diff --git a/matlab/dynare_solve.m b/matlab/dynare_solve.m
index 31868ac4b..eff38e5a7 100644
--- a/matlab/dynare_solve.m
+++ b/matlab/dynare_solve.m
@@ -76,7 +76,9 @@ if ~isempty(i)
return;
end
-if max(abs(fvec)) < tolf
+% this test doesn't check complementarity conditions and is not used for
+% mixed complementarity problems
+if (options.solve_algo ~= 10) && (max(abs(fvec)) < tolf)
return ;
end
diff --git a/matlab/ep/ep_notes.org b/matlab/ep/ep_notes.org
new file mode 100644
index 000000000..0d1f30814
--- /dev/null
+++ b/matlab/ep/ep_notes.org
@@ -0,0 +1,35 @@
+
+ debug: 0
+ memory: 0
+ verbosity: 0
+ use_bytecode: 0
+ init: 0
+ maxit: 500
+ periods: 200
+ step: 50
+ check_stability: 0
+ lp: 5
+ fp: 2
+ innovation_distribution: 'gaussian'
+ set_dynare_seed_to_default: 1
+ stack_solve_algo: 4
+ stochastic: [1x1 struct]
+ IntegrationAlgorithm: 'Tensor-Gaussian-Quadrature'
+
+stochastic:
+ method: ''
+ algo: 0
+ quadrature: [1x1 struct]
+ order: 1
+ hybrid_order: 0
+ homotopic_steps: 1
+ nodes: 3
+quadrature:
+ ortpol: 'hermite'
+ nodes: 5
+ pruned: [1x1 struct]
+
+pruned:
+ ortpol: 'hermite'
+ nodes: 5
+ pruned: [1x1 struct]
diff --git a/matlab/ep/extended_path.m b/matlab/ep/extended_path.m
index 51b67fea6..169e9efb2 100644
--- a/matlab/ep/extended_path.m
+++ b/matlab/ep/extended_path.m
@@ -1,4 +1,4 @@
-function ts = extended_path(initial_conditions,sample_size)
+function [ts,results] = extended_path(initial_conditions,sample_size)
% Stochastic simulation of a non linear DSGE model using the Extended Path method (Fair and Taylor 1983). A time
% series of size T is obtained by solving T perfect foresight models.
%
@@ -30,26 +30,30 @@ function ts = extended_path(initial_conditions,sample_size)
%
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see .
-global M_ options_ oo_
+ global M_ options_ oo_
-options_.verbosity = options_.ep.verbosity;
-verbosity = options_.ep.verbosity+options_.ep.debug;
+ep = options_.ep;
+options_.verbosity = ep.verbosity;
+verbosity = ep.verbosity+ep.debug;
% Set maximum number of iterations for the deterministic solver.
-options_.simul.maxit = options_.ep.maxit;
+options_.simul.maxit = ep.maxit;
% Prepare a structure needed by the matlab implementation of the perfect foresight model solver
pfm = setup_stochastic_perfect_foresight_model_solver(M_,options_,oo_);
+endo_nbr = M_.endo_nbr;
exo_nbr = M_.exo_nbr;
-ep = options_.ep;
+maximum_lag = M_.maximum_lag;
+maximum_lead = M_.maximum_lead;
+epreplic_nbr = ep.replic_nbr;
steady_state = oo_.steady_state;
dynatol = options_.dynatol;
% Set default initial conditions.
if isempty(initial_conditions)
if isempty(M_.endo_histval)
- initial_conditions = oo_.steady_state;
+ initial_conditions = steady_state;
else
initial_conditions = M_.endo_histval;
end
@@ -57,18 +61,19 @@ end
% Set the number of periods for the perfect foresight model
-periods = options_.ep.periods;
+periods = ep.periods;
pfm.periods = periods;
pfm.i_upd = pfm.ny+(1:pfm.periods*pfm.ny);
+pfm.block = options_.block;
% keep a copy of pfm.i_upd
i_upd = pfm.i_upd;
% Set the algorithm for the perfect foresight solver
-options_.stack_solve_algo = options_.ep.stack_solve_algo;
+options_.stack_solve_algo = ep.stack_solve_algo;
% Set check_stability flag
-do_not_check_stability_flag = ~options_.ep.check_stability;
+do_not_check_stability_flag = ~ep.check_stability;
% Compute the first order reduced form if needed.
