diff --git a/matlab/default_option_values.m b/matlab/default_option_values.m
index 66ba29f13..b4cdf2d93 100644
--- a/matlab/default_option_values.m
+++ b/matlab/default_option_values.m
@@ -12,7 +12,7 @@ function options_ = default_option_values(M_)
% SPECIAL REQUIREMENTS
% none
-% Copyright (C) 2018-2020 Dynare Team
+% Copyright (C) 2018-2021 Dynare Team
%
% This file is part of Dynare.
%
@@ -696,9 +696,6 @@ options_.graph_save_formats.eps = 1;
options_.graph_save_formats.pdf = 0;
options_.graph_save_formats.fig = 0;
-% risky steady state
-options_.risky_steadystate = false;
-
% endogenous prior
options_.endogenous_prior = false;
options_.endogenous_prior_restrictions.irf={};
diff --git a/matlab/dyn_risky_steadystate_solver.m b/matlab/dyn_risky_steadystate_solver.m
deleted file mode 100644
index 7fa8a59ff..000000000
--- a/matlab/dyn_risky_steadystate_solver.m
+++ /dev/null
@@ -1,529 +0,0 @@
-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 has 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) 2001-2020 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;
-
-[~,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;
-dr.nd = nd;
-kk = reshape(1:nd^2,nd,nd);
-kkk = reshape(1:nd^3,nd^2,nd);
-dr.i_fwrd2_f = kk(i_fwrd_f,i_fwrd_f);
-dr.i_fwrd2a_f = kk(i_fwrd_f,:);
-dr.i_fwrd3_f = kkk(dr.i_fwrd2_f,:);
-dr.i_uu = kk(end-exo_nbr+1:end,end-exo_nbr+1:end);
-if options.k_order_solver
- func = @risky_residuals_k_order;
-else
- func = @risky_residuals;
-end
-
-if isfield(options,'portfolio') && options.portfolio == 1
- 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,options.gstep, ...
- options.solve_tolf,options.solve_tolx, ...
- options.steady.maxit,options.debug,pm,M,dr, ...
- options,oo);
- if info
- error('DS approach can''t be computed')
- end
- %[x, info] = csolve(@risky_residuals_ds,x0,[],1e-10,100,M,dr,options,oo);
- % ys0(l_var) = x;
- [resids,dr1] = risky_residuals_ds(x,pm,M,dr,options,oo);
- ys1 = dr1.ys;
-else
- pm = model_structure(M,options);
-end
-
-[ys, info] = solve1(func,ys0,1:endo_nbr,1:endo_nbr,0,options.gstep, ...
- options.solve_tolf,options.solve_tolx, ...
- options.steady.maxit,options.debug,pm,M,dr,options,oo);
-% [ys, info] = csolve(func,ys0,[],1e-10,100,M,dr,options,oo);
-if info
- error('RSS approach can''t be computed')
-end
-dr.ys = ys;
-
-[resid,dr] = func(ys,pm,M,dr,options,oo);
-dr.ghs2 = zeros(M.endo_nbr,1);
-
-for i=1:M.endo_nbr
- if isfield(options,'portfolio') && options.portfolio == 1
- disp(sprintf('%16s %12.6f %12.6f', M.endo_names{i}, ys1(i), ...
- ys(i)))
- else
- disp(sprintf('%16s %12.6f %12.6f', M.endo_names{i}, ys(i)))
- end
-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 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
- pm = model_structure(M,options);
- [dr,info] = dyn_first_order_solver(d1,M,dr,options,0);
- if info
- print_info(info,options.noprint,options);
- end
- dr = dyn_second_order_solver(d1,d2,dr,M,...
- options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
-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
-
-
-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
-
-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, options);
- return
-end
-
-dr_np = dyn_second_order_solver(d1_np,d2_np,dr_np,pm.M_np,...
