Make sure that no variable is named hessian.
parent
450520e946
commit
e76f81740f
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@ -1,7 +1,7 @@
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function dr = dyn_second_order_solver(jacobia,hessian,dr,M_,threads_ABC,threads_BC)
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function dr = dyn_second_order_solver(jacobia,hessian_mat,dr,M_,threads_ABC,threads_BC)
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%@info:
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%! @deftypefn {Function File} {@var{dr} =} dyn_second_order_solver (@var{jacobia},@var{hessian},@var{dr},@var{M_},@var{threads_ABC},@var{threads_BC})
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%! @deftypefn {Function File} {@var{dr} =} dyn_second_order_solver (@var{jacobia},@var{hessian_mat},@var{dr},@var{M_},@var{threads_ABC},@var{threads_BC})
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%! @anchor{dyn_second_order_solver}
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%! @sp 1
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%! Computes the second order reduced form of the DSGE model
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@ -11,7 +11,7 @@ function dr = dyn_second_order_solver(jacobia,hessian,dr,M_,threads_ABC,threads_
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%! @table @ @var
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%! @item jacobia
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%! Matrix containing the Jacobian of the model
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%! @item hessian
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%! @item hessian_mat
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%! Matrix containing the second order derivatives of the model
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%! @item dr
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%! Matlab's structure describing the reduced form solution of the model.
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@ -73,7 +73,7 @@ function dr = dyn_second_order_solver(jacobia,hessian,dr,M_,threads_ABC,threads_
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kk1 = reshape([1:nk^2],nk,nk);
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kk1 = kk1(kk,kk);
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% reordering second order derivatives
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hessian = hessian(:,kk1(:));
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hessian_mat = hessian_mat(:,kk1(:));
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zx = zeros(np,np);
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zu=zeros(np,M_.exo_nbr);
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@ -91,7 +91,7 @@ function dr = dyn_second_order_solver(jacobia,hessian,dr,M_,threads_ABC,threads_
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zu=[zu; eye(M_.exo_nbr);zeros(M_.exo_det_nbr,M_.exo_nbr)];
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[nrzx,nczx] = size(zx);
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[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian,zx,threads_BC);
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[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zx,threads_BC);
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mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
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rhs = -rhs;
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@ -118,7 +118,7 @@ function dr = dyn_second_order_solver(jacobia,hessian,dr,M_,threads_ABC,threads_
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%ghxu
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%rhs
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hu = dr.ghu(nstatic+1:nstatic+nspred,:);
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[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian,zx,zu,threads_BC);
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[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zx,zu,threads_BC);
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mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
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hu1 = [hu;zeros(np-nspred,M_.exo_nbr)];
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@ -136,7 +136,7 @@ function dr = dyn_second_order_solver(jacobia,hessian,dr,M_,threads_ABC,threads_
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%ghuu
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%rhs
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[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian,zu,threads_BC);
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[rhs, err] = sparse_hessian_times_B_kronecker_C(hessian_mat,zu,threads_BC);
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mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
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[B1, err] = A_times_B_kronecker_C(B*dr.ghxx,hu1,threads_ABC);
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@ -164,7 +164,7 @@ function dr = dyn_second_order_solver(jacobia,hessian,dr,M_,threads_ABC,threads_
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hxx = dr.ghxx(nstatic+[1:nspred],:);
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[junk,k2a,k2] = find(M_.lead_lag_incidence(M_.maximum_endo_lag+2,order_var));
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k3 = nnz(M_.lead_lag_incidence(1:M_.maximum_endo_lag+1,:))+(1:M_.nsfwrd)';
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[B1, err] = sparse_hessian_times_B_kronecker_C(hessian(:,kh(k3,k3)),gu(k2a,:),threads_BC);
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[B1, err] = sparse_hessian_times_B_kronecker_C(hessian_mat(:,kh(k3,k3)),gu(k2a,:),threads_BC);
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mexErrCheck('sparse_hessian_times_B_kronecker_C', err);
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RHS = RHS + jacobia(:,k2)*guu(k2a,:)+B1;
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@ -1,4 +1,4 @@
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function [xparams,lpd,hessian] = ...
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function [xparams,lpd,hessian_mat] = ...
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maximize_prior_density(iparams, prior_shape, prior_hyperparameter_1, prior_hyperparameter_2, prior_inf_bound, prior_sup_bound,DynareOptions,DynareModel,BayesInfo,EstimatedParams,DynareResults)
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% Maximizes the logged prior density using Chris Sims' optimization routine.
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%
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@ -13,7 +13,7 @@ function [xparams,lpd,hessian] = ...
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% OUTPUTS
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% xparams [double] vector, prior mode.
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% lpd [double] scalar, value of the logged prior density at the mode.
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% hessian [double] matrix, Hessian matrix at the prior mode.
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% hessian_mat [double] matrix, Hessian matrix at the prior mode.
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% Copyright (C) 2009-2015 Dynare Team
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%
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@ -32,7 +32,7 @@ function [xparams,lpd,hessian] = ...
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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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[xparams, lpd, exitflag, hessian]=dynare_minimize_objective('minus_logged_prior_density', ...
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[xparams, lpd, exitflag, hessian_mat]=dynare_minimize_objective('minus_logged_prior_density', ...
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iparams, DynareOptions.mode_compute, DynareOptions, [prior_inf_bound, prior_sup_bound], ...
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BayesInfo.name, BayesInfo, [], ...
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prior_shape, prior_hyperparameter_1, prior_hyperparameter_2, prior_inf_bound, prior_sup_bound, ...
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@ -1,8 +1,8 @@
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function mode_check(fun,x,hessian,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
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function mode_check(fun,x,hessian_mat,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults)
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% Checks the estimated ML mode or Posterior mode.
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%@info:
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%! @deftypefn {Function File} mode_check (@var{fun}, @var{x}, @var{hessian}, @var{DynareDataset}, @var{DynareOptions}, @var{Model}, @var{EstimatedParameters}, @var{BayesInfo}, @var{DynareResults})
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%! @deftypefn {Function File} mode_check (@var{fun}, @var{x}, @var{hessian_mat}, @var{DynareDataset}, @var{DynareOptions}, @var{Model}, @var{EstimatedParameters}, @var{BayesInfo}, @var{DynareResults})
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%! @anchor{mode_check}
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%! @sp 1
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%! Checks the estimated ML mode or Posterior mode by plotting sections of the likelihood/posterior kernel.
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@ -58,13 +58,13 @@ function mode_check(fun,x,hessian,DynareDataset,DatasetInfo,DynareOptions,Model,
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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TeX = DynareOptions.TeX;
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if ~isempty(hessian);
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[ s_min, k ] = min(diag(hessian));
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if ~isempty(hessian_mat);
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[ s_min, k ] = min(diag(hessian_mat));
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end
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fval = feval(fun,x,DynareDataset,DatasetInfo,DynareOptions,Model,EstimatedParameters,BayesInfo,BoundsInfo,DynareResults);
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if ~isempty(hessian);
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if ~isempty(hessian_mat);
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skipline()
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disp('MODE CHECK')
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skipline()
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@ -51,7 +51,7 @@ objective_function_penalty_base = minus_logged_prior_density(xinit, BayesInfo.ps
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BayesInfo.p4,DynareOptions,ModelInfo,EstimationInfo,DynareResults);
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% Maximization of the prior density
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[xparams, lpd, hessian] = ...
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[xparams, lpd, hessian_mat] = ...
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maximize_prior_density(xinit, BayesInfo.pshape, ...
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BayesInfo.p6, ...
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BayesInfo.p7, ...
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