%
@@ -76,22 +81,15 @@ do_not_check_stability_flag = ~options_.ep.check_stability;
% all the globals in a mat file called linear_reduced_form.mat;
dr = struct();
-if options_.ep.init
+if ep.init
options_.order = 1;
- [dr,Info,M_,options_,oo_] = resol(1,M_,options_,oo_);
+ oo_.dr=set_state_space(dr,M_,options_);
+ [dr,Info,M_,options_,oo_] = resol(0,M_,options_,oo_);
end
% Do not use a minimal number of perdiods for the perfect foresight solver (with bytecode and blocks)
options_.minimal_solving_period = 100;%options_.ep.periods;
-% Initialize the exogenous variables.
-% !!!!!!!! Needs to fixed
-options_.periods = periods;
-make_ex_;
-
-% Initialize the endogenous variables.
-make_y_;
-
% Initialize the output array.
time_series = zeros(M_.endo_nbr,sample_size);
@@ -107,19 +105,37 @@ covariance_matrix_upper_cholesky = chol(covariance_matrix);
%exo_nbr = effective_number_of_shocks;
% Set seed.
-if options_.ep.set_dynare_seed_to_default
+if ep.set_dynare_seed_to_default
set_dynare_seed('default');
end
% Set bytecode flag
-bytecode_flag = options_.ep.use_bytecode;
+bytecode_flag = ep.use_bytecode;
+% Set number of replications
+replic_nbr = ep.replic_nbr;
% Simulate shocks.
-switch options_.ep.innovation_distribution
+switch ep.innovation_distribution
case 'gaussian'
- oo_.ep.shocks = transpose(transpose(covariance_matrix_upper_cholesky)*randn(effective_number_of_shocks,sample_size));
+ shocks = transpose(transpose(covariance_matrix_upper_cholesky)* ...
+ randn(effective_number_of_shocks,sample_size* ...
+ replic_nbr));
+ shocks(:,positive_var_indx) = shocks;
+ case 'calibrated'
+ replic_nbr = 1;
+ shocks = zeros(sample_size,M_.exo_nbr);
+ for i = 1:length(M_.unanticipated_det_shocks)
+ k = M_.unanticipated_det_shocks(i).periods;
+ ivar = M_.unanticipated_det_shocks(i).exo_id;
+ v = M_.unanticipated_det_shocks(i).value;
+ if ~M_.unanticipated_det_shocks(i).multiplicative
+ shocks(k,ivar) = v;
+ else
+ socks(k,ivar) = shocks(k,ivar) * v;
+ end
+ end
otherwise
- error(['extended_path:: ' options_.ep.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
+ error(['extended_path:: ' ep.innovation_distribution ' distribution for the structural innovations is not (yet) implemented!'])
end
@@ -128,7 +144,7 @@ hh = dyn_waitbar(0,'Please wait. Extended Path simulations...');
set(hh,'Name','EP simulations.');
% hybrid correction
-pfm.hybrid_order = options_.ep.stochastic.hybrid_order;
+pfm.hybrid_order = ep.stochastic.hybrid_order;
if pfm.hybrid_order
oo_.dr = set_state_space(oo_.dr,M_,options_);
options = options_;
@@ -142,16 +158,16 @@ end
pfm.nnzA = M_.NNZDerivatives(1);
% setting up integration nodes if order > 0
-if options_.ep.stochastic.order > 0
+if ep.stochastic.order > 0
[nodes,weights,nnodes] = setup_integration_nodes(options_.ep,pfm);
pfm.nodes = nodes;
pfm.weights = weights;
pfm.nnodes = nnodes;
% compute number of blocks
- [block_nbr,pfm.world_nbr] = get_block_world_nbr(options_.ep.stochastic.algo,nnodes,options_.ep.stochastic.order,options_.ep.periods);
+ [block_nbr,pfm.world_nbr] = get_block_world_nbr(ep.stochastic.algo,nnodes,ep.stochastic.order,ep.periods);
else
- block_nbr = options_.ep.periods
+ block_nbr = ep.periods;
end
@@ -167,74 +183,56 @@ oo_.ep.failures.previous_period = cell(0);
oo_.ep.failures.shocks = cell(0);
% Initializes some variables.
-t = 0;
+t = 1;
tsimul = 1;
+for k = 1:replic_nbr
+ results{k} = zeros(endo_nbr,sample_size+1);
+ results{k}(:,1) = initial_conditions;
+end
+make_ex_;
+exo_simul_ = zeros(maximum_lag+sample_size+maximum_lead,exo_nbr);
+exo_simul_(1:size(oo_.exo_simul,1),1:size(oo_.exo_simul,2)) = oo_.exo_simul;
% Main loop.
-while (t 1 && ep.parallel_1
+ parfor k = 1:replic_nbr
+ exo_simul = repmat(oo_.exo_steady_state',periods+2,1);
+ % exo_simul(1:sample_size+3-t,:) = exo_simul_(t:end,:);
+ exo_simul(2,:) = exo_simul_(M_.maximum_lag+t,:) + ...
