diff --git a/matlab/estimation/online/online_auxiliary_filter.m b/matlab/estimation/online/online_auxiliary_filter.m
index 9e006d6e807489f8fa9ba441537f067f9df8c6bc..f4945f491f57cf98ff706546f32519b8e976d73f 100644
--- a/matlab/estimation/online/online_auxiliary_filter.m
+++ b/matlab/estimation/online/online_auxiliary_filter.m
@@ -41,7 +41,7 @@ SimulationFolder = CheckPath('online', M_.dname);
bounds = prior_bounds(bayestopt_, options_.prior_trunc); % Reset bounds as lb and ub must only be operational during mode-finding
% initialization of state particles
-[~, M_, options_, oo_, ReducedForm] = solve_model_for_online_filter(true, xparam1, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_);
+[~, ~, ReducedForm] = solve_model_for_online_filter(true, xparam1, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_.dr , oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
order = options_.order;
mf0 = ReducedForm.mf0;
@@ -80,7 +80,7 @@ for i=1:number_of_particles
info = 12042009;
while info
candidate = Prior.draw();
- [info, M_, options_, oo_] = solve_model_for_online_filter(false, candidate, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_);
+ [info] = solve_model_for_online_filter(false, candidate, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_.dr , oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
if ~info
xparam(:,i) = candidate(:);
end
@@ -117,8 +117,8 @@ for t=1:sample_size
tau_tilde = zeros(1,number_of_particles);
for i=1:number_of_particles
% model resolution
- [info, M_, options_, oo_, ReducedForm] = ...
- solve_model_for_online_filter(false, fore_xparam(:,i), dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_);
+ [info, M_, ReducedForm] = ...
+ solve_model_for_online_filter(false, fore_xparam(:,i), dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_.dr , oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
if ~info(1)
steadystate = ReducedForm.steadystate;
state_variables_steady_state = ReducedForm.state_variables_steady_state;
@@ -211,8 +211,8 @@ for t=1:sample_size
candidate = xparam(:,i) + chol_sigma_bar*randn(number_of_parameters, 1);
if all(candidate>=bounds.lb) && all(candidate<=bounds.ub)
% model resolution for new parameters particles
- [info, M_, options_, oo_, ReducedForm] = ...
- solve_model_for_online_filter(false, candidate, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_) ;
+ [info, M_, ReducedForm] = ...
+ solve_model_for_online_filter(false, candidate, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_.dr , oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
if ~info(1)
xparam(:,i) = candidate ;
steadystate = ReducedForm.steadystate;
@@ -359,7 +359,6 @@ for t=1:sample_size
end
end
save(sprintf('%s%sparameters_particles-%u.mat', SimulationFolder, filesep(), t), 'xparam');
-
dyntable(options_,'', {'Parameter'; 'Lower Bound (95%)';'Mean';'Upper Bound (95%)'}, bayestopt_.name, [lb95_xparam(:,t), mean_xparam(:,t), ub95_xparam(:,t)], cellofchararraymaxlength(bayestopt_.name)+2, 10, 6)
disp('')
diff --git a/matlab/nonlinear-filters/solve_model_for_online_filter.m b/matlab/nonlinear-filters/solve_model_for_online_filter.m
index 988bcb59cfc7e34ccba1381d21f10595193acf66..f93eb112508fa101c10894a911559ec4a9d97039 100644
--- a/matlab/nonlinear-filters/solve_model_for_online_filter.m
+++ b/matlab/nonlinear-filters/solve_model_for_online_filter.m
@@ -1,7 +1,7 @@
-function [info, M_, options_, oo_, ReducedForm] = ...
- solve_model_for_online_filter(setinitialcondition, xparam1, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_)
-% [info, M_, options_, oo_, ReducedForm] = ...
-% solve_model_for_online_filter(setinitialcondition, xparam1, dataset_, options_, M_, estim_params_, bayestopt_, bounds, oo_)
+function [info, M_, ReducedForm] = ...
+ solve_model_for_online_filter(setinitialcondition, xparam1, dataset_, options_, M_, estim_params_, bayestopt_, bounds, dr, endo_steady_state, exo_steady_state, exo_det_steady_state)
+% [info, M_, ReducedForm] = ...
