diff --git a/matlab/dyn_risky_steadystate_solver.m b/matlab/dyn_risky_steadystate_solver.m
index 6067aa19e..d739a8b19 100644
--- a/matlab/dyn_risky_steadystate_solver.m
+++ b/matlab/dyn_risky_steadystate_solver.m
@@ -1,92 +1,93 @@
function [dr,info] = dyn_risky_steadystate_solver(ys0,M, ...
dr,options,oo)
-%@info:
-%! @deftypefn {Function File} {[@var{dr},@var{info}] =} dyn_risky_steadystate_solver (@var{ys0},@var{M},@var{dr},@var{options},@var{oo})
-%! @anchor{dyn_risky_steadystate_solver}
-%! @sp 1
-%! Computes the second order risky steady state and first and second order reduced form of the DSGE model.
-%! @sp 2
-%! @strong{Inputs}
-%! @sp 1
-%! @table @ @var
-%! @item ys0
-%! Vector containing a guess value for the risky steady state
-%! @item M
-%! Matlab's structure describing the model (initialized by @code{dynare}).
-%! @item dr
-%! Matlab's structure describing the reduced form solution of the model.
-%! @item options
-%! Matlab's structure describing the options (initialized by @code{dynare}).
-%! @item oo
-%! Matlab's structure gathering the results (initialized by @code{dynare}).
-%! @end table
-%! @sp 2
-%! @strong{Outputs}
-%! @sp 1
-%! @table @ @var
-%! @item dr
-%! Matlab's structure describing the reduced form solution of the model.
-%! @item info
-%! Integer scalar, error code.
-%! @sp 1
-%! @table @ @code
-%! @item info==0
-%! No error.
-%! @item info==1
-%! The model doesn't determine the current variables uniquely.
-%! @item info==2
-%! MJDGGES returned an error code.
-%! @item info==3
-%! Blanchard & Kahn conditions are not satisfied: no stable equilibrium.
-%! @item info==4
-%! Blanchard & Kahn conditions are not satisfied: indeterminacy.
-%! @item info==5
-%! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure.
-%! @item info==6
-%! The jacobian evaluated at the deterministic steady state is complex.
-%! @item info==19
-%! The steadystate routine thrown an exception (inconsistent deep parameters).
-%! @item info==20
-%! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations).
-%! @item info==21
-%! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.
-%! @item info==22
-%! The steady has NaNs.
-%! @item info==23
-%! M_.params has been updated in the steadystate routine and has complex valued scalars.
-%! @item info==24
-%! M_.params has been updated in the steadystate routine and has some NaNs.
-%! @end table
-%! @end table
-%! @end deftypefn
-%@eod:
+ %@info:
+ %! @deftypefn {Function File} {[@var{dr},@var{info}] =} dyn_risky_steadystate_solver (@var{ys0},@var{M},@var{dr},@var{options},@var{oo})
+ %! @anchor{dyn_risky_steadystate_solver}
+ %! @sp 1
+ %! Computes the second order risky steady state and first and second order reduced form of the DSGE model.
+ %! @sp 2
+ %! @strong{Inputs}
+ %! @sp 1
+ %! @table @ @var
+ %! @item ys0
+ %! Vector containing a guess value for the risky steady state
+ %! @item M
+ %! Matlab's structure describing the model (initialized by @code{dynare}).
+ %! @item dr
+ %! Matlab's structure describing the reduced form solution of the model.
+ %! @item options
+ %! Matlab's structure describing the options (initialized by @code{dynare}).
+ %! @item oo
+ %! Matlab's structure gathering the results (initialized by @code{dynare}).
+ %! @end table
+ %! @sp 2
+ %! @strong{Outputs}
+ %! @sp 1
+ %! @table @ @var
+ %! @item dr
+ %! Matlab's structure describing the reduced form solution of the model.
+ %! @item info
+ %! Integer scalar, error code.
+ %! @sp 1
+ %! @table @ @code
+ %! @item info==0
+ %! No error.
+ %! @item info==1
+ %! The model doesn't determine the current variables uniquely.
+ %! @item info==2
+ %! MJDGGES returned an error code.
+ %! @item info==3
+ %! Blanchard & Kahn conditions are not satisfied: no stable equilibrium.
+ %! @item info==4
+ %! Blanchard & Kahn conditions are not satisfied: indeterminacy.
+ %! @item info==5
+ %! Blanchard & Kahn conditions are not satisfied: indeterminacy due to rank failure.
+ %! @item info==6
+ %! The jacobian evaluated at the deterministic steady state is complex.
+ %! @item info==19
+ %! The steadystate routine thrown an exception (inconsistent deep parameters).
+ %! @item info==20
+ %! Cannot find the steady state, info(2) contains the sum of square residuals (of the static equations).
+ %! @item info==21
+ %! The steady state is complex, info(2) contains the sum of square of imaginary parts of the steady state.
+ %! @item info==22
+ %! The steady has NaNs.
+ %! @item info==23
+ %! M_.params has been updated in the steadystate routine and has complex valued scalars.
+ %! @item info==24
+ %! M_.params has been updated in the steadystate routine and has some NaNs.
+ %! @end table
+ %! @end table
+ %! @end deftypefn
+ %@eod:
-% Copyright (C) 2001-2012 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 .
+ % Copyright (C) 2001-2012 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 .
info = 0;
lead_lag_incidence = M.lead_lag_incidence;
order_var = dr.order_var;
+ endo_nbr = M.endo_nbr;
exo_nbr = M.exo_nbr;
M.var_order_endo_names = M.endo_names(dr.order_var,:);
-
+
[junk,dr.i_fwrd_g,i_fwrd_f] = find(lead_lag_incidence(3,order_var));
dr.i_fwrd_f = i_fwrd_f;
nd = nnz(lead_lag_incidence) + M.exo_nbr;
@@ -104,420 +105,283 @@ function [dr,info] = dyn_risky_steadystate_solver(ys0,M, ...
end
if isfield(options,'portfolio') && options.portfolio == 1
- eq_tags = M.equations_tags;
- n_tags = size(eq_tags,1);
- l_var = zeros(n_tags,1);
- for i=1:n_tags
- l_var(i) = find(strncmp(eq_tags(i,3),M.endo_names, ...