+ shocks((t-2)*replic_nbr+k,:);
+ initial_conditions = results{k}(:,t-1);
+ results{k}(:,t) = extended_path_core(ep.periods,endo_nbr,exo_nbr,positive_var_indx, ...
+ exo_simul,ep.init,initial_conditions,...
+ maximum_lag,maximum_lead,steady_state, ...
+ ep.verbosity,bytecode_flag,ep.stochastic.order,...
+ M_.params,pfm,ep.stochastic.algo,ep.stock_solve_algo,...
+ options_.lmmcp,options_,oo_);
+ end
else
- if t==1
- endo_simul_1 = repmat(steady_state,1,periods+2);
+ for k = 1:replic_nbr
+ exo_simul = repmat(oo_.exo_steady_state',periods+maximum_lag+ ...
+ maximum_lead,1);
+ % exo_simul(1:sample_size+maximum_lag+maximum_lead-t+1,:) = ...
+ % exo_simul_(t:end,:);
+ exo_simul(maximum_lag+1,:) = ...
+ exo_simul_(maximum_lag+t,:) + shocks((t-2)*replic_nbr+k,:);
+ initial_conditions = results{k}(:,t-1);
+ results{k}(:,t) = extended_path_core(ep.periods,endo_nbr,exo_nbr,positive_var_indx, ...
+ exo_simul,ep.init,initial_conditions,...
+ maximum_lag,maximum_lead,steady_state, ...
+ ep.verbosity,bytecode_flag,ep.stochastic.order,...
+ M_,pfm,ep.stochastic.algo,ep.stack_solve_algo,...
+ options_.lmmcp,options_,oo_);
end
end
- % Solve a perfect foresight model.
- % Keep a copy of endo_simul_1
- endo_simul = endo_simul_1;
- if verbosity
- save ep_test_1 endo_simul_1 exo_simul_1
- end
- if bytecode_flag && ~options_.ep.stochastic.order
- [flag,tmp] = bytecode('dynamic',endo_simul_1,exo_simul_1, M_.params, endo_simul_1, options_.ep.periods);
- else
- flag = 1;
- end
- if flag
- if options_.ep.stochastic.order == 0
- [flag,tmp,err] = solve_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm);
- else
- switch(options_.ep.stochastic.algo)
- case 0
- [flag,tmp] = ...
- solve_stochastic_perfect_foresight_model(endo_simul_1,exo_simul_1,pfm,options_.ep.stochastic.quadrature.nodes,options_.ep.stochastic.order);
- case 1
- [flag,tmp] = ...
- solve_stochastic_perfect_foresight_model_1(endo_simul_1,exo_simul_1,options_,pfm,options_.ep.stochastic.order);
- end
- end
- end
- info_convergence = ~flag;
- if verbosity
- if info_convergence
- disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
- else
- disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
- end
- end
- endo_simul_1 = tmp;
- if info_convergence
- % Save results of the perfect foresight model solver.
- time_series(:,tsimul) = endo_simul_1(:,2);
- endo_simul_1(:,1:end-1) = endo_simul_1(:,2:end);
- endo_simul_1(:,1) = time_series(:,tsimul);
- endo_simul_1(:,end) = oo_.steady_state;
- tsimul = tsimul+1;
- else
- oo_.ep.failures.periods = [oo_.ep.failures.periods t];
- oo_.ep.failures.previous_period = [oo_.ep.failures.previous_period endo_simul_1(:,1)];
- oo_.ep.failures.shocks = [oo_.ep.failures.shocks shocks];
- endo_simul_1 = repmat(steady_state,1,periods+2);
- endo_simul_1(:,1) = time_series(:,tsimul-1);
- end
+
+
end% (while) loop over t
dyn_waitbar_close(hh);
@@ -242,14 +240,105 @@ dyn_waitbar_close(hh);
if isnan(options_.initial_period)
initial_period = dates(1,1);
else
- initial_period = optins_.initial_period;
+ initial_period = options_.initial_period;
end
if nargout
- ts = dseries(transpose([initial_conditions, time_series]),initial_period,cellstr(M_.endo_names));
+ if ~isnan(results{1})
+ ts = dseries(transpose([results{1}]), ...
+ initial_period,cellstr(M_.endo_names));
+ else
+ ts = NaN;
+ end
else
- oo_.endo_simul = [initial_conditions, time_series];
- ts = dseries(transpose(oo_.endo_simul),initial_period,cellstr(M_.endo_names));
- dyn2vec;
+ if ~isnan(results{1})
+ oo_.endo_simul = results{1};
+ ts = dseries(transpose(results{1}),initial_period, ...
