function disp_identification(pdraws, idemodel, idemoments, name, advanced)

% Copyright (C) 2008-2010 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/>.

global options_

if nargin<5 | isempty(advanced),
    advanced=0;
end

[SampleSize, npar] = size(pdraws);
jok = 0;
jokP = 0;
jokJ = 0;
jokPJ = 0;

for j=1:npar,
%     if any(idemodel.ind(j,:)==0),
%         pno = 100*length(find(idemodel.ind(j,:)==0))/SampleSize;
%         disp(['Parameter ',name{j},' is not identified in the model for ',num2str(pno),'% of MC runs!' ])
%         disp(' ')
%     end
%     if any(idemoments.ind(j,:)==0),
%         pno = 100*length(find(idemoments.ind(j,:)==0))/SampleSize;
%         disp(['Parameter ',name{j},' is not identified by J moments for ',num2str(pno),'% of MC runs!' ])
%         disp(' ')
%     end
    if any(idemodel.ind(j,:)==1),
        iok = find(idemodel.ind(j,:)==1);
        jok = jok+1;
        kok(jok) = j;
        mmin(jok,1) = min(idemodel.Mco(j,iok));
        mmean(jok,1) = mean(idemodel.Mco(j,iok));
        mmax(jok,1) = max(idemodel.Mco(j,iok));
        [ipmax, jpmax] = find(abs(squeeze(idemodel.Pco(j,[1:j-1,j+1:end],iok)))>0.95);
        if ~isempty(ipmax)
            jokP = jokP+1;
            kokP(jokP) = j;
            ipmax(find(ipmax>=j))=ipmax(find(ipmax>=j))+1;
            [N,X]=hist(ipmax,[1:npar]);
            jpM(jokP)={find(N)};
            NPM(jokP)={N(find(N))./SampleSize.*100};
            pmeanM(jokP)={mean(squeeze(idemodel.Pco(j,find(N),iok))')};
            pminM(jokP)={min(squeeze(idemodel.Pco(j,find(N),iok))')};
            pmaxM(jokP)={max(squeeze(idemodel.Pco(j,find(N),iok))')};
        end
    end
    if any(idemoments.ind(j,:)==1),
        iok = find(idemoments.ind(j,:)==1);
        jokJ = jokJ+1;
        kokJ(jokJ) = j;
        mminJ(jokJ,1) = min(idemoments.Mco(j,iok));
        mmeanJ(jokJ,1) = mean(idemoments.Mco(j,iok));
        mmaxJ(jokJ,1) = max(idemoments.Mco(j,iok));
        [ipmax, jpmax] = find(abs(squeeze(idemoments.Pco(j,[1:j-1,j+1:end],iok)))>0.95);
        if ~isempty(ipmax)
            jokPJ = jokPJ+1;
            kokPJ(jokPJ) = j;
            ipmax(find(ipmax>=j))=ipmax(find(ipmax>=j))+1;
            [N,X]=hist(ipmax,[1:npar]);
            jpJ(jokPJ)={find(N)};
            NPJ(jokPJ)={N(find(N))./SampleSize.*100};
            pmeanJ(jokPJ)={mean(squeeze(idemoments.Pco(j,find(N),iok))')};
            pminJ(jokPJ)={min(squeeze(idemoments.Pco(j,find(N),iok))')};
            pmaxJ(jokPJ)={max(squeeze(idemoments.Pco(j,find(N),iok))')};
        end
    end
end


if any(idemodel.ino),
    disp('WARNING !!!')
    if SampleSize>1,
    disp(['The rank of H (model) is deficient for ', num2str(length(find(idemodel.ino))/SampleSize*100),'% of MC runs!'  ]),
    else
    disp(['The rank of H (model) is deficient!'  ]),
    end
end
for j=1:npar,
    if any(idemodel.ind(j,:)==0),
        pno = 100*length(find(idemodel.ind(j,:)==0))/SampleSize;
        if SampleSize>1
        disp([name{j},' is not identified in the model for ',num2str(pno),'% of MC runs!' ])
        else
        disp([name{j},' is not identified in the model!' ])
        end
    end
    iweak = length(find(idemodel.Mco(j,:)'>(1-1.e-10)));
    if iweak, 
%         disp('WARNING !!!')
%         disp(['Model derivatives of parameter ',name{j},' are multi-collinear (with tol = 1.e-10) for ',num2str(iweak/SampleSize*100),'% of MC runs!' ])
        if SampleSize>1
        disp([name{j},' is collinear w.r.t. all other params ',num2str(iweak/SampleSize*100),'% of MC runs!' ])
        else
        disp([name{j},' is collinear w.r.t. all other params!' ])
        end
        if npar>(j+1),
            [ipair, jpair] = find(squeeze(idemodel.Pco(j,j+1:end,:))'>(1-1.e-10));
        else
            [ipair, jpair] = find(squeeze(idemodel.Pco(j,j+1:end,:))>(1-1.e-10));
        end
        if ~isempty(jpair),
            for jx=j+1:npar,
                ixp = find(jx==(jpair+j));
                if ~isempty(ixp)
                    if SampleSize > 1,
                    disp(['    [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str(length(ixp)/SampleSize*100),'% of MC runs!' ])            
                    else
                    disp(['    [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10)!' ])            
                    end
                end
            end
        end
    end
end
if ~any(idemodel.ino) && ~any(any(idemodel.ind==0))
    disp(['All parameters are identified in the model (rank of H).' ]),
    disp(' ')
end

