diff --git a/doc/manual/source/the-model-file.rst b/doc/manual/source/the-model-file.rst
index 6594f21c9..740ab0197 100644
--- a/doc/manual/source/the-model-file.rst
+++ b/doc/manual/source/the-model-file.rst
@@ -4310,10 +4310,6 @@ Computing the stochastic solution
When a list of ``VARIABLE_NAME`` is specified, results are
displayed only for these variables.
- The ``stoch_simul`` command with a first order approximation can
- benefit from the block decomposition of the model (see
- :opt:`block`).
-
*Options*
.. option:: ar = INTEGER
@@ -4982,8 +4978,8 @@ which is described below.
the endogenous variables are generated by assuming that the
agents believe that there will no more shocks after period
:math:`t+S`. This is an experimental feature and can be quite
- slow. A non-zero value is not compatible with either the
- ``bytecode`` or the ``block`` option of the ``model`` block.
+ slow. A non-zero value is not compatible with the ``bytecode``
+ option of the ``model`` block.
Default: ``0``.
.. option:: hybrid
@@ -5110,15 +5106,10 @@ The coefficients of the decision rules are stored as follows:
to all endogenous in the declaration order.
* :math:`A` is stored in ``oo_.dr.ghx``. The matrix rows correspond to
all endogenous in DR-order. The matrix columns correspond to state
- variables in DR-order, as given by ``oo_.dr.state_var``. (N.B.: if the
- ``block`` option to the ``model`` block has been specified, then rows
- are in declaration order, and columns are ordered
- according to ``oo_.dr.state_var`` which may differ from DR-order.)
+ variables in DR-order, as given by ``oo_.dr.state_var``.
* :math:`B` is stored ``oo_.dr.ghu``. The matrix rows correspond to
all endogenous in DR-order. The matrix columns correspond to
- exogenous variables in declaration order. (N.B.: if the
- ``block`` option to the ``model`` block has been specified, then rows
- are in declaration order.)
+ exogenous variables in declaration order.
Of course, the shown form of the approximation is only stylized,
because it neglects the required different ordering in :math:`y^s` and
@@ -5707,9 +5698,6 @@ Note that in order to avoid stochastic singularity, you must have at
least as many shocks or measurement errors in your model as you have
observed variables.
-The estimation using a first order approximation can benefit from the
-block decomposition of the model (see :opt:`block`).
-
.. _varobs:
.. command:: varobs VARIABLE_NAME...;
diff --git a/matlab/DsgeSmoother.m b/matlab/DsgeSmoother.m
index 7d3ab0284..7ea0cd462 100644
--- a/matlab/DsgeSmoother.m
+++ b/matlab/DsgeSmoother.m
@@ -57,7 +57,7 @@ function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,de
% SPECIAL REQUIREMENTS
% None
-% Copyright © 2006-2020 Dynare Team
+% Copyright © 2006-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -359,11 +359,7 @@ if ~options_.smoother_redux
oo_.dr.restrict_var_list = oldoo.restrict_var_list;
oo_.dr.restrict_columns = oldoo.restrict_columns;
else
- if options_.block == 0
- ic = [ M_.nstatic+(1:M_.nspred) M_.endo_nbr+(1:size(oo_.dr.ghx,2)-M_.nspred) ]';
- else
- ic = oo_.dr.restrict_columns;
- end
+ ic = [ M_.nstatic+(1:M_.nspred) M_.endo_nbr+(1:size(oo_.dr.ghx,2)-M_.nspred) ]';
if isempty(options_.nk)
nk=1;
diff --git a/matlab/check.m b/matlab/check.m
index 684325014..ec9ec6513 100644
--- a/matlab/check.m
+++ b/matlab/check.m
@@ -12,7 +12,7 @@ function [eigenvalues_,result,info] = check(M, options, oo)
% - result [integer] scalar, equal to 1 if Blanchard and Kahn conditions are satisfied, zero otherwise.
% - info [integer] scalar or vector, error code as returned by resol routine.