- length(cell2mat(eq_tags(i,3)))));
+ pm = portfolio_model_structure(M,options);
+
+ x0 = ys0(pm.v_p);
+ n = length(x0);
+ [x, info] = solve1(@risky_residuals_ds,x0,1:n,1:n,0,1, options.gstep, ...
+ options.solve_tolf,options.solve_tolx, ...
+ options.solve_maxit,options.debug,pm,M,dr, ...
+ options,oo);
+ if info
+ error('DS approach can''t be computed')
end
- dr.ys = ys0;
- x0 = ys0(l_var);
- % dr = first_step_ds(x0,M,dr,options,oo);
- n = size(ys0);
- %x0 = ys0;
- [x, info] = solve1(@risky_residuals_ds,x0,1:n_tags,1:n_tags,0,1,M,dr,options,oo);
- %[x, info] = solve1(@risky_residuals,x0,1:n,1:n,0,1,M,dr,options,oo);
+ %[x, info] = csolve(@risky_residuals_ds,x0,[],1e-10,100,M,dr,options,oo);
% ys0(l_var) = x;
- ys0(l_var) = x;
- dr.ys = ys0;
- oo.dr = dr;
- oo.steady_state = ys0;
- disp_steady_state(M,oo);
- end
-
- [ys, info] = csolve(func,ys0,[],1e-10,100,M,dr,options,oo);
-
- if options.k_order_solver
- [resid,dr] = risky_residuals_k_order(ys,M,dr,options,oo);
- else
- [resid,dr] = risky_residuals(ys,M,dr,options,oo);
+ [resids,dr1] = risky_residuals_ds(x,pm,M,dr,options,oo);
+ ys1 = dr1.ys;
end
- dr.ys = ys;
- for i=1:M.endo_nbr
- disp(sprintf('%16s %12.6f %12.6f',M.endo_names(i,:),ys0(i), ys(i)))
- end
-
- dr.ghs2 = zeros(size(dr.ghs2));
-
- k_var = setdiff(1:M.endo_nbr,l_var);
- dr.ghx(k_var,:) = dr.ghx;
- dr.ghu(k_var,:) = dr.ghu;
- dr.ghxx(k_var,:) = dr.ghxx;
- dr.ghxu(k_var,:) = dr.ghxu;
- dr.ghuu(k_var,:) = dr.ghuu;
- dr.ghs2(k_var,:) = dr.ghs2;
-end
-
-function [resid,dr] = risky_residuals(ys,M,dr,options,oo)
- persistent old_ys old_resid
-
- lead_lag_incidence = M.lead_lag_incidence;
- iyv = lead_lag_incidence';
- iyv = iyv(:);
- iyr0 = find(iyv) ;
-
- if M.exo_nbr == 0
- oo.exo_steady_state = [] ;
- end
-
- z = repmat(ys,1,3);
- z = z(iyr0) ;
- [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
- [oo.exo_simul ...
- oo.exo_det_simul], M.params, dr.ys, 2);
- if ~isreal(d1) || ~isreal(d2)
- pause
- end
-
- if options.use_dll
- % In USE_DLL mode, the hessian is in the 3-column sparse representation
- d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
- size(d1, 1), size(d1, 2)*size(d1, 2));
- end
-
- if isfield(options,'portfolio') && options.portfolio == 1
- eq_tags = M.equations_tags;
- n_tags = size(eq_tags,1);
- portfolios_eq = cell2mat(eq_tags(strcmp(eq_tags(:,2), ...
- 'portfolio'),1));
- eq = setdiff(1:M.endo_nbr,portfolios_eq);
- l_var = zeros(n_tags,1);
- for i=1:n_tags
- l_var(i) = find(strncmp(eq_tags(i,3),M.endo_names, ...
- length(cell2mat(eq_tags(i,3)))));
- end
- k_var = setdiff(1:M.endo_nbr,l_var);
- lli1 = lead_lag_incidence(:,k_var);
- lead_incidence = lli1(3,:)';
- k = find(lli1');
- lli2 = lli1';
- lli2(k) = 1:nnz(lli1);
- lead_lag_incidence = lli2';
- x = ys(l_var);
- dr = first_step_ds(x,M,dr,options,oo);
-
-
- M.lead_lag_incidence = lead_lag_incidence;
- lli1a = [nonzeros(lli1'); size(d1,2)+(-M.exo_nbr+1:0)'];
- d1a = d1(eq,lli1a);
- ih = 1:size(d2,2);
- ih = reshape(ih,size(d1,2),size(d1,2));
- ih1 = ih(lli1a,lli1a);
- d2a = d2(eq,ih1);
-
- M.endo_nbr = M.endo_nbr-n_tags;
- dr = set_state_space(dr,M,options);
-
- [junk,dr.i_fwrd_g] = find(lead_lag_incidence(3,dr.order_var));
- i_fwrd_f = nonzeros(lead_incidence(dr.order_var));
- i_fwrd2_f = ih(i_fwrd_f,i_fwrd_f);
- dr.i_fwrd_f = i_fwrd_f;
- dr.i_fwrd2_f = i_fwrd2_f;
- nd = nnz(lead_lag_incidence) + M.exo_nbr;
- dr.nd = nd;
- kk = reshape(1:nd^2,nd,nd);
- kkk = reshape(1:nd^3,nd^2,nd);
- dr.i_fwrd2a_f = kk(i_fwrd_f,:);
- % dr.i_fwrd3_f = kkk(i_fwrd2_f,:);
- dr.i_uu = kk(end-M.exo_nbr+1:end,end-M.exo_nbr+1:end);
- else
- d1a = d1;
- d2a = d2;
- end
-
-% $$$ [junk,cols_b,cols_j] = find(lead_lag_incidence(2,dr.order_var));
-% $$$ b = zeros(M.endo_nbr,M.endo_nbr);
-% $$$ b(:,cols_b) = d1a(:,cols_j);
-% $$$
-% $$$ [dr,info] = dyn_first_order_solver(d1a,b,M,dr,options,0);
-% $$$ if info
-% $$$ [m1,m2]=max(abs(ys-old_ys));
-% $$$ disp([m1 m2])
-% $$$ % print_info(info,options.noprint);
-% $$$ resid = old_resid+info(2)/40;
-% $$$ return
-% $$$ end
-% $$$
-% $$$ dr = dyn_second_order_solver(d1a,d2a,dr,M);
-
- gu1 = dr.ghu(dr.i_fwrd_g,:);
-
- resid = resid1+0.5*(d1(:,dr.i_fwrd_f)*dr.ghuu(dr.i_fwrd_g,:)+ ...
