removing command CALIB: it has not been working for a long time. Calibration is in fact a special case of method of moments and should be replaced by such a method.
Michel Juillard
parent
256ff761e8
commit
05b9c70ae8
193
matlab/calib.m
193
matlab/calib.m
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@ -1,193 +0,0 @@
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function M_.Sigma_e = calib(var_indices,targets,var_weights,nar,cova,M_.Sigma_e)
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% Copyright (C) 2005 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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global oo_ M_ vx
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ncstr = 0;
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ni = size(var_indices,1);
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for i=1:nar+3
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ncstr = ncstr + size(var_indices{i},1);
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end
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if cova
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if ncstr < M_.exo_nbr*(M_.exo_nbr+1)/2
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error(['number of preset variances is smaller than number of shock' ...
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' variances and covariances to be estimated !'])
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end
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else
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if ncstr < M_.exo_nbr
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error(['number of preset variances is smaller than number of shock' ...
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' variances to be estimated !'])
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end
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end
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% computes approximate solution at order 1
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dr = resol(oo_.steady_state,0,0,1);
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ghx = dr.ghx;
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ghu = dr.ghu;
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npred = dr.npred;
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nstatic = dr.nstatic;
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kstate = dr.kstate;
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order = dr.order_var;
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iv(order) = [1:M_.endo_nbr];
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iv = iv';
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nx = size(ghx,2);
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ikx = [nstatic+1:nstatic+npred];
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A = zeros(nx,nx);
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A(1:npred,:)=ghx(ikx,:);
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offset_r = npred;
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offset_c = 0;
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i0 = find(kstate(:,2) == M_.maximum_lag+1);
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n0 = size(i0,1);
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for i=M_.maximum_lag:-1:2
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i1 = find(kstate(:,2) == i);
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n1 = size(i1,1);
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j = zeros(n1,1);
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for j1 = 1:n1
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j(j1) = find(kstate(i0,1)==kstate(i1(j1),1));
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end
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A(offset_r+1:offset_r+n1,offset_c+j)=eye(n1);
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offset_r = offset_r + n1;
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offset_c = offset_c + n0;
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i0 = i1;
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n0 = n1;
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end
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ghu1 = [ghu(ikx,:);zeros(nx-npred,M_.exo_nbr)];
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% IA = speye(nx*nx)-kron(A,A);
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% kron_ghu = kron(ghu1,ghu1);
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% vx1 such that vec(sigma_x) = vx1 * vec(M_.Sigma_e) (predetermined vars)
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vx1 = [];
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% vx1 = IA\kron_ghu;
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IA = [];
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kron_ghu = [];
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% computes required variables and indices among required variables
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iiy = [];
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for i=1:nar+3
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if i ~= 3 & ~isempty(var_indices{i})
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iiy = union(iiy, iv(var_indices{i}(:,1)));
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end
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end
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if ~isempty(var_indices{2})
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iiy = union(iiy, iv(var_indices{2}(:,2)));
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end
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ny = size(iiy,1);
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for i=1:nar+3
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if i ~= 3 & ~isempty(var_indices{i})
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var_indices{i}(:,1) = indnv(iv(var_indices{i}(:,1)),iiy);
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end
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if i ~= 2 & i ~= 3 & ~isempty(var_indices{i})
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var_indices{i} = sub2ind([ny ny],var_indices{i},var_indices{i});
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end
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end
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if ~isempty(var_indices{2})
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var_indices{2}(:,2) = indnv(iv(var_indices{2}(:,2)),iiy);
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var_indices{2} = sub2ind([ny ny],var_indices{2}(:,1),var_indices{2}(:,2));
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end
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if ~isempty(var_indices{3})
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var_indices{3} = sub2ind([M_.exo_nbr M_.exo_nbr],var_indices{3}(:,1),var_indices{3}(:,2));
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end
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if isempty(M_.Sigma_e)
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M_.Sigma_e = 0.01*eye(M_.exo_nbr);
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b = 0.1*ghu1*ghu1';
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else
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b = ghu1*M_.Sigma_e*ghu1';
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M_.Sigma_e = chol(M_.Sigma_e+1e-14*eye(M_.exo_nbr));
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end
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options=optimset('LargeScale','on','MaxFunEvals',20000*ny,'TolX',1e-4, ...