+ cellstr(M_.endo_names));
+ else
+ oo_.endo_simul = NaN;
+ ts = NaN;
+ end
end
assignin('base', 'Simulated_time_series', ts);
+
+
+function y = extended_path_core(periods,endo_nbr,exo_nbr,positive_var_indx, ...
+ exo_simul,init,initial_conditions,...
+ maximum_lag,maximum_lead,steady_state, ...
+ verbosity,bytecode_flag,order,M,pfm,algo,stack_solve_algo,...
+ olmmcp,options,oo)
+
+
+ep = options.ep;
+if init% Compute first order solution (Perturbation)...
+ endo_simul = simult_(initial_conditions,oo.dr,exo_simul(2:end,:),1);
+else
+ endo_simul = [initial_conditions repmat(steady_state,1,periods+1)];
+end
+oo.endo_simul = endo_simul;
+oo_.endo_simul = endo_simul;
+% Solve a perfect foresight model.
+% Keep a copy of endo_simul_1
+if verbosity
+ save ep_test_1 endo_simul exo_simul
+end
+if bytecode_flag && ~ep.stochastic.order
+ [flag,tmp] = bytecode('dynamic',endo_simul,exo_simul, M_.params, endo_simul, periods);
+else
+ flag = 1;
+end
+if flag
+ if order == 0
+ options.periods = periods;
+ options.block = pfm.block;
+ oo.endo_simul = endo_simul;
+ oo.exo_simul = exo_simul;
+ oo.steady_state = steady_state;
+ options.bytecode = bytecode_flag;
+ options.lmmcp = olmmcp;
+ options.stack_solve_algo = stack_solve_algo;
+ [tmp,flag] = perfect_foresight_solver_core(M,options,oo);
+ if ~flag && ~options.no_homotopy
+ exo_orig = oo.exo_simul;
+ endo_simul = repmat(steady_state,1,periods+1);
+ for i = 1:10
+ weight = i/10;
+ oo.endo_simul = [weight*initial_conditions + (1-weight)*steady_state ...
+ endo_simul];
+ oo.exo_simul = repmat((1-weight)*oo.exo_steady_state', ...
+ size(oo.exo_simul,1),1) + weight*exo_orig;
+ [tmp,flag] = perfect_foresight_solver_core(M,options,oo);
+ disp([i,flag])
+ if ~flag
+ break
+ end
+ endo_simul = tmp.endo_simul;
+ end
+ end
+ info_convergence = flag;
+ else
+ switch(algo)
+ case 0
+ [flag,tmp] = ...
+ solve_stochastic_perfect_foresight_model(endo_simul,exo_simul,pfm,ep.stochastic.quadrature.nodes,ep.stochastic.order);
+ case 1
+ [flag,tmp] = ...
+ solve_stochastic_perfect_foresight_model_1(endo_simul,exo_simul,options_,pfm,ep.stochastic.order);
+ end
+ endo_simul = tmp;
+ info_convergence = ~flag;
+ end
+end
+if verbosity
+ if info_convergence
+ disp(['Time: ' int2str(t) '. Convergence of the perfect foresight model solver!'])
+ else
+ disp(['Time: ' int2str(t) '. No convergence of the perfect foresight model solver!'])
+ end
+end
+if info_convergence
+ y = endo_simul(:,2);
+else
+ y = NaN(size(endo_nbr,1));
+end
diff --git a/matlab/ep/setup_stochastic_perfect_foresight_model_solver.m b/matlab/ep/setup_stochastic_perfect_foresight_model_solver.m
index 3df0a788b..286bd16ac 100644
--- a/matlab/ep/setup_stochastic_perfect_foresight_model_solver.m
+++ b/matlab/ep/setup_stochastic_perfect_foresight_model_solver.m
@@ -20,7 +20,9 @@ function pfm = setup_stochastic_perfect_foresight_model_solver(DynareModel,Dynar
pfm.lead_lag_incidence = DynareModel.lead_lag_incidence;
pfm.ny = DynareModel.endo_nbr;
pfm.Sigma = DynareModel.Sigma_e;
-pfm.Omega = chol(pfm.Sigma,'upper'); % Sigma = Omega'*Omega
+if det(pfm.Sigma) > 0
+ pfm.Omega = chol(pfm.Sigma,'upper'); % Sigma = Omega'*Omega
+end
pfm.number_of_shocks = length(pfm.Sigma);
pfm.stochastic_order = DynareOptions.ep.stochastic.order;
pfm.max_lag = DynareModel.maximum_endo_lag;
diff --git a/matlab/global_initialization.m b/matlab/global_initialization.m
index 1ea7af0a1..5549ca8ef 100644
--- a/matlab/global_initialization.m
+++ b/matlab/global_initialization.m
@@ -195,7 +195,11 @@ ep.innovation_distribution = 'gaussian';
% Set flag for the seed
ep.set_dynare_seed_to_default = 1;
% Set algorithm for the perfect foresight solver
-ep.stack_solve_algo = 4;
+ep.stack_solve_algo = 7;
+% Number of replications
+ep.replic_nbr = 1;
+% Parallel execution of replications
+ep.parallel_1 = false;
% Stochastic extended path related options.