- d2(:,dr.i_fwrd2_f)*kron(gu1,gu1))*vec(M.Sigma_e);
- disp(d1(:,dr.i_fwrd_f)*dr.ghuu(dr.i_fwrd_g,:)*vec(M.Sigma_e));
- old_ys = ys;
- disp(max(abs(resid)))
- old_resid = resid;
-end
-
-function [resid,dr] = risky_residuals_ds(x,M,dr,options,oo)
- persistent old_ys old_resid old_resid1 old_d1 old_d2
-
- dr = first_step_ds(x,M,dr,options,oo);
-
- lead_lag_incidence = M.lead_lag_incidence;
- iyv = lead_lag_incidence';
- iyv = iyv(:);
- iyr0 = find(iyv) ;
-
- if M.exo_nbr == 0
- oo.exo_steady_state = [] ;
- end
-
- eq_tags = M.equations_tags;
- n_tags = size(eq_tags,1);
- portfolios_eq = cell2mat(eq_tags(strcmp(eq_tags(:,2), ...
- 'portfolio'),1));
- eq = setdiff(1:M.endo_nbr,portfolios_eq);
- l_var = zeros(n_tags,1);
- for i=1:n_tags
- l_var(i) = find(strncmp(eq_tags(i,3),M.endo_names, ...
- length(cell2mat(eq_tags(i,3)))));
- end
- k_var = setdiff(1:M.endo_nbr,l_var);
- lli1 = lead_lag_incidence(:,k_var);
- k = find(lli1');
- lli2 = lli1';
- lli2(k) = 1:nnz(lli1);
- lead_lag_incidence = lli2';
-
- ys = dr.ys;
- ys(l_var) = x;
-
- z = repmat(ys,1,3);
- z = z(iyr0) ;
- [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
- [oo.exo_simul ...
- oo.exo_det_simul], M.params, dr.ys, 2);
-% $$$ if isempty(old_resid)
-% $$$ old_resid1 = resid1;
-% $$$ old_d1 = d1;
-% $$$ old_d2 = d2;
-% $$$ old_ys = ys;
-% $$$ else
-% $$$ if ~isequal(resid1,old_resid)
-% $$$ disp('ys')
-% $$$ disp((ys-old_ys)');
-% $$$ disp('resids1')
-% $$$ disp((resid1-old_resid1)')
-% $$$ old_resid1 = resid1;
-% $$$ pause
-% $$$ end
-% $$$ if ~isequal(d1,old_d1)
-% $$$ disp('d1')
-% $$$ disp(d1-old_d1);
-% $$$ old_d1 = d1;
-% $$$ pause
-% $$$ end
-% $$$ if ~isequal(d2,old_d2)
-% $$$ disp('d2')
-% $$$ disp(d2-old_d2);
-% $$$ old_d2 = d2;
-% $$$ pause
-% $$$ end
-% $$$ end
- if ~isreal(d1) || ~isreal(d2)
- pause
- end
-
- if options.use_dll
- % In USE_DLL mode, the hessian is in the 3-column sparse representation
- d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
- size(d1, 1), size(d1, 2)*size(d1, 2));
- end
-
-% $$$ if isfield(options,'portfolio') && options.portfolio == 1
-% $$$ lli1a = [nonzeros(lli1'); size(d1,2)+(-M.exo_nbr+1:0)'];
-% $$$ d1a = d1(eq,lli1a);
-% $$$ ih = 1:size(d2,2);
-% $$$ ih = reshape(ih,size(d1,2),size(d1,2));
-% $$$ ih1 = ih(lli1a,lli1a);
-% $$$ d2a = d2(eq,ih1);
-% $$$
-% $$$ M.endo_nbr = M.endo_nbr-n_tags;
-% $$$ dr = set_state_space(dr,M);
-% $$$
-% $$$ dr.i_fwrd_g = find(lead_lag_incidence(3,dr.order_var)');
-% $$$ else
-% $$$ d1a = d1;
-% $$$ d2a = d2;
-% $$$ end
-% $$$
-% $$$ [junk,cols_b,cols_j] = find(lead_lag_incidence(2,dr.order_var));
-% $$$ b = zeros(M.endo_nbr,M.endo_nbr);
-% $$$ b(:,cols_b) = d1a(:,cols_j);
-% $$$
-% $$$ [dr,info] = dyn_first_order_solver(d1a,b,M,dr,options,0);
-% $$$ if info
-% $$$ [m1,m2]=max(abs(ys-old_ys));
-% $$$ disp([m1 m2])
-% $$$ % print_info(info,options.noprint);
-% $$$ resid = old_resid+info(2)/40;
-% $$$ return
-% $$$ end
-% $$$
-% $$$ dr = dyn_second_order_solver(d1a,d2a,dr,M);
-
- gu1 = dr.ghu(dr.i_fwrd_g,:);
-
- % resid = resid1+0.5*(d1(:,dr.i_fwrd_f)*dr.ghuu(dr.i_fwrd_g,:)+ ...
- % d2(:,dr.i_fwrd2_f)*kron(gu1,gu1))*vec(M.Sigma_e);
- resid = resid1+0.5*(d2(:,dr.i_fwrd2_f)*kron(gu1,gu1))*vec(M.Sigma_e);
-
-% $$$ if isempty(old_resid)
-% $$$ old_resid = resid;
-% $$$ else
-% $$$ disp('resid')
-% $$$ dr = (resid-old_resid)';
-% $$$ % disp(dr)
-% $$$ % disp(dr(portfolios_eq))
-% $$$ old_resid = resid;
-% $$$ end
- resid = resid(portfolios_eq)
-end
-
-function [dr] = first_step_ds(x,M,dr,options,oo)
-
- lead_lag_incidence = M.lead_lag_incidence;
- iyv = lead_lag_incidence';
- iyv = iyv(:);
- iyr0 = find(iyv) ;
-
- if M.exo_nbr == 0
- oo.exo_steady_state = [] ;
- end
-
- eq_tags = M.equations_tags;
- n_tags = size(eq_tags,1);
- portfolios_eq = cell2mat(eq_tags(strcmp(eq_tags(:,2), ...