+% solve_model_for_online_filter(setinitialcondition, xparam1, dataset_, options_, M_, estim_params_, bayestopt_, bounds, dr , endo_steady_state, exo_steady_state, exo_det_steady_state)
% Solves the dsge model for an particular parameters set.
%
@@ -14,13 +14,14 @@ function [info, M_, options_, oo_, ReducedForm] = ...
% - estim_params_ [struct] Estimated parameters.
% - bayestopt_ [struct] Prior definition.
% - bounds [struct] Prior bounds.
-% - oo_ [struct] Dynare results.
+% - dr [structure] Reduced form model.
+% - endo_steady_state [vector] steady state value for endogenous variables
+% - exo_steady_state [vector] steady state value for exogenous variables
+% - exo_det_steady_state [vector] steady state value for exogenous deterministic variables
%
% OUTPUTS
% - info [integer] scalar, nonzero if any problem occur when computing the reduced form.
% - M_ [struct] M_ description.
-% - options_ [struct] Dynare options.
-% - oo_ [struct] Dynare results.
% - ReducedForm [struct] Reduced form model.
% Copyright © 2013-2024 Dynare Team
@@ -40,21 +41,23 @@ function [info, M_, options_, oo_, ReducedForm] = ...
% You should have received a copy of the GNU General Public License
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
-info = zeros(4,1);
+info = zeros(4,1);
%----------------------------------------------------
% 1. Get the structural parameters & define penalties
%----------------------------------------------------
+% Ensure that xparam1 is a column vector.
+% (Don't do the transformation if xparam1 is empty, otherwise it would become a
+% 0×1 matrix, which create issues with older MATLABs when comparing with [] in
+% check_bounds_and_definiteness_estimation)
if ~isempty(xparam1)
xparam1 = xparam1(:);
end
M_ = set_all_parameters(xparam1,estim_params_,M_);
-
[~,info,~,Q,H]=check_bounds_and_definiteness_estimation(xparam1, M_, estim_params_, bounds);
-
if info(1)
return
end
@@ -68,20 +71,17 @@ M_.H = H;
%------------------------------------------------------------------------------
warning('off', 'MATLAB:nearlySingularMatrix')
-[oo_.dr, info, M_.params] = ...
- compute_decision_rules(M_, options_, oo_.dr, oo_.steady_state, oo_.exo_steady_state, oo_.exo_det_steady_state);
+[dr, info, M_.params] = ...
+ compute_decision_rules(M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
warning('on', 'MATLAB:nearlySingularMatrix')
if info(1)~=0
- if nargout==5
+ if nargout==3
ReducedForm = 0;
end
return
end
-% Get decision rules and transition equations.
-dr = oo_.dr;
-
% Set persistent variables (first call).
mf0 = bayestopt_.mf0;
mf1 = bayestopt_.mf1;
@@ -91,7 +91,7 @@ number_of_state_variables = length(mf0);
% Return reduced form model.
-if nargout>4
+if nargout>2
ReducedForm.ghx = dr.ghx(restrict_variables_idx,:);
ReducedForm.ghu = dr.ghu(restrict_variables_idx,:);
ReducedForm.steadystate = dr.ys(dr.order_var(restrict_variables_idx));
@@ -133,16 +133,11 @@ if setinitialcondition
StateVectorVariance = StateVectorVariance(mf0,mf0);
case 2% Initial state vector covariance is a monte-carlo based estimate of the ergodic variance (consistent with a k-order Taylor-approximation of the model).
StateVectorMean = ReducedForm.state_variables_steady_state;%.constant(mf0);
- old_DynareOptionsperiods = options_.periods;
options_.periods = 5000;
- old_DynareOptionspruning = options_.pruning;
options_.pruning = options_.particle.pruning;
y_ = simult(dr.ys, dr, M_, options_);
y_ = y_(dr.order_var(state_variables_idx),2001:options_.periods);
StateVectorVariance = cov(y_');
- options_.periods = old_DynareOptionsperiods;
- options_.pruning = old_DynareOptionspruning;
- clear('old_DynareOptionsperiods','y_');
case 3% Initial state vector covariance is a diagonal matrix.
StateVectorMean = ReducedForm.state_variables_steady_state;%.constant(mf0);
StateVectorVariance = options_.particle.initial_state_prior_std*eye(number_of_state_variables);