* Bug fixes.
* Removed local copy of endo_simul. * Cosmetic changes. git-svn-id: https://www.dynare.org/svn/dynare/trunk@3293 ac1d8469-bf42-47a9-8791-bf33cf982152time-shift
stepan
parent
e6a1916577
commit
ec6c70443a
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@ -31,14 +31,14 @@ function time_series = extended_path(initial_conditions,sample_size,init)
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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global M_ oo_ options_
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% Set default initial conditions.
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if isempty(initial_conditions)
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initial_conditions = repmat(oo_.steady_state,1,M_.maximum_lag);
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end
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% Copy sample_size to periods.
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options_.periods = sample_size;
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% Set the number of periods for the deterministic solver.
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options_.periods = 100;
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% Initialize the exogenous variables.
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make_ex_;
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@ -60,8 +60,8 @@ tdx = M_.maximum_lag+1;
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norme = 0;
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% Set verbose option
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verbose = 0;
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verbose = 1;
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for t=1:sample_size
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shocks = exp(randn(1,number_of_structural_innovations)*covariance_matrix_upper_cholesky-.5*variances(positive_var_indx)');
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oo_.exo_simul(tdx,positive_var_indx) = shocks;
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@ -72,7 +72,7 @@ for t=1:sample_size
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info
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end
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if ~info.convergence
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info = homotopic_steps(tdx,positive_var_indx,shocks,norme,.2);
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info = homotopic_steps(tdx,positive_var_indx,shocks,norme,.5);
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if verbose
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norme
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info
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@ -88,49 +88,50 @@ for t=1:sample_size
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oo_.endo_simul(:,1:end-1) = oo_.endo_simul(:,2:end);
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oo_.endo_simul(:,end) = oo_.steady_state;
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end
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function info = homotopic_steps(tdx,positive_var_indx,shocks,init_weight,step)
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global oo_
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weight = init_weight;
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verbose = 0;
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iter = 0;
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time = 0;
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reduce_step = 0;
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while iter<=100 && weight<=1
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iter = iter+1;
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old_weight = weight;
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weight = weight+step;
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oo_.exo_simul(tdx,positive_var_indx) = weight*shocks+(1-weight);
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info = perfect_foresight_simulation;
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time = time+info.time;
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if verbose
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[iter,step]
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[info.iterations.time,info.iterations.error]
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end
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if ~info.convergence
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global oo_
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weight = init_weight;
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verbose = 1;
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iter = 0;
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time = 0;
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reduce_step = 0;
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while iter<=100 && weight<=1
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iter = iter+1;
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old_weight = weight;
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weight = weight+step;
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oo_.exo_simul(tdx,positive_var_indx) = weight*shocks+(1-weight);
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info = perfect_foresight_simulation;
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time = time+info.time;
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if verbose
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disp('Reduce step size!')
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[iter,step]
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[info.iterations.time,info.iterations.error]
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end
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reduce_step = 1;
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break
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else
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if length(info.iterations.error)<5
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if ~info.convergence
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if verbose
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disp('Increase step size!')
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disp('Reduce step size!')
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end
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reduce_step = 1;
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break
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else
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if length(info.iterations.error)<5
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if verbose
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disp('Increase step size!')
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end
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step = step*1.5;
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end
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step = step*1.5;
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end
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end
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end
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if reduce_step
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step=step/1.5;
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info = homotopic_steps(tdx,positive_var_indx,shocks,old_weight,step);
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time = time+info.time;
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elseif weight<1 && iter<100
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oo_.exo_simul(tdx,positive_var_indx) = shocks;
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info = perfect_foresight_simulation;
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info.time = info.time+time;
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else
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info.time = time;
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end
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if reduce_step
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step=step/1.5;
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info = homotopic_steps(tdx,positive_var_indx,shocks,old_weight,step);
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time = time+info.time;
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elseif weight<1 && iter<100
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oo_.exo_simul(tdx,positive_var_indx) = shocks;
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info = perfect_foresight_simulation;
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info.time = info.time+time;
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else
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info.time = time;
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end
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@ -1,4 +1,4 @@
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function [info,endo_simul] = perfect_foresight_simulation(endo_simul,exo_simul,compute_linear_solution,steady_state)
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function info = perfect_foresight_simulation(compute_linear_solution,steady_state)
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% Performs deterministic simulations with lead or lag on one period
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%
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% INPUTS
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@ -18,7 +18,7 @@ function [info,endo_simul] = perfect_foresight_simulation(endo_simul,exo_simul,c
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% SPECIAL REQUIREMENTS
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% None.
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% Copyright (C) 1996-2009 Dynare Team
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% Copyright (C) 2009 Dynare Team
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%
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% This file is part of Dynare.
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%
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@ -34,17 +34,19 @@ function [info,endo_simul] = perfect_foresight_simulation(endo_simul,exo_simul,c
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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global M_ options_ it_
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global M_ options_ it_ oo_
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persistent flag_init
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persistent lead_lag_incidence dynamic_model ny nyp nyf nrs nrc iyf iyp isp is isf isf1 iz icf
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persistent ghx
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initial_endo_simul = oo_.endo_simul;
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if isempty(flag_init)
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lead_lag_incidence = M_.lead_lag_incidence;
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dynamic_model = [M_.fname '_dynamic'];
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ny = size(endo_simul,1);
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ny = size(oo_.endo_simul,1);
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nyp = nnz(lead_lag_incidence(1,:));% number of lagged variables.