- 'portfolio'),1));
- eq = setdiff(1:M.endo_nbr,portfolios_eq);
- l_var = zeros(n_tags,1);
- for i=1:n_tags
- l_var(i) = find(strncmp(eq_tags(i,3),M.endo_names, ...
- length(cell2mat(eq_tags(i,3)))));
- end
- k_var = setdiff(1:M.endo_nbr,l_var);
- lli1 = lead_lag_incidence(:,k_var);
- k = find(lli1');
- lli2 = lli1';
- lli2(k) = 1:nnz(lli1);
- lead_lag_incidence = lli2';
- M.lead_lag_incidence = lead_lag_incidence;
-
- ys = dr.ys;
- ys(l_var) = x;
-
- z = repmat(ys,1,3);
- z = z(iyr0) ;
- [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
- [oo.exo_simul ...
- oo.exo_det_simul], M.params, dr.ys, 2);
- if ~isreal(d1) || ~isreal(d2)
- pause
- end
-
- if options.use_dll
- % In USE_DLL mode, the hessian is in the 3-column sparse representation
- d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
- size(d1, 1), size(d1, 2)*size(d1, 2));
- end
-
- if isfield(options,'portfolio') && options.portfolio == 1
- lli1a = [nonzeros(lli1'); size(d1,2)+(-M.exo_nbr+1:0)'];
- d1a = d1(eq,lli1a);
- ih = 1:size(d2,2);
- ih = reshape(ih,size(d1,2),size(d1,2));
- ih1 = ih(lli1a,lli1a);
- d2a = d2(eq,ih1);
-
- M.endo_nbr = M.endo_nbr-n_tags;
- dr = set_state_space(dr,M,options);
-
- dr.i_fwrd_g = find(lead_lag_incidence(3,dr.order_var)');
- else
- d1a = d1;
- d2a = d2;
- end
-
- [junk,cols_b,cols_j] = find(lead_lag_incidence(2,dr.order_var));
- b = zeros(M.endo_nbr,M.endo_nbr);
- b(:,cols_b) = d1a(:,cols_j);
-
- [dr,info] = dyn_first_order_solver(d1a,M,dr,options,0);
+ [ys, info] = solve1(func,ys0,1:endo_nbr,1:endo_nbr,0,1, options.gstep, ...
+ options.solve_tolf,options.solve_tolx, ...
+ options.solve_maxit,options.debug,pm,M,dr,options,oo);
+ % [ys, info] = csolve(func,ys0,[],1e-10,100,M,dr,options,oo);
if info
- [m1,m2]=max(abs(ys-old_ys));
- disp([m1 m2])
- % print_info(info,options.noprint);
- resid = old_resid+info(2)/40;
+ error('RSS approach can''t be computed')
+ end
+ dr.ys = ys;
+
+ [resid,dr] = func(ys,pm,M,dr,options,oo);
+
+ for i=1:M.endo_nbr
+ disp(sprintf('%16s %12.6f %12.6f',M.endo_names(i,:),ys1(i), ys(i)))
+ end
+
+end
+
+function [resid,dr] = risky_residuals(ys,pm,M,dr,options,oo)
+
+ lead_lag_incidence = M.lead_lag_incidence;
+ iyv = lead_lag_incidence';
+ iyv = iyv(:);
+ iyr0 = find(iyv) ;
+
+ if M.exo_nbr == 0
+ oo.exo_steady_state = [] ;
+ end
+
+ z = repmat(ys,1,3);
+ z = z(iyr0) ;
+ [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
+ [oo.exo_simul ...
+ oo.exo_det_simul], M.params, dr.ys, 2);
+ if ~isreal(d1) || ~isreal(d2)
+ pause
+ end
+
+ if options.use_dll
+ % In USE_DLL mode, the hessian is in the 3-column sparse representation
+ d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
+ size(d1, 1), size(d1, 2)*size(d1, 2));
+ end
+
+ if isfield(options,'portfolio') && options.portfolio == 1
+ pm = portfolio_model_structure(M,options);
+ x = ys(pm.v_p);
+ dr = first_step_ds(x,pm,M,dr,options,oo);
+ dr.ys = ys;
+ else
+ d1a = d1;
+ d2a = d2;
+ end
+
+ gu1 = dr.ghu(pm.i_fwrd_g,:);
+
+ resid = resid1+0.5*(d1(:,pm.i_fwrd_f1)*dr.ghuu(pm.i_fwrd_g,:)+ ...
+ d2(:,pm.i_fwrd_f2)*kron(gu1,gu1))*vec(M.Sigma_e);
+end
+
+function [resid,dr] = risky_residuals_ds(x,pm,M,dr,options,oo)
+
+ v_p = pm.v_p;
+ v_np = pm.v_np;
+
+ % computing steady state of non-portfolio variables consistent with
+ % assumed portfolio
+ dr.ys(v_p) = x;
+ ys0 = dr.ys(v_np);
+ f_h =str2func([M.fname '_static']);
+ [dr.ys(v_np),info] = csolve(@ds_static_model,ys0,[],1e-10,100,f_h,x,pm.eq_np,v_np,v_p, ...
+ M.endo_nbr,M.exo_nbr,M.params);
+ if info
+ error('can''t compute non-portfolio steady state')
+ end
+
+ dr_np = first_step_ds(x,pm,M,dr,options,oo);
+
+ lead_lag_incidence = M.lead_lag_incidence;
+ iyv = lead_lag_incidence';
+ iyv = iyv(:);
+ iyr0 = find(iyv) ;
+
+ z = repmat(dr.ys,1,3);
+ z = z(iyr0) ;
+ [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
+ [oo.exo_simul ...