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'TolFun',1e-4,'Display','Iter','MaxIter',10000);
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% [M_.Sigma_e,f]=fminunc(@calib_obj,M_.Sigma_e,options,A,ghu1,ghx(iiy,:),ghu(iiy,:),targets,var_weights,var_indices,nar);
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[M_.Sigma_e,f]=fmincon(@calib_obj,diag(M_.Sigma_e).^2,-eye(M_.exo_nbr),zeros(M_.exo_nbr,1),[],[],[],[],[],options,A,ghu1,ghx(iiy,:),ghu(iiy,:),targets,var_weights,var_indices,nar);
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M_.Sigma_e = diag(M_.Sigma_e);
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objective = calib_obj2(diag(M_.Sigma_e),A,ghu1,ghx(iiy,:),ghu(iiy,:),targets,var_weights,var_indices,nar);
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disp('CALIBRATION')
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disp('')
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if ~isempty(var_indices{1})
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disp(sprintf('%16s %14s %14s %14s %14s','Std. Dev','Target','Obtained','Diff'));
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str = ' ';
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for i=1:size(var_indices{1},1)
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[i1,i2] = ind2sub([ny ny],var_indices{1}(i));
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str = sprintf('%16s: %14.2f %14.2f %14.2f',M_.endo_names(order(iiy(i1)),:),targets{1}(i),objective{1}(i),objective{1}(i)-targets{1}(i));
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disp(str);
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end
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end
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if ~isempty(var_indices{2})
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disp(sprintf('%32s %14s %14s','Correlations','Target','Obtained','Diff'));
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str = ' ';
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for i=1:size(var_indices{2},1)
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[i1,i2]=ind2sub([ny ny],var_indices{2}(i));
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str = sprintf('%16s,%16s: %14.2f %14.2f %14.2f',M_.endo_names(order(iiy(i1)),:), ...
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M_.endo_names(order(iiy(i2)),:),targets{2}(i),objective{2}(i),objective{2}(i)-targets{2}(i));
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disp(str);
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end
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end
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if ~isempty(var_indices{3})
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disp(sprintf('%32s %16s %16s','Constrained shocks (co)variances','Target','Obtained'));
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str = ' ';
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for i=1:size(var_indices{3},1)
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[i1,i2]=ind2sub([M_.exo_nbr M_.exo_nbr],var_indices{3}(i));
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if i1 == i2
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str = sprintf('%32s: %16.4f %16.4f',M_.exo_name(order(i1),:), ...
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targets{3}(i),objective{3}(i));
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else
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str = sprintf('%16s,%16s: %16.4f %16.4f',M_.exo_name(order(i1),:), ...
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M_.exo_name(order(i2), :),targets{3}(i),objective{3}(i));
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end
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disp(str);
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end
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end
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flag = 1;
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for j=4:nar+3
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if ~isempty(var_indices{j})
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if flag
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disp(sprintf('%16s %16s %16s','Autocorrelations','Target','Obtained'));
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str = ' ';
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flag = 0;
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end
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for i=1:size(var_indices{j},1)
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[i1,i2] = ind2sub([ny ny],var_indices{j}(i));
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str = sprintf('%16s(%d): %16.4f %16.4f',M_.endo_names(order(iiy(i1)),:), ...
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j-3,targets{j}(i),objective{j}(i));
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disp(str);
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end
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end
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end
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disp('');
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disp('Calibrated variances')
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str = ' ';
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for i=1:M_.exo_nbr
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str = [str sprintf('%16s',M_.exo_name(i,:))];
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end
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disp(str);
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disp('');
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str = ' ';
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for i=1:M_.exo_nbr
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str = [str sprintf('%16f',M_.Sigma_e(i,i))];
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end
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disp(str);
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% 10/9/02 MJ
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@ -1,84 +0,0 @@
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function f=calib_obj(M_.Sigma_e,A,ghu1,ghx,ghu,targets,var_weights,iy,nar)
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% targets and iy order: 1) variances 2) correlations
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% 3) constraints on M_.Sigma_e itself 4) autocorrelations
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% Copyright (C) 2005-2008 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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global vx fold options_
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oo_.gamma_y = cell(nar+1,1);
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% M_.Sigma_e = M_.Sigma_e'*M_.Sigma_e;
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M_.Sigma_e=diag(M_.Sigma_e);