ep.stochastic.method = '';
ep.stochastic.algo = 0;
diff --git a/matlab/perfect-foresight-models/make_y_.m b/matlab/perfect-foresight-models/make_y_.m
index 7611c9e97..1085b9030 100644
--- a/matlab/perfect-foresight-models/make_y_.m
+++ b/matlab/perfect-foresight-models/make_y_.m
@@ -53,6 +53,6 @@ else
end
% the first NaNs take care of the case where there are lags > 1 on
% exogenous variables
- oo_.endo_simul = [NaN(M_.endo_nbr,M_.maximum_lag-1) M_.endo_histval ...
+ oo_.endo_simul = [M_.endo_histval ...
oo_.steady_state*ones(1,options_.periods+M_.maximum_lead)];
end
diff --git a/matlab/perfect-foresight-models/perfect_foresight_solver.m b/matlab/perfect-foresight-models/perfect_foresight_solver.m
index 9a2b83f88..e171c749d 100644
--- a/matlab/perfect-foresight-models/perfect_foresight_solver.m
+++ b/matlab/perfect-foresight-models/perfect_foresight_solver.m
@@ -58,7 +58,7 @@ end
initperiods = 1:M_.maximum_lag;
lastperiods = (M_.maximum_lag+options_.periods+1):(M_.maximum_lag+options_.periods+M_.maximum_lead);
-oo_ = simulation_core(options_, M_, oo_);
+oo_ = perfect_foresight_solver_core(M_,options_,oo_);
% If simulation failed try homotopy.
if ~oo_.deterministic_simulation.status && ~options_.no_homotopy
@@ -135,7 +135,7 @@ if ~oo_.deterministic_simulation.status && ~options_.no_homotopy
saved_endo_simul = oo_.endo_simul;
- [oo_, me] = simulation_core(options_, M_, oo_);
+ [oo_,me] = perfect_foresight_solver_core(M_,options_,oo_);
if oo_.deterministic_simulation.status == 1
current_weight = new_weight;
diff --git a/matlab/perfect-foresight-models/perfect_foresight_solver_core.m b/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
new file mode 100644
index 000000000..34edc1ac0
--- /dev/null
+++ b/matlab/perfect-foresight-models/perfect_foresight_solver_core.m
@@ -0,0 +1,176 @@
+function [oo_, maxerror] = perfect_foresight_solver_core(M_, options_, oo_)
+%function [oo_, maxerror] = simulation_core(M_, options_, oo_)
+
+% Copyright (C) 2015 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 .
+
+if options_.linear_approximation && ~(isequal(options_.stack_solve_algo,0) || isequal(options_.stack_solve_algo,7))
+ error('perfect_foresight_solver: Option linear_approximation is only available with option stack_solve_algo equal to 0.')
