Modifications linked to previous commit:
Added first order moments Added LRE analysis for trivial no-identification git-svn-id: https://www.dynare.org/svn/dynare/trunk@3361 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
ratto
parent
cda0f571b4
commit
94d215d73e
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@ -1,4 +1,4 @@
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function [H, dA, dOm, Hss, info] = getH(A, B, M_,oo_,kronflag,indx,indexo)
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function [H, dA, dOm, Hss, gp, info] = getH(A, B, M_,oo_,kronflag,indx,indexo)
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% computes derivative of reduced form linear model w.r.t. deep params
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%
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214
matlab/getJJ.m
214
matlab/getJJ.m
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@ -1,4 +1,4 @@
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function [JJ, H, gam] = getJJ(A, B, M_,oo_,options_,kronflag,indx,indexo,mf,nlags,useautocorr)
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function [JJ, H, gam, gp] = getJJ(A, B, M_,oo_,options_,kronflag,indx,indexo,mf,nlags,useautocorr)
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% Copyright (C) 2009 Dynare Team
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%
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@ -22,118 +22,118 @@ if nargin<8 | isempty(indexo), indexo = [];, end,
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if nargin<10 | isempty(nlags), nlags=3; end,
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if nargin<11 | isempty(useautocorr), useautocorr=0; end,
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if useautocorr,
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if useautocorr,
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warning('off','MATLAB:divideByZero')
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end
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end
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if kronflag == -1,
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fun = 'thet2tau';
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params0 = M_.params;
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JJ = fdjac(fun,[sqrt(diag(M_.Sigma_e(indexo,indexo))); M_.params(indx)],indx,indexo,1,mf,nlags,useautocorr);
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M_.params = params0;
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params0 = M_.params;
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H = fdjac(fun,[sqrt(diag(M_.Sigma_e(indexo,indexo))); M_.params(indx)],indx,indexo,0,mf,nlags,useautocorr);
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M_.params = params0;
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assignin('base','M_', M_);
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assignin('base','oo_', oo_);
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fun = 'thet2tau';
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params0 = M_.params;
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JJ = fdjac(fun,[sqrt(diag(M_.Sigma_e(indexo,indexo))); M_.params(indx)],indx,indexo,1,mf,nlags,useautocorr);
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M_.params = params0;
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params0 = M_.params;
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H = fdjac(fun,[sqrt(diag(M_.Sigma_e(indexo,indexo))); M_.params(indx)],indx,indexo,0,mf,nlags,useautocorr);
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M_.params = params0;
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assignin('base','M_', M_);
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assignin('base','oo_', oo_);
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else
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[H, dA, dOm, dYss] = getH(A, B, M_,oo_,kronflag,indx,indexo);
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% if isempty(H),
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% JJ = [];
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% GAM = [];
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% return
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% end
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m = length(A);
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GAM = lyapunov_symm(A,B*M_.Sigma_e*B',options_.qz_criterium,options_.lyapunov_complex_threshold,1);
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k = find(abs(GAM) < 1e-12);
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GAM(k) = 0;
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% if useautocorr,
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sdy = sqrt(diag(GAM));
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sy = sdy*sdy';
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% end
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% BB = dOm*0;
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% for j=1:length(indx),
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% BB(:,:,j)= dA(:,:,j)*GAM*A'+A*GAM*dA(:,:,j)'+dOm(:,:,j);
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% end
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% XX = lyapunov_symm_mr(A,BB,options_.qz_criterium,options_.lyapunov_complex_threshold,0);
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for j=1:length(indexo),
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dum = lyapunov_symm(A,dOm(:,:,j),options_.qz_criterium,options_.lyapunov_complex_threshold,2);
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% dum = XX(:,:,j);
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k = find(abs(dum) < 1e-12);
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dum(k) = 0;
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if useautocorr
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dsy = 1/2./sdy.*diag(dum);
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dsy = dsy*sdy'+sdy*dsy';
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dum1=dum;
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dum1 = (dum1.*sy-dsy.*GAM)./(sy.*sy);
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dum1 = dum1-diag(diag(dum1))+diag(diag(dum));
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dumm = vech(dum1(mf,mf));
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else
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dumm = vech(dum(mf,mf));
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end
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for i=1:nlags,
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dum1 = A^i*dum;
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if useautocorr
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dum1 = (dum1.*sy-dsy.*(A^i*GAM))./(sy.*sy);
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end
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dumm = [dumm; vec(dum1(mf,mf))];
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end
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JJ(:,j) = dumm;
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[H, dA, dOm, dYss, gp] = getH(A, B, M_,oo_,kronflag,indx,indexo);
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gp = reshape(gp,size(gp,1)*size(gp,2),size(gp,3));
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% if isempty(H),
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% JJ = [];
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% GAM = [];
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% return
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% end
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m = length(A);
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GAM = lyapunov_symm(A,B*M_.Sigma_e*B',options_.qz_criterium,options_.lyapunov_complex_threshold,1);
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k = find(abs(GAM) < 1e-12);