+ oo.exo_det_simul], M.params, dr.ys, 2);
+ if ~isreal(d1) || ~isreal(d2)
+ pause
+ end
+
+ if options.use_dll
+ % In USE_DLL mode, the hessian is in the 3-column sparse representation
+ d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
+ size(d1, 1), size(d1, 2)*size(d1, 2));
+ end
+
+
+ gu1 = dr_np.ghu(pm.i_fwrd_g,:);
+
+ resid = resid1+0.5*(d2(:,pm.i_fwrd_f2)*kron(gu1,gu1))*vec(M.Sigma_e);
+
+ resid = resid(pm.eq_p)
+end
+
+function dr_np = first_step_ds(x,pm,M,dr,options,oo)
+
+ lead_lag_incidence = M.lead_lag_incidence;
+ iyv = lead_lag_incidence';
+ iyv = iyv(:);
+ iyr0 = find(iyv) ;
+
+ ys = dr.ys;
+ ys(pm.v_p) = x;
+
+ z = repmat(ys,1,3);
+ z = z(iyr0) ;
+ [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
+ [oo.exo_simul ...
+ oo.exo_det_simul], M.params, dr.ys, 2);
+ if ~isreal(d1) || ~isreal(d2)
+ pause
+ end
+
+ if options.use_dll
+ % In USE_DLL mode, the hessian is in the 3-column sparse representation
+ d2 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
+ size(d1, 1), size(d1, 2)*size(d1, 2));
+ end
+
+ d1_np = d1(pm.eq_np,pm.i_d1_np);
+ d2_np = d2(pm.eq_np,pm.i_d2_np);
+
+ [dr_np,info] = dyn_first_order_solver(d1_np,pm.M_np,pm.dr_np,options,0);
+ if info
+ print_info(info,0);
return
end
- dr = dyn_second_order_solver(d1a,d2a,dr,M,...
- options.threads.kronecker.A_times_B_kronecker_C,...
- options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
+ dr_np = dyn_second_order_solver(d1_np,d2_np,dr_np,pm.M_np,...
+ options.threads.kronecker.A_times_B_kronecker_C,...
+ options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
end
-function [resid,dr] = risky_residuals_k_order(ys,M,dr,options,oo)
-
- lead_lag_incidence = M.lead_lag_incidence;
- npred = dr.npred;
+function [resid,dr] = risky_residuals_k_order(ys,pm,M,dr,options,oo)
exo_nbr = M.exo_nbr;
- vSigma_e = vec(M.Sigma_e);
+ endo_nbr = M.endo_nbr;
- iyv = lead_lag_incidence';
+ iyv = M.lead_lag_incidence';
iyv = iyv(:);
iyr0 = find(iyv) ;
- if M.exo_nbr == 0
+ if exo_nbr == 0
oo.exo_steady_state = [] ;
end
z = repmat(ys,1,3);
z = z(iyr0) ;
- [resid1,d1,d2,d3] = feval([M.fname '_dynamic'],z,...
- [oo.exo_simul ...
+ [resid1,d1,d2] = feval([M.fname '_dynamic'],z,...
+ [oo.exo_simul ...
oo.exo_det_simul], M.params, dr.ys, 2);
-
-% $$$ hessian = sparse(d2(:,1), d2(:,2), d2(:,3), ...
-% $$$ size(d1, 1), size(d1, 2)*size(d1, 2));
-% $$$ fy3 = sparse(d2(:,1), d2(:,2), d2(:,3), ...
-% $$$ size(d1, 1), size(d1, 2)^3);
-
- options.order = 3;
- nu2 = exo_nbr*(exo_nbr+1)/2;
-% $$$ d1_0 = d1;
-% $$$ gu1 = dr.ghu(dr.i_fwrd_g,:);
-% $$$ guu = dr.ghuu;
-% $$$ for i=1:2
-% $$$ d1 = d1_0 + 0.5*(hessian(:,dr.i_fwrd2a_f)*kron(eye(dr.nd),guu(dr.i_fwrd_g,:)*vSigma_e)+ ...
-% $$$ fy3(:,dr.i_fwrd3_f)*kron(eye(dr.nd),kron(gu1,gu1)*vSigma_e));
-% $$$ [junk,cols_b,cols_j] = find(lead_lag_incidence(2,dr.order_var));
-% $$$ b = zeros(M.endo_nbr,M.endo_nbr);
-% $$$ b(:,cols_b) = d1(:,cols_j);
+ if isfield(options,'portfolio') && options.portfolio == 1
+ eq_np = pm.eq_np;
+
+ d1_np = d1(eq_np,pm.i_d1_np);
+ d2_np = d2(eq_np,pm.i_d2_np);
-% $$$ [dr,info] = dyn_first_order_solver(d1,b,M,dr,options,0);
- [err,g_0, g_1, g_2, g_3] = k_order_perturbation(dr,M,options);
- mexErrCheck('k_order_perturbation', err);
- gu1 = g_1(dr.i_fwrd_g,end-exo_nbr+1:end);
- guu = unfold(g_2(:,end-nu2+1:end),exo_nbr);
- d1old = d1;
- % disp(max(max(abs(d1-d1old))));
- % end
+ M_np = pm.M_np;
+ dr_np = pm.dr_np;
+
+ [dr_np,info] = dyn_first_order_solver(d1_np,pm.M_np,pm.dr_np,options,0);
+ if info
+ print_info(info,0);
+ return
+ end
+
+ dr_np = dyn_second_order_solver(d1_np,d2_np,dr_np,pm.M_np,...
+ options.threads.kronecker.A_times_B_kronecker_C,...
+ options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
+ end
- [junk,cols_b,cols_j] = find(lead_lag_incidence(2,dr.order_var));
+ i_fwrd_f1 = pm.i_fwrd_f1;
+ i_fwrd_f2 = pm.i_fwrd_f2;
+ i_fwrd_f3 = pm.i_fwrd_f3;
+ i_fwrd_g = pm.i_fwrd_g;
+ gu1 = dr_np.ghu(i_fwrd_g,:);
+ ghuu = dr_np.ghuu;
- resid = resid1+0.5*(d1(:,dr.i_fwrd_f)*guu(dr.i_fwrd_g,:)+hessian(:,dr.i_fwrd2_f)*kron(gu1,gu1))*vec(M.Sigma_e);
+ resid = resid1+0.5*(d1(:,i_fwrd_f1)*ghuu(i_fwrd_g,:)+d2(:,i_fwrd_f2)* ...
+ kron(gu1,gu1))*vec(M.Sigma_e);
if nargout > 1
- [dr,info] = k_order_pert(dr,M,options,oo);
+ [resid1,d1,d2,d3] = feval([M.fname '_dynamic'],z,...
+ [oo.exo_simul ...