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nyf = nnz(lead_lag_incidence(3,:));% number of leaded variables.
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nrs = ny+nyp+nyf+1;
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@ -60,10 +62,11 @@ if isempty(flag_init)
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flag_init = 1;
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end
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if nargin<3
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if nargin<1
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compute_linear_solution = 0;
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if nargin<4
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error('The steady state (fourth input argument) is missing!');
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else
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if nargin<2
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error('The steady state (second input argument) is missing!');
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end
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end
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@ -95,16 +98,16 @@ for iter = 1:options_.maxit_
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c = zeros(ny*periods,nrc);
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end
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it_ = it_init;
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z = [ endo_simul(iyp,it_-1) ; endo_simul(:,it_) ; endo_simul(iyf,it_+1) ];
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[d1,jacobian] = feval(dynamic_model,z,exo_simul, M_.params, it_);
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z = [ oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1) ];
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[d1,jacobian] = feval(dynamic_model,z,oo_.exo_simul, M_.params, it_);
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jacobian = [jacobian(:,iz) , -d1];
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ic = 1:ny;
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icp = iyp;
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c(ic,:) = jacobian(:,is)\jacobian(:,isf1) ;
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for it_ = it_init+(1:periods-1)
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z = [ endo_simul(iyp,it_-1) ; endo_simul(:,it_) ; endo_simul(iyf,it_+1)];
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[d1,jacobian] = feval(dynamic_model,z,exo_simul, M_.params, it_);
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jacobian = [jacobian(:,iz) , -d1];
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z = [ oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1)];
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[d1,jacobian] = feval(dynamic_model,z,oo_.exo_simul, M_.params, it_);
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jacobian = [jacobian(:,iz) , -d1];
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jacobian(:,[isf nrs]) = jacobian(:,[isf nrs])-jacobian(:,isp)*c(icp,:);
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ic = ic + ny;
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icp = icp + ny;
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@ -116,14 +119,18 @@ for iter = 1:options_.maxit_
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s(:,isf) = s(:,isf)+c(ic,1:nyf);
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ic = ic + ny;
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c(ic,nrc) = s\c(ic,nrc);
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else% Terminal condition is Y_{T}-Y^{\star} = TransitionMatrix*(Y_{T+1}-Y^{\star})
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z = [ endo_simul(iyp,it_-1) ; endo_simul(:,it_) ; endo_simul(iyf,it_+1) ] ;
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[d1,jacobian] = feval(dynamic_model,z,exo_simul, M_.params, it_);
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else% Terminal condition is Y_{T+1}-Y^{\star} = TransitionMatrix*(Y_{T}-Y^{\star})
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z = [ oo_.endo_simul(iyp,it_-1) ; oo_.endo_simul(:,it_) ; oo_.endo_simul(iyf,it_+1) ] ;
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[d1,jacobian] = feval(dynamic_model,z,oo_.exo_simul, M_.params, it_);
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jacobian = [jacobian(:,iz) -d1];
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jacobian(:,[isf nrs]) = jacobian(:,[isf nrs])-jacobian(:,isp)*c(icp,:) ;
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ic = ic + ny;
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icp = icp + ny;
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s = jacobian(:,is);
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iyp
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nyp
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isf
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iyp-nyp
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s(:,iyp-nyp) = s(:,iyp-nyp)+jacobian(:,isf)*ghx;
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c (ic,:) = s\jacobian(:,isf1);
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end
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@ -133,18 +140,18 @@ for iter = 1:options_.maxit_
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else% Terminal condition is Y_{T}=Y^{\star}
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c = bksup0(c,ny,nrc,iyf,icf,periods);
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c = reshape(c,ny,periods);
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endo_simul(:,it_init+(0:periods-1)) = endo_simul(:,it_init+(0:periods-1))+options_.slowc*c;
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oo_.endo_simul(:,it_init+(0:periods-1)) = oo_.endo_simul(:,it_init+(0:periods-1))+options_.slowc*c;
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end
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err = max(max(abs(c)));
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info.iterations.time(iter) = etime(clock,h2);
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info.iterations.error(iter) = err;
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% if iter>1
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% error_growth = error_growth + (info.iterations.error(iter)>info.iterations.error(iter-1));
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% end
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% if isnan(err) || error_growth>3
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% last_line = iter;
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% break
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% end
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if iter>1
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error_growth = error_growth + (info.iterations.error(iter)>info.iterations.error(iter-1));
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end
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if isnan(err) || error_growth>3
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last_line = iter;
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break
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end
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if err < options_.dynatol
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stop = 1;
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info.time = etime(clock,h1);
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@ -156,7 +163,7 @@ for iter = 1:options_.maxit_
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end
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if options_.terminal_condition==2
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distance_to_steady_state = abs(((endo_simul(:,end-1)-endo_simul(:,end))./endo_simul(:,end)))*100;
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distance_to_steady_state = abs(((oo_.endo_simul(:,end-1)-oo_.endo_simul(:,end))./oo_.endo_simul(:,end)))*100;
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disp('Distance to steady state at the end is (in percentage):')
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distance_to_steady_state
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end
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@ -167,6 +174,5 @@ if ~stop
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info.convergence = 0;
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info.iterations.time = info.iterations.time(1:last_line);
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info.iterations.error = info.iterations.error(1:last_line);
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info.iterations.error
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endo_simul = [ ];
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oo_.endo_simul = initial_endo_simul;
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end
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