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nx = size(ghx,2);
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b=ghu1*M_.Sigma_e*ghu1';
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vx = [];
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if isempty(vx)
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vx = lyapunov_symm(A,b,options_.qz_criterium,options_.lyapunov_complex_threshold);
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else
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[vx,status] = bicgstab_(@f_var,b(:),vx(:),1e-8,50,A,nx);
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if status
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vx = lyapunov_symm(A,b,options_.qz_criterium,options_.lyapunov_complex_threshold);
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else
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vx=reshape(vx,nx,nx);
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end
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end
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oo_.gamma_y{1} = ghx*vx*ghx'+ ghu*M_.Sigma_e*ghu';
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f = 0;
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if ~isempty(targets{1})
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e = targets{1}-sqrt(oo_.gamma_y{1}(iy{1}));
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f = e'*(var_weights{1}.*e);
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end
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sy = sqrt(diag(oo_.gamma_y{1}));
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sy = sy *sy';
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if ~isempty(targets{2})
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e = targets{2}-oo_.gamma_y{1}(iy{2})./(sy(iy{2})+1e-10);
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f = f+e'*(var_weights{2}.*e);
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end
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if ~isempty(targets{3})
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e = targets{3}-sqrt(M_.Sigma_e(iy{3}));
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f = f+e'*(var_weights{3}.*e);
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end
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% autocorrelations
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if nar > 0
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vxy = (A*vx*ghx'+ghu1*M_.Sigma_e*ghu');
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oo_.gamma_y{2} = ghx*vxy./(sy+1e-10);
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if ~isempty(targets{4})
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e = targets{4}-oo_.gamma_y{2}(iy{4});
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f = f+e'*(var_weights{4}.*e);
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end
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for i=2:nar
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vxy = A*vxy;
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oo_.gamma_y{i+1} = ghx*vxy./(sy+1e-10);
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if ~isempty(targets{i+3})
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e = targets{i+3}-oo_.gamma_y{i+1}(iy{i+3});
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f = f+e'*(var_weights{i+3}.*e);
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end
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end
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end
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if isempty(fold) | f < 2*fold
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fold = f;
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vxold = vx;
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end
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% 11/04/02 MJ generalized for correlations, autocorrelations and
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% constraints on M_.Sigma_e
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% 01/25/03 MJ targets std. dev. instead of variances
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@ -1,64 +0,0 @@
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function objective=calib_obj2(M_.Sigma_e,A,ghu1,ghx,ghu,targets,var_weights,iy,nar)
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% targets and iy order: 1) variances 2) correlations
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% 3) constraints on M_.Sigma_e itself 4) autocorrelations
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% Copyright (C) 2005-2008 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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global vx fold options_
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objective = cell (nar+3);
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oo_.gamma_y = cell(nar+1,1);
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M_.Sigma_e=diag(M_.Sigma_e);
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nx = size(ghx,2);
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b=ghu1*M_.Sigma_e*ghu1';
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vx = lyapunov_symm(A,b,options_.qz_criterium,options_.lyapunov_complex_threshold);
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oo_.gamma_y{1} = ghx*vx*ghx'+ ghu*M_.Sigma_e*ghu';
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if ~isempty(targets{1})
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objective{1} = sqrt(oo_.gamma_y{1}(iy{1}));
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end
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sy = sqrt(diag(oo_.gamma_y{1}));
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sy = sy *sy';
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if ~isempty(targets{2})
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objective{2} = oo_.gamma_y{1}(iy{2})./(sy(iy{2})+1e-10);
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end
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if ~isempty(targets{3})
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objective{3} = M_.Sigma_e(iy{3});
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end
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% autocorrelations
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if nar > 0
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vxy = (A*vx*ghx'+ghu1*M_.Sigma_e*ghu');
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oo_.gamma_y{2} = ghx*vxy./(sy+1e-10);
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if ~isempty(targets{4})
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objective{4} = oo_.gamma_y{2}(iy{4});
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end
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for i=2:nar
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vxy = A*vxy;