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GAM(k) = 0;
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% if useautocorr,
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sdy = sqrt(diag(GAM));
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sy = sdy*sdy';
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% end
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% BB = dOm*0;
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% for j=1:length(indx),
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% BB(:,:,j)= dA(:,:,j)*GAM*A'+A*GAM*dA(:,:,j)'+dOm(:,:,j);
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% end
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% XX = lyapunov_symm_mr(A,BB,options_.qz_criterium,options_.lyapunov_complex_threshold,0);
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for j=1:length(indexo),
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dum = lyapunov_symm(A,dOm(:,:,j),options_.qz_criterium,options_.lyapunov_complex_threshold,2);
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% dum = XX(:,:,j);
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k = find(abs(dum) < 1e-12);
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dum(k) = 0;
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if useautocorr
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dsy = 1/2./sdy.*diag(dum);
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dsy = dsy*sdy'+sdy*dsy';
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dum1=dum;
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dum1 = (dum1.*sy-dsy.*GAM)./(sy.*sy);
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dum1 = dum1-diag(diag(dum1))+diag(diag(dum));
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dumm = vech(dum1(mf,mf));
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else
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dumm = vech(dum(mf,mf));
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end
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nexo = length(indexo);
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for j=1:length(indx),
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dum = lyapunov_symm(A,dA(:,:,j+nexo)*GAM*A'+A*GAM*dA(:,:,j+nexo)'+dOm(:,:,j+nexo),options_.qz_criterium,options_.lyapunov_complex_threshold,2);
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% dum = XX(:,:,j);
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k = find(abs(dum) < 1e-12);
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dum(k) = 0;
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if useautocorr
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dsy = 1/2./sdy.*diag(dum);
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dsy = dsy*sdy'+sdy*dsy';
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dum1=dum;
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dum1 = (dum1.*sy-dsy.*GAM)./(sy.*sy);
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dum1 = dum1-diag(diag(dum1))+diag(diag(dum));
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dumm = vech(dum1(mf,mf));
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else
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dumm = vech(dum(mf,mf));
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end
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for i=1:nlags,
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dum1 = A^i*dum;
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for ii=1:i,
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dum1 = dum1 + A^(ii-1)*dA(:,:,j+nexo)*A^(i-ii)*GAM;
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end
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if useautocorr
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dum1 = (dum1.*sy-dsy.*(A^i*GAM))./(sy.*sy);
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end
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dumm = [dumm; vec(dum1(mf,mf))];
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end
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JJ(:,j+nexo) = dumm;
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for i=1:nlags,
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dum1 = A^i*dum;
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if useautocorr
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dum1 = (dum1.*sy-dsy.*(A^i*GAM))./(sy.*sy);
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end
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dumm = [dumm; vec(dum1(mf,mf))];
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end
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JJ = [ [zeros(length(mf),nexo) dYss(mf,:)]; JJ];
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if nargout >2,
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% sy=sy(mf,mf);
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options_.ar=nlags;
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[GAM,stationary_vars] = th_autocovariances(oo_.dr,oo_.dr.order_var(mf),M_,options_);
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sy=sqrt(diag(GAM{1}));
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sy=sy*sy';
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if useautocorr,
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sy=sy-diag(diag(sy))+eye(length(mf));
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GAM{1}=GAM{1}./sy;
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else
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for j=1:nlags,
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GAM{j+1}=GAM{j+1}.*sy;
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end
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end
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gam = vech(GAM{1});
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for j=1:nlags,
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gam = [gam; vec(GAM{j+1})];
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end
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JJ(:,j) = dumm;
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end
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nexo = length(indexo);
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for j=1:length(indx),
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dum = lyapunov_symm(A,dA(:,:,j+nexo)*GAM*A'+A*GAM*dA(:,:,j+nexo)'+dOm(:,:,j+nexo),options_.qz_criterium,options_.lyapunov_complex_threshold,2);
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% dum = XX(:,:,j);
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k = find(abs(dum) < 1e-12);
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dum(k) = 0;
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if useautocorr
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dsy = 1/2./sdy.*diag(dum);
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dsy = dsy*sdy'+sdy*dsy';
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dum1=dum;
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dum1 = (dum1.*sy-dsy.*GAM)./(sy.*sy);
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dum1 = dum1-diag(diag(dum1))+diag(diag(dum));
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dumm = vech(dum1(mf,mf));
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else
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dumm = vech(dum(mf,mf));
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end
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gam = [oo_.dr.ys(oo_.dr.order_var(mf)); gam];
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for i=1:nlags,
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dum1 = A^i*dum;
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for ii=1:i,
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dum1 = dum1 + A^(ii-1)*dA(:,:,j+nexo)*A^(i-ii)*GAM;
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end
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if useautocorr