+ oo.exo_det_simul], M.params, dr.ys, 2);
+
+
+ [a,b,c] = find(d2(eq_np,pm.i_d2_np));
+ d2_np = [a b c];
+
+ [a,b,c] = find(d3(eq_np,pm.i_d3_np));
+ d3_np = [a b c];
+
+ options.order = 3;
+ % space holder, unused by k_order_pertrubation
+ dr_np.ys = dr.ys(pm.v_np);
+ nu2 = exo_nbr*(exo_nbr+1)/2;
+ nu3 = exo_nbr*(exo_nbr+1)*(exo_nbr+2)/3;
+ M_np.NZZDerivatives = [nnz(d1_np); nnz(d2_np); nnz(d3_np)];
+ [err,g_0, g_1, g_2, g_3] = k_order_perturbation(dr_np,M_np,options,d1_np,d2_np,d3_np);
+ mexErrCheck('k_order_perturbation', err);
+
+ gu1 = g_1(i_fwrd_g,end-exo_nbr+1:end);
+ ghuu = unfold2(g_2(:,end-nu2+1:end),exo_nbr);
+ ghsuu = get_ghsuu(g_3,size(g_1,2),exo_nbr);
+
+ i_fwrd1_f2 = pm.i_fwrd1_f2;
+ i_fwrd1_f3 = pm.i_fwrd1_f3;
+ n = size(d1,2);
+ d1b = d1 + 0.5*( ...
+ d1(:,i_fwrd_f1)*...
+ d2(:,i_fwrd1_f2)*kron(eye(n),dr_np.ghuu(i_fwrd_g,:)*vec(M.Sigma_e))...
+ + 0.5*d3(:,i_fwrd1_f3)*kron(eye(n),kron(gu1,gu1)*vec(M.Sigma_e)));
+ format short
+ kk1 = [nonzeros(M.lead_lag_incidence(:,1:6)'); ...
+ nnz(M.lead_lag_incidence)+[1; 2]]
+ kk2 = [nonzeros(M.lead_lag_incidence(:,1:6)'); ...
+ nnz(M.lead_lag_incidence)+[3; 4]]
+ format short
+ gu1
+ kron(gu1,gu1)*vec(M.Sigma_e)
+ disp(d1(:,:))
+ disp(d1b(:,:))
+ aa2=d2(:,i_fwrd1_f2)*kron(eye(n),dr_np.ghuu(i_fwrd_g,:)*vec(M.Sigma_e));
+ aa3=d3(:,i_fwrd1_f3)*kron(eye(n),kron(gu1,gu1)*vec(M.Sigma_e));
+ disp(d3(4,7+6*n+6*n*n))
+ disp(d3(4,8+16*n+17*n*n)) %8,17,18
+ disp(d3(4,8+17*n+16*n*n)) %8,17,18
+ disp(d3(4,7*n+17+17*n*n)) %8,17,18
+ disp(d3(4,7*n+18+16*n*n)) %8,17,18
+ disp(d3(4,7*n*n+16*n+18)) %8,17,18
+ disp(d3(4,7*n*n+17+17*n)) %8,17,18
+ pause
+ disp(aa2(:,kk1))
+ disp(aa2(:,kk2))
+ disp(aa3(:,kk1))
+ disp(aa3(:,kk2))
+ [dr,info] = dyn_first_order_solver(d1b,M,dr,options,0);
+ if info
+ print_info(info,0);
+ return
+ end
+
+ disp_dr(dr,dr.order_var,[]);
+
end
end
-function y=unfold(x,n)
+function y=unfold2(x,n)
y = zeros(size(x,1),n*n);
k = 1;
for i=1:n
@@ -526,6 +390,122 @@ function y=unfold(x,n)
if i ~= j
y(:,(j-1)*n+i) = x(:,k);
end
+ k = k+1;
end
end
end
+
+function y=unfold3(x,n)
+ y = zeros(size(x,1),n*n*n);
+ k = 1;
+ for i=1:n
+ for j=i:n
+ for m=j:n
+ y(:,(i-1)*n*n+(j-1)*n+m) = x(:,k);
+ y(:,(i-1)*n*n+(m-1)*n+j) = x(:,k);
+ y(:,(j-1)*n*n+(i-1)*n+m) = x(:,k);
+ y(:,(j-1)*n*n+(m-1)*n+i) = x(:,k);
+ y(:,(m-1)*n*n+(i-1)*n+j) = x(:,k);
+ y(:,(m-1)*n*n+(j-1)*n+i) = x(:,k);
+
+ k = k+1;
+ end
+ end
+ end
+end
+
+function pm = portfolio_model_structure(M,options)
+
+ i_d3_np = [];
+ i_d3_p = [];
+
+ lead_index = M.maximum_endo_lag+2;
+ lead_lag_incidence = M.lead_lag_incidence;
+ eq_tags = M.equations_tags;
+ n_tags = size(eq_tags,1);
+ eq_p = cell2mat(eq_tags(strcmp(eq_tags(:,2), ...
+ 'portfolio'),1));
+ pm.eq_p = eq_p;
+ pm.eq_np = setdiff(1:M.endo_nbr,eq_p);
+ v_p = zeros(n_tags,1);
+ for i=1:n_tags
+ v_p(i) = find(strncmp(eq_tags(i,3),M.endo_names, ...
+ length(cell2mat(eq_tags(i,3)))));
+ end
+ if any(lead_lag_incidence(lead_index,v_p))
+ error(['portfolio variables appear in the model as forward ' ...