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oo_.gamma_y{i+1} = ghx*vxy./(sy+1e-10);
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if ~isempty(targets{i+3})
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objecitve{i+3} = oo_.gamma_y{i+1}(iy{i+3});
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end
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end
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end
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% 11/04/02 MJ generalized for correlations, autocorrelations and
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% constraints on M_.Sigma_e
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@ -737,7 +737,7 @@ CalibStatement::CalibStatement(int covar_arg) : covar(covar_arg)
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void
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CalibStatement::writeOutput(ostream &output, const string &basename) const
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{
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output << "M_.Sigma_e=calib(calib_var_index,calib_targets,calib_weights," << covar << ",Sigma_e_);\n";
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output << "M_.Sigma_e=calib(calib_var_index,calib_targets,calib_weights,options_.ar," << covar << ",M_.Sigma_e);\n";
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}
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OsrParamsStatement::OsrParamsStatement(const SymbolList &symbol_list_arg) :
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@ -93,7 +93,7 @@ class ParsingDriver;
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%token BVAR_PRIOR_DECAY BVAR_PRIOR_FLAT BVAR_PRIOR_LAMBDA
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%token BVAR_PRIOR_MU BVAR_PRIOR_OMEGA BVAR_PRIOR_TAU BVAR_PRIOR_TRAIN
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%token BVAR_REPLIC BYTECODE
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%token CALIB CALIB_VAR CHANGE_TYPE CHECK CONDITIONAL_FORECAST CONDITIONAL_FORECAST_PATHS CONF_SIG CONSTANT CONTROLLED_VAREXO CORR COVAR CUTOFF
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%token CHANGE_TYPE CHECK CONDITIONAL_FORECAST CONDITIONAL_FORECAST_PATHS CONF_SIG CONSTANT CONTROLLED_VAREXO CORR COVAR CUTOFF
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%token DATAFILE DR_ALGO DROP DSAMPLE DYNASAVE DYNATYPE
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%token END ENDVAL EQUAL ESTIMATION ESTIMATED_PARAMS ESTIMATED_PARAMS_BOUNDS ESTIMATED_PARAMS_INIT
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%token FILENAME FILTER_STEP_AHEAD FILTERED_VARS FIRST_OBS
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@ -166,7 +166,7 @@ class ParsingDriver;
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%type <string_val> non_negative_number signed_number signed_integer
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%type <string_val> filename symbol expectation_input
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%type <string_val> vec_value_1 vec_value
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%type <string_val> calib_arg2 range prior
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%type <string_val> range prior
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%type <symbol_type_val> change_type_arg
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%type <vector_string_val> change_type_var_list
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%type <vector_int_val> vec_int_elem vec_int_1 vec_int vec_int_number
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@ -211,8 +211,6 @@ statement : parameters
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| optim_weights
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| osr_params
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| osr
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| calib_var
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| calib
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| dynatype
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| dynasave
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| model_comparison
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@ -1230,31 +1228,6 @@ osr : OSR ';'
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{driver.run_osr(); }
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;
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calib_var : CALIB_VAR ';' calib_var_list END ';' { driver.run_calib_var(); };
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calib_var_list : calib_var_list calib_arg1
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| calib_arg1
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;
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calib_arg1 : symbol calib_arg2 EQUAL expression ';'
|
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{ driver.set_calib_var($1, $2, $4); }
|
||||
| symbol COMMA symbol calib_arg2 EQUAL expression ';'
|
||||
{ driver.set_calib_covar($1, $3, $4, $6); }
|
||||
| AUTOCORR symbol '(' INT_NUMBER ')' calib_arg2 EQUAL expression ';'
|
||||
{ driver.set_calib_ac($2, $4, $6, $8); }
|
||||
;
|
||||
|
||||
calib_arg2 : { $$ = new string("1"); }
|
||||
| '(' non_negative_number ')'
|
||||
{ $$ = $2; }
|
||||
;
|
||||
|
||||
calib : CALIB ';'
|
||||
{ driver.run_calib(0); }
|
||||
| CALIB '(' COVAR ')' ';'
|
||||
{ driver.run_calib(1); }
|
||||
;
|
||||
|
||||
dynatype : DYNATYPE '(' filename ')' ';'
|
||||
{ driver.run_dynatype($3); }
|
||||
| DYNATYPE '(' filename ')' symbol_list ';'
|
||||
|
|
|
|||
|
|
@ -128,7 +128,6 @@ string eofbuff;
|
|||
<INITIAL>stoch_simul {BEGIN DYNARE_STATEMENT; return token::STOCH_SIMUL;}
|
||||
<INITIAL>dsample {BEGIN DYNARE_STATEMENT; return token::DSAMPLE;}
|
||||
<INITIAL>Sigma_e {BEGIN DYNARE_STATEMENT; sigma_e = 1; return token::SIGMA_E;}
|
||||
<INITIAL>calib {BEGIN DYNARE_STATEMENT; return token::CALIB;}
|
||||
<INITIAL>planner_objective {BEGIN DYNARE_STATEMENT; return token::PLANNER_OBJECTIVE;}
|
||||
<INITIAL>ramsey_policy {BEGIN DYNARE_STATEMENT; return token::RAMSEY_POLICY;}
|
||||
<INITIAL>identification {BEGIN DYNARE_STATEMENT; return token::IDENTIFICATION;}
|
||||
|
|
@ -171,7 +170,6 @@ string eofbuff;
|
|||
<INITIAL>estimated_params_bounds {BEGIN DYNARE_BLOCK; return token::ESTIMATED_PARAMS_BOUNDS;}
|
||||
<INITIAL>observation_trends {BEGIN DYNARE_BLOCK; return token::OBSERVATION_TRENDS;}
|
||||
<INITIAL>optim_weights {BEGIN DYNARE_BLOCK; return token::OPTIM_WEIGHTS;}
|
||||
<INITIAL>calib_var {BEGIN DYNARE_BLOCK; return token::CALIB_VAR;}
|
||||
<INITIAL>homotopy_setup {BEGIN DYNARE_BLOCK; return token::HOMOTOPY_SETUP;}
|
||||
<INITIAL>conditional_forecast_paths {BEGIN DYNARE_BLOCK; return token::CONDITIONAL_FORECAST_PATHS;}
|
||||
<INITIAL>svar_identification {BEGIN DYNARE_BLOCK; return token::SVAR_IDENTIFICATION;}
|
||||
|
|
|
|||
Loading…
Reference in New Issue