-% Copyright © 2001-2019 Dynare Team
+% Copyright © 2001-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -51,18 +51,8 @@ end
eigenvalues_ = dr.eigval;
[m_lambda,i]=sort(abs(eigenvalues_));
-% Count number of forward looking variables
-if ~options.block
- nyf = M.nsfwrd;
-else
- nyf = 0;
- for j = 1:length(M.block_structure.block)
- nyf = nyf + M.block_structure.block(j).n_forward + M.block_structure.block(j).n_mixed;
- end
-end
-
result = 0;
-if (nyf == dr.edim) && (dr.full_rank)
+if (M.nsfwrd == dr.edim) && (dr.full_rank)
result = 1;
end
@@ -73,7 +63,7 @@ if ~options.noprint
z=[m_lambda real(eigenvalues_(i)) imag(eigenvalues_(i))]';
disp(sprintf('%16.4g %16.4g %16.4g\n',z))
disp(sprintf('\nThere are %d eigenvalue(s) larger than 1 in modulus ', dr.edim));
- disp(sprintf('for %d forward-looking variable(s)',nyf));
+ disp(sprintf('for %d forward-looking variable(s)', M.nsfwrd));
skipline()
if result
disp('The rank condition is verified.')
diff --git a/matlab/disp_dr.m b/matlab/disp_dr.m
index b959ad7b9..7c95714c5 100644
--- a/matlab/disp_dr.m
+++ b/matlab/disp_dr.m
@@ -10,7 +10,7 @@ function disp_dr(dr,order,var_list)
% OUTPUTS
% none
-% Copyright © 2001-2019 Dynare Team
+% Copyright © 2001-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -36,14 +36,9 @@ end
nx =size(dr.ghx,2);
nu =size(dr.ghu,2);
-if options_.block
- k = M_.nspred;
- k1 = 1:M_.endo_nbr;
-else
- k = find(dr.kstate(:,2) <= M_.maximum_lag+1);
- klag = dr.kstate(k,[1 2]);
- k1 = dr.order_var;
-end
+k = find(dr.kstate(:,2) <= M_.maximum_lag+1);
+klag = dr.kstate(k,[1 2]);
+k1 = dr.order_var;
if isempty(var_list)
var_list = M_.endo_names(1:M_.orig_endo_nbr);
diff --git a/matlab/dr_block.m b/matlab/dr_block.m
deleted file mode 100644
index 6444523ec..000000000
--- a/matlab/dr_block.m
+++ /dev/null
@@ -1,717 +0,0 @@
-function [dr,info,M_,oo_] = dr_block(dr,task,M_,options_,oo_,varargin)
-% function [dr,info,M_,oo_] = dr_block(dr,task,M_,options_,oo_,varargin)
-% computes the reduced form solution of a rational expectations block-decomposed model
-% (first order approximation of the stochastic model around the deterministic steady state).
-%
-% NB: This code does not work with option mfs > 0. The preprocessor has a check to avoid this
-% configuration. See also #1726.
-%
-% INPUTS
-% dr [matlab structure] Decision rules for stochastic simulations.
-% task [integer] if task = 0 then dr_block computes decision rules.
-% if task = 1 then dr_block computes eigenvalues.
-% M_ [matlab structure] Definition of the model.
-% options_ [matlab structure] Global options.
-% oo_ [matlab structure] Results
-% oo_ [matlab cell] Other input arguments
-%
-% OUTPUTS
-% dr [matlab structure] Decision rules for stochastic simulations.
-% info [integer] info=1: the model doesn't define current variables uniquely
-% info=2: problem in mjdgges.dll info(2) contains error code.
-% info=3: BK order condition not satisfied info(2) contains "distance"
-% absence of stable trajectory.
-% info=4: BK order condition not satisfied info(2) contains "distance"
-% indeterminacy.
-% info=5: BK rank condition not satisfied.
-% info=6: The jacobian matrix evaluated at the steady state is complex.
-% M_ [matlab structure]
-% oo_ [matlab structure]
-%
-% ALGORITHM
-% first order block relaxation method applied to the model
-% E[A Yt-1 + B Yt + C Yt+1 + ut] = 0
-%
-% SPECIAL REQUIREMENTS
-% none.