- options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
-end
-
-function [resid,dr] = risky_residuals_k_order(ys,pm,M,dr,options,oo)
-exo_nbr = M.exo_nbr;
-endo_nbr = M.endo_nbr;
-
-iyv = M.lead_lag_incidence';
-iyv = iyv(:);
-iyr0 = find(iyv) ;
-
-if 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 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);
-
- 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, options);
- return
- end
-
- dr_np = dyn_second_order_solver(d1_np,d2_np,dr_np,pm.M_np,...
- options.threads.kronecker.sparse_hessian_times_B_kronecker_C);
-end
-
-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(:,i_fwrd_f1)*ghuu(i_fwrd_g,:)+d2(:,i_fwrd_f2)* ...
- kron(gu1,gu1))*vec(M.Sigma_e);
-
-if nargout > 1
- [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)];
- dynpp_derivs = k_order_perturbation(dr_np,M_np,options,d1_np,d2_np,d3_np);
- g_0 = dynpp_derivs.g_0;
- g_1 = dynpp_derivs.g_1;
- g_2 = dynpp_derivs.g_2;
- g_3 = dynpp_derivs.g_3;
-
- 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, options);
- return
- end
-
- disp_dr(dr,dr.order_var,[]);
-
-end
-end
-
-function y=unfold2(x,n)
-y = zeros(size(x,1),n*n);
-k = 1;
-for i=1:n
- for j=i:n
- y(:,(i-1)*n+j) = x(:,k);
- 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 = model_structure(M,options)
-
-
-lead_index = M.maximum_endo_lag+2;
-lead_lag_incidence = M.lead_lag_incidence;
-dr = struct();
-dr = set_state_space(dr,M,options);
-pm.i_fwrd_g = find(lead_lag_incidence(lead_index,dr.order_var)');
-
-i_fwrd_f1 = nonzeros(lead_lag_incidence(lead_index,dr.order_var));
-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 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;
-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
diff --git a/matlab/mex/k_order_perturbation.m b/matlab/mex/k_order_perturbation.m
index 15e34a7ce..25b1142e6 100644
--- a/matlab/mex/k_order_perturbation.m
+++ b/matlab/mex/k_order_perturbation.m
@@ -1,13 +1,10 @@
-% [dynpp_derivs, dyn_derivs] = k_order_perturbation(dr,DynareModel,DynareOptions,g1,g2,g3)
-% computes a k_order_petrubation solution for k=1,2,3
+% [dynpp_derivs, dyn_derivs] = k_order_perturbation(dr,DynareModel,DynareOptions)
+% computes a k-th order perturbation solution
%
% INPUTS
% dr: struct describing the reduced form solution of the model.
% DynareModel: struct jobs's parameters
% DynareOptions: struct job's options
-% g1: matrix First order derivatives of the model (optional)
-% g2: matrix Second order derivatives of the model (optional)
-% g3: matrix Third order derivatives of the model (optional)
%
% OUTPUTS
% dynpp_derivs struct Derivatives of the decision rule in Dynare++ format.
@@ -24,11 +21,11 @@
% + if order ≥ 2: gyy, gyu, guu, gss
% + if order ≥ 3: gyyy, gyyu, gyuu, guuu, gyss, guss
%
-% k_order_perturbation is a compiled MEX function. It's source code is in
+% k_order_perturbation is a compiled MEX function. Its source code is in
% dynare/mex/sources/k_order_perturbation.cc and it uses code provided by
% dynare++
-% Copyright (C) 2013-2020 Dynare Team
+% Copyright (C) 2013-2021 Dynare Team
%
% This file is part of Dynare.
%
diff --git a/matlab/stochastic_solvers.m b/matlab/stochastic_solvers.m
index 85398fa94..6759a3b87 100644
--- a/matlab/stochastic_solvers.m
+++ b/matlab/stochastic_solvers.m
@@ -24,7 +24,7 @@ function [dr, info] = stochastic_solvers(dr, task, M_, options_, oo_)
% info=6 -> The jacobian matrix evaluated at the steady state is complex.
% info=9 -> k_order_pert was unable to compute the solution
-% Copyright (C) 1996-2020 Dynare Team
+% Copyright (C) 1996-2021 Dynare Team
%
% This file is part of Dynare.