+end
+
+if options_.linear && isequal(options_.stack_solve_algo,0)
+ options_.linear_approximation = 1;
+end
+
+if options_.block
+ if options_.bytecode
+ try
+ [info, tmp] = bytecode('dynamic', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state,1,options_.periods+2), options_.periods);
+ catch
+ info = 0;
+ end
+ if info
+ oo_.deterministic_simulation.status = false;
+ else
+ oo_.endo_simul = tmp;
+ oo_.deterministic_simulation.status = true;
+ end
+ if options_.no_homotopy
+ mexErrCheck('bytecode', info);
+ end
+ else
+ oo_ = feval([M_.fname '_dynamic'], options_, M_, oo_);
+ end
+else
+ if options_.bytecode
+ try
+ [info, tmp] = bytecode('dynamic', oo_.endo_simul, oo_.exo_simul, M_.params, repmat(oo_.steady_state,1,options_.periods+2), options_.periods);
+ catch
+ info = 0;
+ end
+ if info
+ oo_.deterministic_simulation.status = false;
+ else
+ oo_.endo_simul = tmp;
+ oo_.deterministic_simulation.status = true;
+ end
+ if options_.no_homotopy
+ mexErrCheck('bytecode', info);
+ end
+ else
+ if M_.maximum_endo_lead == 0 % Purely backward model
+ oo_ = sim1_purely_backward(options_, M_, oo_);
+ elseif M_.maximum_endo_lag == 0 % Purely forward model
+ oo_ = sim1_purely_forward(options_, M_, oo_);
+ else % General case
+ if options_.stack_solve_algo == 0
+ if options_.linear_approximation
+ oo_ = sim1_linear(options_, M_, oo_);
+ else
+ oo_ = sim1(M_, options_, oo_);
+ end
+ elseif options_.stack_solve_algo == 6
+ oo_ = sim1_lbj(options_, M_, oo_);
+ elseif options_.stack_solve_algo == 7
+ periods = options_.periods;
+ if ~isfield(options_.lmmcp,'lb')
+ [lb,ub,pfm.eq_index] = get_complementarity_conditions(M_,options_.ramsey_policy);
+ options_.lmmcp.lb = repmat(lb,periods,1);
+ options_.lmmcp.ub = repmat(ub,periods,1);
+ end
+ y = oo_.endo_simul;
+ y0 = y(:,1);
+ yT = y(:,periods+2);
+ z = y(:,2:periods+1);
+ illi = M_.lead_lag_incidence';
+ [i_cols,junk,i_cols_j] = find(illi(:));
+ illi = illi(:,2:3);
+ [i_cols_J1,junk,i_cols_1] = find(illi(:));
+ i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
+ if options_.linear_approximation
+ y_steady_state = oo_.steady_state;
+ x_steady_state = transpose(oo_.exo_steady_state);
+ ip = find(M_.lead_lag_incidence(1,:)');
+ ic = find(M_.lead_lag_incidence(2,:)');
+ in = find(M_.lead_lag_incidence(3,:)');
+ % Evaluate the Jacobian of the dynamic model at the deterministic steady state.
+ model_dynamic = str2func([M_.fname,'_dynamic']);
+ [d1,jacobian] = model_dynamic(y_steady_state([ip; ic; in]), x_steady_state, M_.params, y_steady_state, 1);
+ % Check that the dynamic model was evaluated at the steady state.
+ if max(abs(d1))>1e-12
+ error('Jacobian is not evaluated at the steady state!')
+ end
+ nyp = nnz(M_.lead_lag_incidence(1,:)) ;
+ ny0 = nnz(M_.lead_lag_incidence(2,:)) ;
+ nyf = nnz(M_.lead_lag_incidence(3,:)) ;
+ nd = nyp+ny0+nyf; % size of y (first argument passed to the dynamic file).
+ jexog = transpose(nd+(1:M_.exo_nbr));
+ jendo = transpose(1:nd);
+ z = bsxfun(@minus,z,y_steady_state);
+ x = bsxfun(@minus,oo_.exo_simul,x_steady_state);
+ [y,info] = dynare_solve(@linear_perfect_foresight_problem,z(:), options_, ...
+ jacobian, y0-y_steady_state, yT-y_steady_state, ...
+ x, M_.params, y_steady_state, ...
+ M_.maximum_lag, options_.periods, M_.endo_nbr, i_cols, ...
+ i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
+ M_.NNZDerivatives(1),jendo,jexog);
+ else
+ [y,info] = dynare_solve(@perfect_foresight_problem,z(:),options_, ...
+ str2func([M_.fname '_dynamic']),y0,yT, ...
+ oo_.exo_simul,M_.params,oo_.steady_state, ...
+ M_.maximum_lag,options_.periods,M_.endo_nbr,i_cols, ...
+ i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
+ M_.NNZDerivatives(1));
+ end
+ if all(imag(y)<.1*options_.dynatol.f)
+ if ~isreal(y)
+ y = real(y);
+ end
+ else
+ info = 1;
+ end
+ if options_.linear_approximation
+ oo_.endo_simul = [y0 bsxfun(@plus,reshape(y,M_.endo_nbr,periods),y_steady_state) yT];
+ else
+ oo_.endo_simul = [y0 reshape(y,M_.endo_nbr,periods) yT];
+ end
+ if info == 1
+ oo_.deterministic_simulation.status = false;
+ else
+ oo_.deterministic_simulation.status = true;
+ end
+ end
+ end
+ end
+end
+
+if nargout>1
+ y0 = oo_.endo_simul(:,1);
+ yT = oo_.endo_simul(:,options_.periods+2);
+ yy = oo_.endo_simul(:,2:options_.periods+1);
+ if ~exist('illi')
+ illi = M_.lead_lag_incidence';
+ [i_cols,junk,i_cols_j] = find(illi(:));
+ illi = illi(:,2:3);
+ [i_cols_J1,junk,i_cols_1] = find(illi(:));
+ i_cols_T = nonzeros(M_.lead_lag_incidence(1:2,:)');
+ end
+ if options_.block && ~options_.bytecode
+ maxerror = oo_.deterministic_simulation.error;
+ else
+ if options_.bytecode
+ [chck, residuals, junk]= bytecode('dynamic','evaluate', oo_.endo_simul, oo_.exo_simul, M_.params, oo_.steady_state, 1);
+ else
+ residuals = perfect_foresight_problem(yy(:),str2func([M_.fname '_dynamic']), y0, yT, ...