-%
-
-% Copyright © 2010-2023 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;
-verbose = 0;
-if nargin > 5
- verbose = varargin{1};
-end
-%verbose = options_.verbosity;
-if options_.order > 1
- error('2nd and 3rd order approximation not implemented with block option')
-else
- if options_.loglinear
- error('The loglinear option is not yet supported in first order approximation for a block decomposed model');
- end
-end
-
-z = repmat(dr.ys,1,M_.maximum_lead + M_.maximum_lag + 1);
-zx = repmat([oo_.exo_simul oo_.exo_det_simul],M_.maximum_lead + M_.maximum_lag + 1, 1);
-if isempty(zx)
- zx = [repmat(oo_.exo_steady_state',M_.maximum_lead + M_.maximum_lag + 1,1) repmat(oo_.exo_det_steady_state',M_.maximum_lead + M_.maximum_lag + 1,1)];
-end
-
-if ~isfield(M_,'block_structure')
- error('Option ''block'' has not been specified')
-end
-data = M_.block_structure.block;
-
-if options_.bytecode
- [~, data]= bytecode('dynamic', 'evaluate', 'block_decomposed', z, zx, M_.params, dr.ys, 1, data);
-else
- T=NaN(M_.block_structure.dyn_tmp_nbr, 1);
- it_=M_.maximum_lag+1;
- y=dynvars_from_endo_simul(z, it_, M_);
- for blk = 1:length(M_.block_structure.block)
- [~, ~, T, data(blk).g1, data(blk).g1_x, data(blk).g1_xd, data(blk).g1_o]=feval([M_.fname '.dynamic'], blk, y, zx, M_.params, dr.ys, T, it_, true);
- end
-end
-dr.full_rank = 1;
-dr.eigval = [];
-dr.nd = 0;
-
-dr.ghx = [];
-dr.ghu = [];
-%Determine the global list of state variables:
-dr.state_var = M_.state_var;
-M_.block_structure.state_var = dr.state_var;
-n_sv = size(dr.state_var, 2);
-dr.ghx = zeros(M_.endo_nbr, length(dr.state_var));
-dr.exo_var = 1:M_.exo_nbr;
-dr.ghu = zeros(M_.endo_nbr, M_.exo_nbr);
-for i = 1:length(data)
- ghx = [];
- indexi_0 = 0;
- if (verbose)
- disp('======================================================================');
- disp(['Block ' int2str(i)]);
- disp('-----------');
- data(i)
- end
- n_pred = data(i).n_backward;
- n_fwrd = data(i).n_forward;
- n_both = data(i).n_mixed;
- n_static = data(i).n_static;
- nd = n_pred + n_fwrd + 2*n_both;
- dr.nd = dr.nd + nd;
- n_dynamic = n_pred + n_fwrd + n_both;
- exo_nbr = M_.block_structure.block(i).exo_nbr;
- exo_det_nbr = M_.block_structure.block(i).exo_det_nbr;
- other_endo_nbr = M_.block_structure.block(i).other_endo_nbr;
- jacob = full(data(i).g1);
- lead_lag_incidence = data(i).lead_lag_incidence;
- endo = data(i).variable;
- exo = data(i).exogenous;
- if (verbose)
- disp('jacob');
- disp(jacob);
- disp('lead_lag_incidence');
- disp(lead_lag_incidence);
- end
- maximum_lag = data(i).maximum_endo_lag;
- maximum_lead = data(i).maximum_endo_lead;
- n = n_dynamic + n_static;
-
- block_type = M_.block_structure.block(i).Simulation_Type;
- if task ~= 1
- if block_type == 2 || block_type == 4 || block_type == 7
- block_type = 8;
- end
- end
- if maximum_lag > 0 && (n_pred > 0 || n_both > 0) && block_type ~= 1
- indexi_0 = min(lead_lag_incidence(2,:));
- end
- switch block_type
- case 1
- %% ------------------------------------------------------------------
- %Evaluate Forward
- if maximum_lag > 0 && n_pred > 0
- indx_r = find(M_.block_structure.block(i).lead_lag_incidence(1,:));
- indx_c = M_.block_structure.block(i).lead_lag_incidence(1,indx_r);
- data(i).eigval = diag(jacob(indx_r, indx_c));
- data(i).rank = 0;
- else
- data(i).eigval = [];
- data(i).rank = 0;
- end
- dr.eigval = [dr.eigval ; data(i).eigval];
- %First order approximation
- if task ~= 1