+ 'variable'])
+ end
+ pm.v_p = v_p;
+ v_np = setdiff(1:M.endo_nbr,v_p);
+ pm.v_np = v_np;
+ lli_np = lead_lag_incidence(:,v_np)';
+ k = find(lli_np);
+ lead_lag_incidence_np = lli_np;
+ lead_lag_incidence_np(k) = 1:nnz(lli_np);
+ lead_lag_incidence_np = lead_lag_incidence_np';
+ pm.lead_lag_incidence_np = lead_lag_incidence_np;
+ i_d1_np = [nonzeros(lli_np); nnz(lead_lag_incidence)+(1:M.exo_nbr)'];
+ pm.i_d1_np = i_d1_np;
+
+ n = nnz(lead_lag_incidence)+M.exo_nbr;
+ ih = reshape(1:n*n,n,n);
+ i_d2_np = ih(i_d1_np,i_d1_np);
+ pm.i_d2_np = i_d2_np(:);
+
+ ih = reshape(1:n*n*n,n,n,n);
+ i_d3_np = ih(i_d1_np,i_d1_np,i_d1_np);
+ pm.i_d3_np = i_d3_np(:);
+
+ M_np = M;
+ M_np.lead_lag_incidence = lead_lag_incidence_np;
+ M_np.lead_lag_incidence = lead_lag_incidence_np;
+ M_np.endo_nbr = length(v_np);
+ M_np.endo_names = M.endo_names(v_np,:);
+ dr_np = struct();
+ dr_np = set_state_space(dr_np,M_np,options);
+ pm.dr_np = dr_np;
+ M_np.var_order_endo_names = M_np.endo_names(dr_np.order_var,:);
+ pm.M_np = M_np;
+ pm.i_fwrd_g = find(lead_lag_incidence_np(lead_index,dr_np.order_var)');
+
+ i_fwrd_f1 = nonzeros(lead_lag_incidence(lead_index,:));
+ pm.i_fwrd_f1 = i_fwrd_f1;
+ n = nnz(lead_lag_incidence)+M.exo_nbr;
+ ih = reshape(1:n*n,n,n);
+ i_fwrd_f2 = ih(i_fwrd_f1,i_fwrd_f1);
+ pm.i_fwrd_f2 = i_fwrd_f2(:);
+ i_fwrd1_f2 = ih(i_fwrd_f1,:);
+ pm.i_fwrd1_f2 = i_fwrd1_f2(:);
+
+ ih = reshape(1:n*n*n,n,n,n);
+ i_fwrd_f3 = ih(i_fwrd_f1,i_fwrd_f1,i_fwrd_f1);
+ pm.i_fwrd_f3 = i_fwrd_f3(:);
+ i_fwrd1_f3 = ih(i_fwrd_f1,i_fwrd_f1,:);
+ pm.i_fwrd1_f3 = i_fwrd1_f3(:);
+end
+
+function r=ds_static_model(y0,f_h,p0,eq_np,v_np,v_p,endo_nbr,exo_nbr,params)
+ ys = zeros(endo_nbr,1);
+ ys(v_p) = p0;
+ ys(v_np) = y0;
+ r = f_h(ys,zeros(exo_nbr,1),params);
+ r = r(eq_np);
+end
+
+function ghsuu = get_ghsuu(g,ns,nx)
+ nxx = nx*(nx+1)/2;
+ m1 = 0;
+ m2 = ns*(ns+1)/2;
+ kk = 1:(nx*nx);
+ ghsuu = zeros(size(g,1),(ns*nx*nx));
+
+ for i=1:n
+ j = m1+(1:m2);
+ k = j(end-nxx+1:end);
+ ghsuu(:,kk) = unfold2(g(:,k),nx);
+ m1 = m1+m2;
+ m2 = m2 - (n-i+1);
+ kk = kk + nx*nx;
+ end
+end
\ No newline at end of file
diff --git a/matlab/dynare_solve.m b/matlab/dynare_solve.m
index 72555ed85..da0e58921 100644
--- a/matlab/dynare_solve.m
+++ b/matlab/dynare_solve.m
@@ -74,7 +74,9 @@ if options_.solve_algo == 0
end
elseif options_.solve_algo == 1
nn = size(x,1);
- [x,info]=solve1(func,x,1:nn,1:nn,jacobian_flag,1,varargin{:});
+ [x,info]=solve1(func,x,1:nn,1:nn,jacobian_flag,1,options_.gstep, ...
+ options_.solve_tolf,options_.solve_tolx, ...
+ options_.solve_maxit,options_.debug,varargin{:});
elseif options_.solve_algo == 2 || options_.solve_algo == 4
nn = size(x,1) ;
tolf = options_.solve_tolf ;
@@ -125,14 +127,19 @@ elseif options_.solve_algo == 2 || options_.solve_algo == 4
if options_.debug
disp(['DYNARE_SOLVE (solve_algo=2|4): solving block ' num2str(i) ', of size ' num2str(r(i+1)-r(i)) ]);
end
- [x,info]=solve1(func,x,j1(r(i):r(i+1)-1),j2(r(i):r(i+1)-1),jacobian_flag, bad_cond_flag, varargin{:});
+ [x,info]=solve1(func,x,j1(r(i):r(i+1)-1),j2(r(i):r(i+1)-1),jacobian_flag, ...
+ bad_cond_flag, options_.gstep, ...
+ options_.solve_tolf,options_.solve_tolx, ...
+ options_.solve_maxit,options_.debug,varargin{:});
if info
return
end
end
fvec = feval(func,x,varargin{:});
if max(abs(fvec)) > tolf
- [x,info]=solve1(func,x,1:nn,1:nn,jacobian_flag, bad_cond_flag, varargin{:});
+ [x,info]=solve1(func,x,1:nn,1:nn,jacobian_flag, bad_cond_flag, ...
+ options_.gstep, options_.solve_tolf,options_.solve_tolx, ...
+ options_.solve_maxit,options_.debug,varargin{:});
end
elseif options_.solve_algo == 3
if jacobian_flag
diff --git a/matlab/sim1_purely_backward.m b/matlab/sim1_purely_backward.m
index b432c4789..beb699880 100644
--- a/matlab/sim1_purely_backward.m
+++ b/matlab/sim1_purely_backward.m
@@ -34,7 +34,11 @@ function sim1_purely_backward()
yb = oo_.endo_simul(:,it-1); % Values at previous period, also used as guess value for current period
yb1 = yb(iyb);
- tmp = solve1(model_dynamic, [yb1; yb], 1:M_.endo_nbr, nyb+1:nyb+M_.endo_nbr, 1, 1, oo_.exo_simul, M_.params, oo_.steady_state, it);
+ tmp = solve1(model_dynamic, [yb1; yb], 1:M_.endo_nbr, nyb+1:nyb+ ...
+ M_.endo_nbr, 1, 1, options_.gstep, ...
+ options_.solve_tolf,options_.solve_tolx, ...
+ options_.solve_maxit,options_.debug,oo_.exo_simul, ...