+ oo_.exo_simul,M_.params,oo_.steady_state, ...
+ M_.maximum_lag,options_.periods,M_.endo_nbr,i_cols, ...
+ i_cols_J1, i_cols_1, i_cols_T, i_cols_j, ...
+ M_.NNZDerivatives(1));
+ end
+ maxerror = max(max(abs(residuals)));
+ end
+end
diff --git a/matlab/perfect-foresight-models/sim1.m b/matlab/perfect-foresight-models/sim1.m
index dc7231b4d..56da30ead 100644
--- a/matlab/perfect-foresight-models/sim1.m
+++ b/matlab/perfect-foresight-models/sim1.m
@@ -1,4 +1,4 @@
-function oo_ = sim1(options_, M_, oo_)
+function oo = sim1(M, options, oo)
% function sim1
% Performs deterministic simulations with lead or lag on one period.
% Uses sparse matrices.
@@ -30,18 +30,18 @@ function oo_ = sim1(options_, M_, oo_)
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see .
-verbose = options_.verbosity;
+verbose = options.verbosity;
-endogenous_terminal_period = options_.endogenous_terminal_period;
-vperiods = options_.periods*ones(1,options_.simul.maxit);
-azero = options_.dynatol.f/1e7;
+endogenous_terminal_period = options.endogenous_terminal_period;
+vperiods = options.periods*ones(1,options.simul.maxit);
+azero = options.dynatol.f/1e7;
-lead_lag_incidence = M_.lead_lag_incidence;
+lead_lag_incidence = M.lead_lag_incidence;
-ny = M_.endo_nbr;
+ny = M.endo_nbr;
-maximum_lag = M_.maximum_lag;
-max_lag = M_.maximum_endo_lag;
+maximum_lag = M.maximum_lag;
+max_lag = M.maximum_endo_lag;
nyp = nnz(lead_lag_incidence(1,:)) ;
ny0 = nnz(lead_lag_incidence(2,:)) ;
@@ -50,11 +50,11 @@ nyf = nnz(lead_lag_incidence(3,:)) ;
nd = nyp+ny0+nyf;
stop = 0 ;
-periods = options_.periods;
-steady_state = oo_.steady_state;
-params = M_.params;
-endo_simul = oo_.endo_simul;
-exo_simul = oo_.exo_simul;
+periods = options.periods;
+steady_state = oo.steady_state;
+params = M.params;
+endo_simul = oo.endo_simul;
+exo_simul = oo.exo_simul;
i_cols_1 = nonzeros(lead_lag_incidence(2:3,:)');
i_cols_A1 = find(lead_lag_incidence(2:3,:)');
i_cols_T = nonzeros(lead_lag_incidence(1:2,:)');
@@ -72,20 +72,18 @@ if verbose
skipline()
end
-model_dynamic = str2func([M_.fname,'_dynamic']);
+model_dynamic = str2func([M.fname,'_dynamic']);
z = Y(find(lead_lag_incidence'));
-[d1,jacobian] = model_dynamic(z,oo_.exo_simul, params, ...
+[d1,jacobian] = model_dynamic(z,oo.exo_simul, params, ...