- [tmp1, tmp2, indx_c] = find(M_.block_structure.block(i).lead_lag_incidence(2,:));
- B = jacob(:,indx_c);
- if (maximum_lag > 0 && n_pred > 0)
- [indx_r, tmp1, indx_r_v] = find(M_.block_structure.block(i).lead_lag_incidence(1,:));
- ghx = - B \ jacob(:,indx_r_v);
- end
- if other_endo_nbr
- fx = data(i).g1_o;
- % retrieves the derivatives with respect to endogenous
- % variable belonging to previous blocks
- fx_tm1 = zeros(n,other_endo_nbr);
- fx_t = zeros(n,other_endo_nbr);
- fx_tp1 = zeros(n,other_endo_nbr);
- % stores in fx_tm1 the lagged values of fx
- [r, c, lag] = find(data(i).lead_lag_incidence_other(1,:));
- fx_tm1(:,c) = fx(:,lag);
- % stores in fx the current values of fx
- [r, c, curr] = find(data(i).lead_lag_incidence_other(2,:));
- fx_t(:,c) = fx(:,curr);
- % stores in fx_tp1 the leaded values of fx
- [r, c, lead] = find(data(i).lead_lag_incidence_other(3,:));
- fx_tp1(:,c) = fx(:,lead);
-
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_x_sv = dr.ghx(dr.state_var, 1:n_sv);
-
- selector_tm1 = M_.block_structure.block(i).tm1;
-
- ghx_other = - B \ (fx_t * l_x + (fx_tp1 * l_x * l_x_sv) + fx_tm1 * selector_tm1);
- dr.ghx(endo, :) = dr.ghx(endo, :) + ghx_other;
- end
-
- if exo_nbr
- fu = data(i).g1_x;
- exo = dr.exo_var;
- if other_endo_nbr > 0
- l_u_sv = dr.ghu(dr.state_var,:);
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_u = dr.ghu(data(i).other_endogenous,:);
- fu_complet = zeros(n, M_.exo_nbr);
- fu_complet(:,data(i).exogenous) = fu;
- ghu = - B \ (fu_complet + fx_tp1 * l_x * l_u_sv + (fx_t) * l_u );
- else
- fu_complet = zeros(n, M_.exo_nbr);
- fu_complet(:,data(i).exogenous) = fu;
- ghu = - B \ fu_complet;
- end
- else
- exo = dr.exo_var;
- if other_endo_nbr > 0
- l_u_sv = dr.ghu(dr.state_var,:);
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_u = dr.ghu(data(i).other_endogenous,:);
- ghu = -B \ (fx_tp1 * l_x * l_u_sv + (fx_t) * l_u );
- else
- ghu = [];
- end
- end
- end
- case 2
- %% ------------------------------------------------------------------
- %Evaluate Backward
- if maximum_lead > 0 && n_fwrd > 0
- indx_r = find(M_.block_structure.block(i).lead_lag_incidence(3,:));
- indx_c = M_.block_structure.block(i).lead_lag_incidence(3,indx_r);
- data(i).eigval = 1 ./ diag(jacob(indx_r, indx_c));
- data(i).rank = sum(abs(data(i).eigval) > 0);
- full_rank = (rcond(jacob(indx_r, indx_c)) > 1e-9);
- else
- data(i).eigval = [];
- data(i).rank = 0;
- full_rank = 1;
- end
- dr.eigval = [dr.eigval ; data(i).eigval];
- dr.full_rank = dr.full_rank && full_rank;
- %First order approximation
- if task ~= 1
- if (maximum_lag > 0)
- indx_r = find(M_.block_structure.block(i).lead_lag_incidence(3,:));
- indx_c = M_.block_structure.block(i).lead_lag_incidence(3,indx_r);
- ghx = - inv(jacob(indx_r, indx_c));
- end
- ghu = - inv(jacob(indx_r, indx_c)) * data(i).g1_x;
- end
- case 3
- %% ------------------------------------------------------------------
- %Solve Forward single equation
- if maximum_lag > 0 && n_pred > 0
- data(i).eigval = - jacob(1 , 1 : n_pred) / jacob(1 , n_pred + n_static + 1 : n_pred + n_static + n_pred + n_both);
- data(i).rank = 0;
- else
- data(i).eigval = [];
- data(i).rank = 0;
- end
- dr.eigval = [dr.eigval ; data(i).eigval];
- %First order approximation
- if task ~= 1
- if (maximum_lag > 0)
- ghx = - jacob(1 , 1 : n_pred) / jacob(1 , n_pred + n_static + 1 : n_pred + n_static + n_pred + n_both);
- else
- ghx = 0;
- end
- if other_endo_nbr