+ M_.params, oo_.steady_state, it);
oo_.endo_simul(:,it) = tmp(nyb+1:nyb+M_.endo_nbr);
end
\ No newline at end of file
diff --git a/matlab/sim1_purely_forward.m b/matlab/sim1_purely_forward.m
index 65a874b50..795cb25e2 100644
--- a/matlab/sim1_purely_forward.m
+++ b/matlab/sim1_purely_forward.m
@@ -33,7 +33,11 @@ function sim1_purely_forward()
yf = oo_.endo_simul(:,it+1); % Values at next period, also used as guess value for current period
yf1 = yf(iyf);
- tmp = solve1(model_dynamic, [yf; yf1], 1:M_.endo_nbr, 1:M_.endo_nbr, 1, 1, oo_.exo_simul, M_.params, oo_.steady_state, it);
+ tmp = solve1(model_dynamic, [yf; yf1], 1:M_.endo_nbr, 1:M_.endo_nbr, ...
+ 1, 1, options_.gstep, options_.solve_tolf, ...
+ options_.solve_tolx, options_.solve_maxit, ...
+ options_.debug,oo_.exo_simul, M_.params, oo_.steady_state, ...
+ it);
oo_.endo_simul(:,it) = tmp(1:M_.endo_nbr);
end
\ No newline at end of file
diff --git a/matlab/simul_backward_nonlinear_model.m b/matlab/simul_backward_nonlinear_model.m
index 546990cb5..80dec87a2 100644
--- a/matlab/simul_backward_nonlinear_model.m
+++ b/matlab/simul_backward_nonlinear_model.m
@@ -104,6 +104,10 @@ DynareOutput.endo_simul(:,1) = DynareOutput.steady_state;
for it = 2:sample_size+1
y(jdx) = DynareOutput.endo_simul(:,it-1); % A good guess for the initial conditions is the previous values for the endogenous variables.
y(hdx) = y(jdx(iy1)); % Set lagged variables.
- y(jdx) = solve1(model_dynamic, y, idx, jdx, 1, 1, DynareOutput.exo_simul, DynareModel.params, DynareOutput.steady_state, it);
+ y(jdx) = solve1(model_dynamic, y, idx, jdx, 1, 1, DynareOptions.gstep, ...
+ DynareOptions.solve_tolf,DynareOptions.solve_tolx, ...
+ DynareOptions.solve_maxit,DynareOptions.debug, ...
+ DynareOutput.exo_simul, DynareModel.params, ...
+ DynareOutput.steady_state, it);
DynareOutput.endo_simul(:,it) = y(jdx);
end
\ No newline at end of file
diff --git a/matlab/solve1.m b/matlab/solve1.m
index 74111276f..97cb5e24e 100644
--- a/matlab/solve1.m
+++ b/matlab/solve1.m
@@ -1,4 +1,4 @@
-function [x,check] = solve1(func,x,j1,j2,jacobian_flag,bad_cond_flag,varargin)
+function [x,check] = solve1(func,x,j1,j2,jacobian_flag,bad_cond_flag,gstep,tolf,tolx,maxit,debug,varargin)
% function [x,check] = solve1(func,x,j1,j2,jacobian_flag,bad_cond_flag,varargin)
% Solves systems of non linear equations of several variables
%
@@ -11,6 +11,12 @@ function [x,check] = solve1(func,x,j1,j2,jacobian_flag,bad_cond_flag,varargin)
% jacobian_flag=0: jacobian obtained numerically
% bad_cond_flag=1: when Jacobian is badly conditionned, use an
% alternative formula to Newton step
+% gstep increment multiplier in numercial derivative
+% computation
+% tolf tolerance for residuals
+% tolx tolerance for solution variation
+% maxit maximum number of iterations
+% debug debug flag
% varargin: list of arguments following bad_cond_flag
%
% OUTPUTS
@@ -37,18 +43,13 @@ function [x,check] = solve1(func,x,j1,j2,jacobian_flag,bad_cond_flag,varargin)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see .
-global M_ options_ fjac
-
nn = length(j1);
fjac = zeros(nn,nn) ;
g = zeros(nn,1) ;
-tolf = options_.solve_tolf ;
-tolx = options_.solve_tolx;
tolmin = tolx ;
stpmx = 100 ;
-maxit = options_.solve_maxit ;
check = 0 ;
@@ -77,7 +78,7 @@ for its = 1:maxit
fvec = fvec(j1);
fjac = fjac(j1,j2);
else
- dh = max(abs(x(j2)),options_.gstep(1)*ones(nn,1))*eps^(1/3);
+ dh = max(abs(x(j2)),gstep(1)*ones(nn,1))*eps^(1/3);
for j = 1:nn
xdh = x ;
@@ -89,33 +90,10 @@ for its = 1:maxit
end
g = (fvec'*fjac)';
- if options_.debug
+ if debug
disp(['cond(fjac) ' num2str(cond(fjac))])
end
- M_.unit_root = 0;
- if M_.unit_root
- if first_time
- first_time = 0;
- [q,r,e]=qr(fjac);
- n = sum(abs(diag(r)) < 1e-12);
- fvec = q'*fvec;
- p = e*[-r(1:end-n,1:end-n)\fvec(1:end-n);zeros(n,1)];
- disp(' ')
- disp('STEADY with unit roots:')
- disp(' ')
- if n > 0
- disp([' The following variable(s) kept their value given in INITVAL' ...
- ' or ENDVAL'])
- disp(char(e(:,end-n+1:end)'*M_.endo_names))
- else
- disp(' STEADY can''t find any unit root!')
- end
- else
- [q,r]=qr(fjac*e);
- fvec = q'*fvec;
- p = e*[-r(1:end-n,1:end-n)\fvec(1:end-n);zeros(n,1)];
- end
- elseif bad_cond_flag && cond(fjac) > 1/sqrt(eps)
+ if bad_cond_flag && rcond(fjac) < sqrt(eps)
fjac2=fjac'*fjac;
p=-(fjac2+1e6*sqrt(nn*eps)*max(sum(abs(fjac2)))*eye(nn))\(fjac'*fvec);
else
@@ -126,7 +104,7 @@ for its = 1:maxit
[x,f,fvec,check]=lnsrch1(xold,fold,g,p,stpmax,func,j1,j2,varargin{:});
- if options_.debug
+ if debug
disp([its f])
disp([xold x])
end