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dum1 = (dum1.*sy-dsy.*(A^i*GAM))./(sy.*sy);
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end
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dumm = [dumm; vec(dum1(mf,mf))];
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end
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JJ(:,j+nexo) = dumm;
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end
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JJ = [ [zeros(length(mf),nexo) dYss(mf,:)]; JJ];
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if nargout >2,
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% sy=sy(mf,mf);
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options_.ar=nlags;
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[GAM,stationary_vars] = th_autocovariances(oo_.dr,oo_.dr.order_var(mf),M_,options_);
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sy=sqrt(diag(GAM{1}));
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sy=sy*sy';
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if useautocorr,
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sy=sy-diag(diag(sy))+eye(length(mf));
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GAM{1}=GAM{1}./sy;
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else
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for j=1:nlags,
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GAM{j+1}=GAM{j+1}.*sy;
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end
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end
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gam = vech(GAM{1});
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for j=1:nlags,
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gam = [gam; vec(GAM{j+1})];
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end
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end
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gam = [oo_.dr.ys(oo_.dr.order_var(mf)); gam];
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end
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if useautocorr,
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if useautocorr,
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warning('on','MATLAB:divideByZero')
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end
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end
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@ -1,4 +1,4 @@
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function [McoH, McoJ, PcoH, PcoJ, condH, condJ, eH, eJ, ind01, ind02, indnoH, indnoJ] = identification_checks(H,JJ, bayestopt_)
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function [McoH, McoJ, McoGP, PcoH, PcoJ, PcoGP, condH, condJ, condGP, eH, eJ, eGP, ind01, ind02, indnoH, indnoJ] = identification_checks(H,JJ, gp, bayestopt_)
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% Copyright (C) 2008 Dynare Team
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%
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@ -22,8 +22,10 @@ function [McoH, McoJ, PcoH, PcoJ, condH, condJ, eH, eJ, ind01, ind02, indnoH, in
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% 1. check rank of H at theta
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npar = size(H,2);
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npar0 = size(gp,2);
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indnoH = {};
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indnoJ = {};
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indnoLRE = {};
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ind1 = find(vnorm(H)~=0);
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H1 = H(:,ind1);
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covH = H1'*H1;
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@ -50,6 +52,19 @@ eJ(find(vnorm(JJ)==0),1:length(find(vnorm(JJ)==0)))=eye(length(find(vnorm(JJ)==0
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% condJ = cond(JJ1'*JJ1);
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condJ = cond(JJ1);
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ind3 = find(vnorm(gp)~=0);
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gp1 = gp(:,ind3);
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covgp = gp1'*gp1;
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sdgp = sqrt(diag(covgp));
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sdgp = sdgp*sdgp';
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[ex1,ex2] = eig( (gp1'*gp1)./sdgp );
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% eJ = NaN(npar,length(ind2));
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eGP = zeros(npar0,npar0);
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eGP(ind3,length(find(vnorm(gp)==0))+1:end) = ex1;
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eGP(find(vnorm(gp)==0),1:length(find(vnorm(gp)==0)))=eye(length(find(vnorm(gp)==0)));
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% condJ = cond(JJ1'*JJ1);
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condGP = cond(gp1);
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if rank(H)<npar
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ixno = 0;
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% - find out which parameters are involved,
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@ -106,12 +121,16 @@ end
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McoH = NaN(npar,1);
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McoJ = NaN(npar,1);
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McoGP = NaN(npar0,1);
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for ii = 1:size(H1,2);
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McoH(ind1(ii),:) = [cosn([H1(:,ii),H1(:,find([1:1:size(H1,2)]~=ii))])];
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end
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for ii = 1:size(JJ1,2);
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McoJ(ind2(ii),:) = [cosn([JJ1(:,ii),JJ1(:,find([1:1:size(JJ1,2)]~=ii))])];
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end
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for ii = 1:size(gp1,2);
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McoGP(ind3(ii),:) = [cosn([gp1(:,ii),gp1(:,find([1:1:size(gp1,2)]~=ii))])];
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end
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% format long % some are nearly 1
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% McoJ
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@ -123,6 +142,7 @@ end
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PcoH = NaN(npar,npar);
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PcoJ = NaN(npar,npar);
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PcoGP = NaN(npar0,npar0);
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for ii = 1:size(H1,2);
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for jj = 1:size(H1,2);
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PcoH(ind1(ii),ind1(jj)) = [cosn([H1(:,ii),H1(:,jj)])];
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@ -135,6 +155,13 @@ for ii = 1:size(JJ1,2);
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end
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end
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for ii = 1:size(gp1,2);
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for jj = 1:size(gp1,2);
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PcoGP(ind3(ii),ind3(jj)) = [cosn([gp1(:,ii),gp1(:,jj)])];
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end
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end
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ind01 = zeros(npar,1);
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ind02 = zeros(npar,1);
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ind01(ind1) = 1;
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