- fx = data(i).g1_o;
- % retrieves the derivatives with respect to endogenous
- % variable belonging to previous blocks
- fx_tm1 = zeros(n,other_endo_nbr);
- fx_t = zeros(n,other_endo_nbr);
- fx_tp1 = zeros(n,other_endo_nbr);
- % stores in fx_tm1 the lagged values of fx
- [r, c, lag] = find(data(i).lead_lag_incidence_other(1,:));
- fx_tm1(:,c) = fx(:,lag);
- % stores in fx the current values of fx
- [r, c, curr] = find(data(i).lead_lag_incidence_other(2,:));
- fx_t(:,c) = fx(:,curr);
- % stores in fx_tm1 the leaded values of fx
- [r, c, lead] = find(data(i).lead_lag_incidence_other(3,:));
- fx_tp1(:,c) = fx(:,lead);
-
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_x_sv = dr.ghx(dr.state_var, 1:n_sv);
-
- selector_tm1 = M_.block_structure.block(i).tm1;
- ghx_other = - (fx_t * l_x + (fx_tp1 * l_x * l_x_sv) + fx_tm1 * selector_tm1) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
- dr.ghx(endo, :) = dr.ghx(endo, :) + ghx_other;
-
- end
- if exo_nbr
- fu = data(i).g1_x;
- if other_endo_nbr > 0
- l_u_sv = dr.ghu(dr.state_var,:);
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_u = dr.ghu(data(i).other_endogenous,:);
- fu_complet = zeros(n, M_.exo_nbr);
- fu_complet(:,data(i).exogenous) = fu;
- ghu = -(fu_complet + fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
- exo = dr.exo_var;
- else
- ghu = - fu / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
- end
- else
- if other_endo_nbr > 0
- l_u_sv = dr.ghu(dr.state_var,:);
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_u = dr.ghu(data(i).other_endogenous,:);
- ghu = -(fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
- exo = dr.exo_var;
- else
- ghu = [];
- end
- end
- end
- case 4
- %% ------------------------------------------------------------------
- %Solve Backward single equation
- if maximum_lead > 0 && n_fwrd > 0
- data(i).eigval = - jacob(1 , n_pred + n - n_fwrd + 1 : n_pred + n) / jacob(1 , n_pred + n + 1 : n_pred + n + n_fwrd) ;
- data(i).rank = sum(abs(data(i).eigval) > 0);
- full_rank = (abs(jacob(1,n_pred+n+1: n_pred+n+n_fwrd)) > 1e-9);
- else
- data(i).eigval = [];
- data(i).rank = 0;
- full_rank = 1;
- end
- dr.full_rank = dr.full_rank && full_rank;
- dr.eigval = [dr.eigval ; data(i).eigval];
- case 6
- %% ------------------------------------------------------------------
- %Solve Forward complete
- if (maximum_lag > 0)
- ghx = - jacob(: , n_pred + 1 : n_pred + n_static ...
- + n_pred + n_both) \ jacob(: , 1 : n_pred);
- else
- ghx = 0;
- end
- if maximum_lag > 0 && n_pred > 0
- data(i).eigval = -eig(ghx(n_static+1:end,:));
- data(i).rank = 0;
- full_rank = (rcond(ghx(n_static+1:end,:)) > 1e-9);
- else
- data(i).eigval = [];
- data(i).rank = 0;
- full_rank = 1;
- end
- dr.eigval = [dr.eigval ; data(i).eigval];
- dr.full_rank = dr.full_rank && full_rank;
- if task ~= 1
- if other_endo_nbr
- fx = data(i).g1_o;
- % retrieves the derivatives with respect to endogenous
- % variable belonging to previous blocks
- fx_tm1 = zeros(n,other_endo_nbr);
- fx_t = zeros(n,other_endo_nbr);
- fx_tp1 = zeros(n,other_endo_nbr);
- % stores in fx_tm1 the lagged values of fx
- [r, c, lag] = find(data(i).lead_lag_incidence_other(1,:));
- fx_tm1(:,c) = fx(:,lag);
- % stores in fx the current values of fx
- [r, c, curr] = find(data(i).lead_lag_incidence_other(2,:));
- fx_t(:,c) = fx(:,curr);
- % stores in fx_tm1 the leaded values of fx
- [r, c, lead] = find(data(i).lead_lag_incidence_other(3,:));
- fx_tp1(:,c) = fx(:,lead);
