	fs2000/fs2000_missing_data.mod \
 	fs2000/fs2000_sd.mod \

tests/fs2000/fs2000_calib_dseries.mod

@@ -0,0 +1,73 @@
+// See fs2000.mod in the examples/ directory for details on the model
+
+set_time(1950Q1);
+
+var m P c e W R k d n l gy_obs gp_obs y dA;
+varexo e_a e_m;
+
+parameters alp bet gam mst rho psi del;
+
+alp = 0.33;
+bet = 0.99;
+gam = 0.003;
+mst = 1.011;
+rho = 0.7;
+psi = 0.787;
+del = 0.02;
+
+model;
+dA = exp(gam+e_a);
+log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
+W = l/n;
+-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
+c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+P*c = m;
+m-1+d = l;
+e = exp(e_a);
+y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+gy_obs = dA*y/y(-1);
+gp_obs = (P/P(-1))*m(-1)/dA;
+end;
+
+initval;
+k = 6;
+m = mst;
+P = 2.25;
+c = 0.45;
+e = 1;
+W = 4;
+R = 1.02;
+d = 0.85;
+n = 0.19;
+l = 0.86;
+y = 0.6;
+gy_obs = exp(gam);
+gp_obs = exp(-gam);
+dA = exp(gam);
+end;
+
+shocks;
+var e_a; stderr 0.014;
+var e_m; stderr 0.005;
+end;
+
+steady;
+
+check;
+
+
+varobs gp_obs gy_obs;
+
+fsdata = dseries('fsdat_simul.m',1950Q1);
+fsdata = fsdata{'g@p,y@_obs'}(1960Q1:1980Q4);
+
+fsdata
+
+data(series=fsdata);//, first_obs=1960Q1, last_obs=1980Q4);
+
+
+calib_smoother(filtered_vars, filter_step_ahead = [3:4]) m P c e W R k d n l y dA;
+

tests/fs2000/fs2000_data.mod

@@ -0,0 +1,79 @@
+// See fs2000.mod in the examples/ directory for details on the model
+
+var m P c e W R k d n l gy_obs gp_obs y dA;
+varexo e_a e_m;
+
+parameters alp bet gam mst rho psi del;
+
+alp = 0.33;
+bet = 0.99;
+gam = 0.003;
+mst = 1.011;
+rho = 0.7;
+psi = 0.787;
+del = 0.02;
+
+model;
+dA = exp(gam+e_a);
+log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
+W = l/n;
+-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
+c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+P*c = m;
+m-1+d = l;
+e = exp(e_a);
+y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+gy_obs = dA*y/y(-1);
+gp_obs = (P/P(-1))*m(-1)/dA;
+end;
+
+initval;
+k = 6;
+m = mst;
+P = 2.25;
+c = 0.45;
+e = 1;
+W = 4;
+R = 1.02;
+d = 0.85;
+n = 0.19;
+l = 0.86;
+y = 0.6;
+gy_obs = exp(gam);
+gp_obs = exp(-gam);
+dA = exp(gam);
+end;
+
+shocks;
+var e_a; stderr 0.014;
+var e_m; stderr 0.005;
+end;
+
+steady;
+
+check;
+
+estimated_params;
+alp, beta_pdf, 0.356, 0.02;
+bet, beta_pdf, 0.993, 0.002;
+gam, normal_pdf, 0.0085, 0.003;
+mst, normal_pdf, 1.0002, 0.007;
+rho, beta_pdf, 0.129, 0.223;
+psi, beta_pdf, 0.65, 0.05;
+del, beta_pdf, 0.01, 0.005;
+stderr e_a, inv_gamma_pdf, 0.035449, inf;
+stderr e_m, inv_gamma_pdf, 0.008862, inf;
+end;
+
+varobs gp_obs gy_obs;
+
+options_.solve_tolf = 1e-12;
+
+set_time(1970Q3); // Interpreted as the first date available in the sample loaded below.
+
+data(file='fsdat_simul.m',first_obs=1971Q1, nobs=40);
+
+estimation(order=1,nobs=192,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8);