%
@@ -84,16 +84,11 @@ if M_.maximum_endo_lead==0 && M_.exo_det_nbr~=0
end
if options_.k_order_solver
- if options_.risky_steadystate
- [dr,info] = dyn_risky_steadystate_solver(oo_.steady_state,M_,dr, ...
- options_,oo_);
- else
- orig_order = options_.order;
- options_.order = local_order;
- dr = set_state_space(dr,M_,options_);
- [dr,info] = k_order_pert(dr,M_,options_);
- options_.order = orig_order;
- end
+ orig_order = options_.order;
+ options_.order = local_order;
+ dr = set_state_space(dr,M_,options_);
+ [dr,info] = k_order_pert(dr,M_,options_);
+ options_.order = orig_order;
return
end
@@ -242,12 +237,6 @@ if M_.maximum_endo_lead == 0
error(['2nd and 3rd order approximation not implemented for purely ' ...
'backward models'])
end
-elseif options_.risky_steadystate
- orig_order = options_.order;
- options_.order = local_order;
- [dr,info] = dyn_risky_steadystate_solver(oo_.steady_state,M_,dr, ...
- options_,oo_);
- options_.order = orig_order;
else
% If required, use AIM solver if not check only
if options_.aim_solver && (task == 0)
diff --git a/mex/sources/k_order_perturbation/k_ord_dynare.cc b/mex/sources/k_order_perturbation/k_ord_dynare.cc
index 5ccd442fb..bb25138c1 100644
--- a/mex/sources/k_order_perturbation/k_ord_dynare.cc
+++ b/mex/sources/k_order_perturbation/k_ord_dynare.cc
@@ -1,5 +1,5 @@
/*
- * Copyright © 2008-2019 Dynare Team
+ * Copyright © 2008-2021 Dynare Team
*
* This file is part of Dynare.
*
@@ -24,6 +24,7 @@
#include "dynare_exception.hh"
#include
+#include
KordpDynare::KordpDynare(const std::vector &endo,
const std::vector &exo, int nexog, int npar,
@@ -68,35 +69,36 @@ KordpDynare::evaluateSystem(Vector &out, const ConstVector &yym, const ConstVect
void
KordpDynare::calcDerivativesAtSteady()
{
- if (dyn_md.empty())
+ assert(md.begin() == md.end());
+
+ std::vector dyn_md; // Model derivatives, in Dynare form
+
+ dyn_md.emplace_back(nY, nJcols); // Allocate Jacobian
+ dyn_md.back().zeros();
+
+ for (int i = 2; i <= nOrder; i++)
{
- dyn_md.emplace_back(nY, nJcols); // Allocate Jacobian
+ // Higher order derivatives, as sparse (3-column) matrices
+ dyn_md.emplace_back(static_cast(NNZD[i-1]), 3);
dyn_md.back().zeros();
-
- for (int i = 2; i <= nOrder; i++)
- {
- // Higher order derivatives, as sparse (3-column) matrices
- dyn_md.emplace_back(static_cast(NNZD[i-1]), 3);
- dyn_md.back().zeros();
- }
-
- Vector xx(nexog());
- xx.zeros();
-
- Vector out(nY);
- out.zeros();
- Vector llxSteady(nJcols-nExog);
- LLxSteady(ySteady, llxSteady);
-
- dynamicModelFile->eval(llxSteady, xx, params, ySteady, out, dyn_md);
}
+ Vector xx(nexog());
+ xx.zeros();
+
+ Vector out(nY);
+ out.zeros();
+ Vector llxSteady(nJcols-nExog);
+ LLxSteady(ySteady, llxSteady);
+
+ dynamicModelFile->eval(llxSteady, xx, params, ySteady, out, dyn_md);
+
for (int i = 1; i <= nOrder; i++)
- populateDerivativesContainer(i);
+ populateDerivativesContainer(dyn_md, i);
}
void
-KordpDynare::populateDerivativesContainer(int ord)
+KordpDynare::populateDerivativesContainer(const std::vector &dyn_md, int ord)
{
const TwoDMatrix &g = dyn_md[ord-1];
@@ -217,12 +219,6 @@ KordpDynare::computeJacobianPermutation(const std::vector &dr_order)
dynToDynpp[dynppToDyn[i]] = i;
}
-void
-KordpDynare::push_back_md(const mxArray *m)
-{
- dyn_md.emplace_back(ConstTwoDMatrix{m});
-}
-
DynareNameList::DynareNameList(std::vector names_arg)
: names(std::move(names_arg))
{
diff --git a/mex/sources/k_order_perturbation/k_ord_dynare.hh b/mex/sources/k_order_perturbation/k_ord_dynare.hh
index 6154fc16a..09c1aa525 100644
--- a/mex/sources/k_order_perturbation/k_ord_dynare.hh
+++ b/mex/sources/k_order_perturbation/k_ord_dynare.hh
@@ -1,5 +1,5 @@
/*
- * Copyright © 2008-2019 Dynare Team
+ * Copyright © 2008-2021 Dynare Team
*
* This file is part of Dynare.