steady_state,maximum_lag+1);
res = zeros(periods*ny,1);
o_periods = periods;
-ZERO = zeros(length(i_upd),1);
-
h1 = clock ;
-iA = zeros(periods*M_.NNZDerivatives(1),3);
-for iter = 1:options_.simul.maxit
+iA = zeros(periods*M.NNZDerivatives(1),3);
+for iter = 1:options.simul.maxit
h2 = clock ;
i_rows = (1:ny)';
@@ -131,7 +129,7 @@ for iter = 1:options_.simul.maxit
err = max(abs(res));
- if options_.debug
+ if options.debug
fprintf('\nLargest absolute residual at iteration %d: %10.3f\n',iter,err);
if any(isnan(res)) || any(isinf(res)) || any(isnan(Y)) || any(isinf(Y))
fprintf('\nWARNING: NaN or Inf detected in the residuals or endogenous variables.\n');
@@ -147,8 +145,7 @@ for iter = 1:options_.simul.maxit
disp(str);
end
-
- if err < options_.dynatol.f
+ if err < options.dynatol.f
stop = 1 ;
break
end
@@ -168,18 +165,18 @@ for iter = 1:options_.simul.maxit
end
if endogenous_terminal_period
- err = evaluate_max_dynamic_residual(model_dynamic, Y, oo_.exo_simul, params, steady_state, o_periods, ny, max_lag, lead_lag_incidence);
+ err = evaluate_max_dynamic_residual(model_dynamic, Y, oo.exo_simul, params, steady_state, o_periods, ny, max_lag, lead_lag_incidence);
periods = o_periods;
end
if stop
if any(isnan(res)) || any(isinf(res)) || any(isnan(Y)) || any(isinf(Y)) || ~isreal(res) || ~isreal(Y)
- oo_.deterministic_simulation.status = false;% NaN or Inf occurred
- oo_.deterministic_simulation.error = err;
- oo_.deterministic_simulation.iterations = iter;
- oo_.deterministic_simulation.periods = vperiods(1:iter);
- oo_.endo_simul = reshape(Y,ny,periods+maximum_lag+M_.maximum_lead);
+ oo.deterministic_simulation.status = false;% NaN or Inf occurred
+ oo.deterministic_simulation.error = err;
+ oo.deterministic_simulation.iterations = iter;
+ oo.deterministic_simulation.periods = vperiods(1:iter);
+ oo.endo_simul = reshape(Y,ny,periods+maximum_lag+M.maximum_lead);
if verbose
skipline()
disp(sprintf('Total time of simulation: %s.', num2str(etime(clock,h1))))
@@ -197,11 +194,11 @@ if stop
disp(sprintf('Total time of simulation: %s', num2str(etime(clock,h1))))
printline(56)
end
- oo_.deterministic_simulation.status = true;% Convergency obtained.
- oo_.deterministic_simulation.error = err;
- oo_.deterministic_simulation.iterations = iter;
- oo_.deterministic_simulation.periods = vperiods(1:iter);
- oo_.endo_simul = reshape(Y,ny,periods+maximum_lag+M_.maximum_lead);
+ oo.deterministic_simulation.status = true;% Convergency obtained.
+ oo.deterministic_simulation.error = err;
+ oo.deterministic_simulation.iterations = iter;
+ oo.deterministic_simulation.periods = vperiods(1:iter);
+ oo.endo_simul = reshape(Y,ny,periods+maximum_lag+M.maximum_lead);
end
elseif ~stop
if verbose
@@ -210,10 +207,10 @@ elseif ~stop
disp('Maximum number of iterations is reached (modify option maxit).')
printline(62)
end
- oo_.deterministic_simulation.status = false;% more iterations are needed.
- oo_.deterministic_simulation.error = err;
- oo_.deterministic_simulation.periods = vperiods(1:iter);
- oo_.deterministic_simulation.iterations = options_.simul.maxit;
+ oo.deterministic_simulation.status = false;% more iterations are needed.
+ oo.deterministic_simulation.error = err;
+ oo.deterministic_simulation.periods = vperiods(1:iter);
+ oo.deterministic_simulation.iterations = options.simul.maxit;
end
if verbose
diff --git a/preprocessor/NumericalInitialization.cc b/preprocessor/NumericalInitialization.cc
index 85055905f..f8491d8b0 100644
--- a/preprocessor/NumericalInitialization.cc
+++ b/preprocessor/NumericalInitialization.cc
@@ -274,7 +274,7 @@ HistValStatement::writeOutput(ostream &output, const string &basename, bool mini
output << "%" << endl
<< "% HISTVAL instructions" << endl
<< "%" << endl
- << "M_.endo_histval = zeros(M_.endo_nbr,M_.maximum_endo_lag);" << endl;
+ << "M_.endo_histval = zeros(M_.endo_nbr,M_.maximum_lag);" << endl;
for (hist_values_t::const_iterator it = hist_values.begin();
it != hist_values.end(); it++)
@@ -310,7 +310,7 @@ HistValStatement::writeOutput(ostream &output, const string &basename, bool mini
int tsid = symbol_table.getTypeSpecificID(symb_id) + 1;
if (type == eEndogenous)
- output << "M_.endo_histval( " << tsid << ", M_.maximum_endo_lag + " << lag << ") = ";
+ output << "M_.endo_histval( " << tsid << ", M_.maximum_lag + " << lag << ") = ";
else if (type == eExogenous)
output << "oo_.exo_simul( M_.maximum_lag + " << lag << ", " << tsid << " ) = ";
else if (type != eExogenousDet)