tests/fs2000/fs2000_dseries_a.mod

@@ -0,0 +1,77 @@
+set_time(1950Q3);
+
+var m P c e W R k d n l gy_obs gp_obs y dA;
+varexo e_a e_m;
+
+parameters alp bet gam mst rho psi del;
+
+alp = 0.33;
+bet = 0.99;
+gam = 0.003;
+mst = 1.011;
+rho = 0.7;
+psi = 0.787;
+del = 0.02;
+
+model;
+dA = exp(gam+e_a);
+log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
+W = l/n;
+-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
+c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+P*c = m;
+m-1+d = l;
+e = exp(e_a);
+y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+gy_obs = dA*y/y(-1);
+gp_obs = (P/P(-1))*m(-1)/dA;
+end;
+
+initval;
+k = 6;
+m = mst;
+P = 2.25;
+c = 0.45;
+e = 1;
+W = 4;
+R = 1.02;
+d = 0.85;
+n = 0.19;
+l = 0.86;
+y = 0.6;
+gy_obs = exp(gam);
+gp_obs = exp(-gam);
+dA = exp(gam);
+end;
+
+shocks;
+var e_a; stderr 0.014;
+var e_m; stderr 0.005;
+end;
+
+steady;
+
+check;
+
+estimated_params;
+alp, beta_pdf, 0.356, 0.02;
+bet, beta_pdf, 0.993, 0.002;
+gam, normal_pdf, 0.0085, 0.003;
+mst, normal_pdf, 1.0002, 0.007;
+rho, beta_pdf, 0.129, 0.223;
+psi, beta_pdf, 0.65, 0.05;
+del, beta_pdf, 0.01, 0.005;
+stderr e_a, inv_gamma_pdf, 0.035449, inf;
+stderr e_m, inv_gamma_pdf, 0.008862, inf;
+end;
+
+varobs gp_obs gy_obs;
+
+options_.solve_tolf = 1e-12;
+
+data(file=fsdat_simul_dseries,first_obs=1950Q3, nobs=192);
+
+estimation(order=1,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8);

tests/fs2000/fs2000_dseries_b.mod

@@ -0,0 +1,80 @@
+set_time(1950Q3);
+
+var m P c e W R k d n l gy_obs gp_obs y dA;
+varexo e_a e_m;
+
+parameters alp bet gam mst rho psi del;
+
+alp = 0.33;
+bet = 0.99;
+gam = 0.003;
+mst = 1.011;
+rho = 0.7;
+psi = 0.787;
+del = 0.02;
+
+model;
+dA = exp(gam+e_a);
+log(m) = (1-rho)*log(mst) + rho*log(m(-1))+e_m;
+-P/(c(+1)*P(+1)*m)+bet*P(+1)*(alp*exp(-alp*(gam+log(e(+1))))*k^(alp-1)*n(+1)^(1-alp)+(1-del)*exp(-(gam+log(e(+1)))))/(c(+2)*P(+2)*m(+1))=0;
+W = l/n;
+-(psi/(1-psi))*(c*P/(1-n))+l/n = 0;
+R = P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(-alp)/W;
+1/(c*P)-bet*P*(1-alp)*exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)/(m*l*c(+1)*P(+1)) = 0;
+c+k = exp(-alp*(gam+e_a))*k(-1)^alp*n^(1-alp)+(1-del)*exp(-(gam+e_a))*k(-1);
+P*c = m;
+m-1+d = l;
+e = exp(e_a);
+y = k(-1)^alp*n^(1-alp)*exp(-alp*(gam+e_a));
+gy_obs = dA*y/y(-1);
+gp_obs = (P/P(-1))*m(-1)/dA;
+end;
+
+initval;
+k = 6;
+m = mst;
+P = 2.25;
+c = 0.45;
+e = 1;
+W = 4;
+R = 1.02;
+d = 0.85;
+n = 0.19;
+l = 0.86;
+y = 0.6;
+gy_obs = exp(gam);
+gp_obs = exp(-gam);
+dA = exp(gam);
+end;
+
+shocks;
+var e_a; stderr 0.014;
+var e_m; stderr 0.005;
+end;
+
+steady;
+
+check;
+
+estimated_params;
+alp, beta_pdf, 0.356, 0.02;
+bet, beta_pdf, 0.993, 0.002;
+gam, normal_pdf, 0.0085, 0.003;
+mst, normal_pdf, 1.0002, 0.007;
+rho, beta_pdf, 0.129, 0.223;
+psi, beta_pdf, 0.65, 0.05;
+del, beta_pdf, 0.01, 0.005;
+stderr e_a, inv_gamma_pdf, 0.035449, inf;
+stderr e_m, inv_gamma_pdf, 0.008862, inf;
+end;
+
+varobs gp_obs gy_obs;
+
+options_.solve_tolf = 1e-12;
+
+fsdataset = dseries('fsdat_simul_dseries.m');
+fsdataset = fsdataset(1950Q3:1950Q3+191);
+
+data(series=fsdataset);
+
+estimation(order=1,loglinear,mh_replic=2000,mh_nblocks=2,mh_jscale=0.8);

tests/gsa/ls2003.mod

@@ -108,7 +108,7 @@ disp(' ');
 disp('MC FILTERING(rmse=1), TO MAP THE FIT FROM PRIORS');
 disp('Press ENTER to continue'); pause(5);
 