if any(idemoments.ino),
    disp('WARNING !!!')
    if SampleSize > 1,
        disp(['The rank of J (moments) is deficient for ', num2str(length(find(idemoments.ino))/SampleSize*100),'% of MC runs!'  ]),
    else
        disp(['The rank of J (moments) is deficient!'  ]),
    end
end
if any(idemoments.ino),
%     disp('WARNING !!!')
%     disp(['The rank of J (moments) is deficient for ', num2str(length(find(idemoments.ino))/SampleSize*100),'% of MC runs!'  ]),
    indno=[];
    for j=1:SampleSize, indno=[indno;idemoments.indno{j}]; end
    freqno = mean(indno)*100;
    ifreq=find(freqno);
%     disp('MOMENT RANK FAILURE DUE TO COLLINEARITY OF PARAMETERS:');
    for j=1:npar,
        if any(idemoments.ind(j,:)==0),
            pno = 100*length(find(idemoments.ind(j,:)==0))/SampleSize;
            if SampleSize > 1
            disp([name{j},' is not identified by J moments for ',num2str(pno),'% of MC runs!' ])
            else
            disp([name{j},' is not identified by J moments!' ])
            end
        end
        iweak = length(find(idemoments.Mco(j,:)'>(1-1.e-10)));
        if iweak,
%             disp('WARNING !!!')
%             disp(['Moment derivatives of parameter ',name{j},' are multi-collinear (with tol = 1.e-10) for ',num2str(iweak/SampleSize*100),'% of MC runs!' ])
            if SampleSize > 1,
            disp([name{j},' is collinear w.r.t. all other params ',num2str(iweak/SampleSize*100),'% of MC runs!' ])
            else
            disp([name{j},' is collinear w.r.t. all other params!' ])
            end
            if npar>(j+1),
                [ipair, jpair] = find(squeeze(idemoments.Pco(j,j+1:end,:))'>(1-1.e-10));
            else
                [ipair, jpair] = find(squeeze(idemoments.Pco(j,j+1:end,:))>(1-1.e-10));
            end
            if ~isempty(jpair),
                for jx=j+1:npar,
                    ixp = find(jx==(jpair+j));
                    if ~isempty(ixp)
                        if SampleSize > 1
                        disp(['    [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) for ',num2str(length(ixp)/SampleSize*100),'% of MC runs!' ])
                        else
                        disp(['    [',name{j},',',name{jx},'] are PAIRWISE collinear (with tol = 1.e-10) !' ])
                        end
                    end
                end
            end
        end
    end
end
if ~any(idemoments.ino) && ~any(any(idemoments.ind==0))
    disp(['All parameters are identified by J moments (rank of J)' ]),
    disp(' ')
end

if ~ options_.noprint & advanced,
disp('Press KEY to continue with identification analysis')
pause;
dyntable('Multi collinearity in the model:',char('param','min','mean','max'), ...
         char(name(kok)),[mmin, mmean, mmax],10,10,6);
disp(' ')
dyntable('Multi collinearity for moments in J:',char('param','min','mean','max'), ...
    char(name(kokJ)),[mminJ, mmeanJ, mmaxJ],10,10,6);
disp(' ')
end

     
% if advanced & (~options_.noprint),
%     for j=1:length(kokP),
%         dyntable([name{kokP(j)},' pairwise correlations in the model'],char(' ','min','mean','max'), ...
%                  char(name{jpM{j}}),[pminM{j}' pmeanM{j}' pmaxM{j}'],10,10,3);  
%     end
% 
%     for j=1:length(kokPJ),
%         dyntable([name{kokPJ(j)},' pairwise correlations in J moments'],char(' ','min','mean','max'), ...
%                  char(name{jpJ{j}}),[pminJ{j}' pmeanJ{j}' pmaxJ{j}'],10,10,3);  
%     end
% end
% disp(' ')