*
@@ -95,7 +95,6 @@ private:
Vector &ySteady;
Vector ¶ms;
TwoDMatrix &vCov;
- std::vector dyn_md; // Model derivatives, in Dynare form
TensorContainer md; // Model derivatives, in Dynare++ form
DynareNameList dnl, denl;
DynareStateNameList dsnl;
@@ -191,8 +190,6 @@ public:
std::cerr << "KordpDynare::clone() not implemented" << std::endl;
exit(EXIT_FAILURE);
}
- // Add model derivatives of a given order passed as argument to the MEX
- void push_back_md(const mxArray *m);
private:
// Given the steady state in yS, returns in llxSteady the steady state extended with leads and lags
void LLxSteady(const Vector &yS, Vector &llxSteady);
@@ -200,7 +197,7 @@ private:
Dynare++ orderings of variables */
void computeJacobianPermutation(const std::vector &var_order);
// Fills model derivatives in Dynare++ form (at a given order) given the Dynare form
- void populateDerivativesContainer(int ord);
+ void populateDerivativesContainer(const std::vector &dyn_md, int ord);
};
#endif
diff --git a/mex/sources/k_order_perturbation/k_order_perturbation.cc b/mex/sources/k_order_perturbation/k_order_perturbation.cc
index c091db339..25c4885f9 100644
--- a/mex/sources/k_order_perturbation/k_order_perturbation.cc
+++ b/mex/sources/k_order_perturbation/k_order_perturbation.cc
@@ -1,5 +1,5 @@
/*
- * Copyright © 2008-2020 Dynare Team
+ * Copyright © 2008-2021 Dynare Team
*
* This file is part of Dynare.
*
@@ -82,8 +82,8 @@ extern "C" {
mexFunction(int nlhs, mxArray *plhs[],
int nrhs, const mxArray *prhs[])
{
- if (nrhs < 3 || nlhs < 1 || nlhs > 2)
- mexErrMsgTxt("Must have at least 3 input parameters and takes 1 or 2 output parameters.");
+ if (nrhs != 3 || nlhs < 1 || nlhs > 2)
+ mexErrMsgTxt("Must have exactly 3 input parameters and takes 1 or 2 output parameters.");
// Give explicit names to input arguments
const mxArray *dr_mx = prhs[0];
@@ -225,16 +225,6 @@ extern "C" {
NNZD, nSteps, kOrder, journal, std::move(dynamicModelFile),
dr_order, llincidence);
- // If model derivatives have been passed as arguments
- if (nrhs > 3)
- {
- dynare.push_back_md(prhs[3]);
- if (nrhs > 4)
- dynare.push_back_md(prhs[4]);
- if (nrhs > 5)
- dynare.push_back_md(prhs[5]);
- }
-
// construct main K-order approximation class
Approximation app(dynare, journal, nSteps, false, qz_criterium);
// run stochastic steady