-dynare_sensitivity(nodisplay, datafile=data_ca1,first_obs=8,nobs=79,prefilter=1, // also presample=2,loglinear, are admissible
+dynare_sensitivity(nodisplay, datafile='data_ca1.m',first_obs=8,nobs=79,prefilter=1, // also presample=2,loglinear, are admissible
 load_stab=1,     // load prior sample
 istart_rmse=2,   //start computing rmse from second observation (i.e. rmse does not inlude initial big error)
 stab=0,          // don't  plot again stability analysis results
@@ -121,7 +121,7 @@ disp('BY USING THE COMBINED CALL');
 disp(' ');
 disp('dynare_sensitivity(redform=1,')
 disp('logtrans_redform=1, namendo=(pie,R), namexo=(e_R), namlagendo=(R),')   
-disp('datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,')
+disp('datafile=data_ca1.m,first_obs=8,nobs=79,prefilter=1,')
 disp('istart_rmse=2, rmse=1);')
 disp(' ');
 disp('Press ENTER to continue'); pause(5);
@@ -131,7 +131,7 @@ disp('Press ENTER to continue'); pause(5);
 //namendo=(pie,R),  // evaluate relationships for pie and R (namendo=(:) for all variables)
 //namexo=(e_R),     // evaluate relationships with exogenous e_R (use namexo=(:) for all shocks)
 //namlagendo=(R),   // evaluate relationships with lagged R (use namlagendo=(:) for all lagged endogenous)
-//datafile=data_ca1,first_obs=8,nobs=79,prefilter=1, 
+//datafile='data_ca1.m',first_obs=8,nobs=79,prefilter=1, 
 //istart_rmse=2,   //start computing rmse from second observation (i.e. rmse does not inlude initial big error)
 //rmse=1,          // do rmse analysis
 //);
@@ -144,13 +144,13 @@ disp(' ');
 disp('Press ENTER to continue'); pause(5);
 
 // run this to generate posterior mode and Metropolis files if not yet done
-estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=2,
+estimation(datafile='data_ca1.m',first_obs=8,nobs=79,mh_nblocks=2,
    prefilter=1,mh_jscale=0.5,mh_replic=5000, mode_compute=4, mh_drop=0.6, nodisplay,
    bayesian_irf, filtered_vars, smoother) y_obs R_obs pie_obs dq de;
 
 
 // run this to produce posterior samples of filtered, smoothed and irf variables, if not yet done
-//estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=2,prefilter=1,mh_jscale=0.3,
+//estimation(datafile='data_ca1.m',first_obs=8,nobs=79,mh_nblocks=2,prefilter=1,mh_jscale=0.3,
 //          mh_replic=0, mode_file=ls2003_mode, mode_compute=0, load_mh_file, bayesian_irf,
 //		  filtered_vars, smoother, mh_drop=0.6);
 
@@ -163,7 +163,7 @@ disp('Press ENTER to continue'); pause(5);
 
 dynare_sensitivity(nodisplay, pprior=0,Nsam=2048,neighborhood_width=0.2,
 mode_file=ls2003_mode,  // specifies the mode file where the mode and Hessian are stored
-datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,
+datafile='data_ca1.m',first_obs=8,nobs=79,prefilter=1,
 rmse=1);
 
 disp(' ');
@@ -182,7 +182,7 @@ disp('RMSE ANALYSIS FOR MULTIVARIATE SAMPLE AT THE POSTERIOR MODE');
 disp(' ');
 disp('Press ENTER to continue'); pause(5);
 dynare_sensitivity(nodisplay, mode_file=ls2003_mode,
-datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,
+datafile='data_ca1.m',first_obs=8,nobs=79,prefilter=1,
 pprior=0,
 stab=0,
 rmse=1,
@@ -195,12 +195,12 @@ disp('THE LAST TWO CALLS COULD BE DONE TOGETHER');
 disp('BY USING THE COMBINED CALL');
 disp(' ');
 disp('dynare_sensitivity(pprior=0,Nsam=2048,alpha2_stab=0.4,mode_file=ls2003_mode,')
-disp('datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,')
+disp('datafile=data_ca1.m,first_obs=8,nobs=79,prefilter=1,')
 disp('rmse=1, alpha2_rmse=0, alpha_rmse=0);')
 disp(' ');
 disp('Press ENTER to continue'); pause(5);
 //dynare_sensitivity(pprior=0,Nsam=2048,alpha2_stab=0.4,mode_file=ls2003_mode,
-//datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,
+//datafile='data_ca1.m',first_obs=8,nobs=79,prefilter=1,
 //rmse=1,
 //alpha2_rmse=0, // no correlation analysis
 //alpha_rmse=0  // no Smirnov sensitivity analysis
@@ -210,10 +210,10 @@ disp(' ');
 disp('RMSE ANALYSIS FOR POSTERIOR MCMC sample (ppost=1)');
 disp('Needs a call to dynare_estimation to load all MH environment');
 disp('Press ENTER to continue'); pause(5);
-//estimation(datafile=data_ca1,first_obs=8,nobs=79,mh_nblocks=2, mode_file=ls2003_mode, load_mh_file,
+//estimation(datafile='data_ca1.m',first_obs=8,nobs=79,mh_nblocks=2, mode_file=ls2003_mode, load_mh_file,
 //  prefilter=1,mh_jscale=0.5,mh_replic=0, mode_compute=0, mh_drop=0.6);
 
 dynare_sensitivity(nodisplay, stab=0, // no need for stability analysis since the posterior sample is surely OK
-datafile=data_ca1,first_obs=8,nobs=79,prefilter=1,
+datafile='data_ca1.m',first_obs=8,nobs=79,prefilter=1,
 rmse=1,ppost=1);