-
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_x_sv = dr.ghx(dr.state_var, 1:n_sv);
-
- selector_tm1 = M_.block_structure.block(i).tm1;
- ghx_other = - (fx_t * l_x + (fx_tp1 * l_x * l_x_sv) + fx_tm1 * selector_tm1) / jacob(: , n_pred + 1 : n_pred + n_static + n_pred + n_both);
- dr.ghx(endo, :) = dr.ghx(endo, :) + ghx_other;
- end
- if exo_nbr
- fu = data(i).g1_x;
- if other_endo_nbr > 0
- l_u_sv = dr.ghu(dr.state_var,:);
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_u = dr.ghu(data(i).other_endogenous,:);
- fu_complet = zeros(n, M_.exo_nbr);
- fu_complet(:,data(i).exogenous) = fu;
- ghu = -(fu_complet + fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(: , n_pred + 1 : n_pred + n_static + n_pred + n_both);
- exo = dr.exo_var;
- else
- ghu = - fu / jacob(: , n_pred + 1 : n_pred + n_static + n_pred + n_both);
- end
- else
- if other_endo_nbr > 0
- l_u_sv = dr.ghu(dr.state_var,:);
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_u = dr.ghu(data(i).other_endogenous,:);
- ghu = -(fx_tp1 * l_x * l_u_sv + (fx_t) * l_u ) / jacob(1 , n_pred + 1 : n_pred + n_static + n_pred + n_both);
- exo = dr.exo_var;
- else
- ghu = [];
- end
- end
- end
- case 7
- %% ------------------------------------------------------------------
- %Solve Backward complete
- if maximum_lead > 0 && n_fwrd > 0
- tmp = -jacob(: , n_pred + 1 : n_pred + n) \ ...
- jacob(: , n_pred + n + 1 : n_pred + n + n_fwrd);
- data(i).eigval = 1 ./ eig(tmp(n_static+1:end, :));
- data(i).rank = sum(abs(data(i).eigval) > 0);
- full_rank = (rcond(jacob(: , n_pred + 1 : n_pred + n)) > 1e-9);
- else
- data(i).eigval = [];
- data(i).rank = 0;
- full_rank = 1;
- end
- dr.full_rank = dr.full_rank && full_rank;
- dr.eigval = [dr.eigval ; data(i).eigval];
- case {5,8}
- %% ------------------------------------------------------------------
- %The lead_lag_incidence contains columns in the following order:
- % static variables, backward variable, mixed variables and forward variables
- %
- %Proceeds to a QR decomposition on the jacobian matrix in order to reduce the problem size
- index_c = lead_lag_incidence(2,:); % Index of all endogenous variables present at time=t
- index_s = lead_lag_incidence(2,1:n_static); % Index of all static endogenous variables present at time=t
- if n_static > 0
- [Q, ~] = qr(jacob(:,index_s));
- aa = Q'*jacob;
- else
- aa = jacob;
- end
- index_0m = (n_static+1:n_static+n_pred) + indexi_0 - 1;
- index_0p = (n_static+n_pred+1:n) + indexi_0 - 1;
- index_m = 1:(n_pred+n_both);
- index_p = lead_lag_incidence(3,find(lead_lag_incidence(3,:)));
- nyf = n_fwrd + n_both;
- A = aa(:,index_m); % Jacobain matrix for lagged endogeneous variables
- B = aa(:,index_c); % Jacobian matrix for contemporaneous endogeneous variables
- C = aa(:,index_p); % Jacobain matrix for led endogeneous variables
-
- row_indx = n_static+1:n;
-
- if task ~= 1 && options_.dr_cycle_reduction
- A1 = [aa(row_indx,index_m ) zeros(n_dynamic,n_fwrd)];
- B1 = [aa(row_indx,index_0m) aa(row_indx,index_0p) ];
- C1 = [zeros(n_dynamic,n_pred) aa(row_indx,index_p)];
- [ghx, info] = cycle_reduction(A1, B1, C1, options_.dr_cycle_reduction_tol);
- %ghx
- ghx = ghx(:,index_m);
- hx = ghx(1:n_pred+n_both,:);
- gx = ghx(1+n_pred:end,:);
- end
-
- if (task ~= 1 && ((options_.dr_cycle_reduction && info ==1) || ~options_.dr_cycle_reduction)) || task == 1
- D = [[aa(row_indx,index_0m) zeros(n_dynamic,n_both) aa(row_indx,index_p)] ; [zeros(n_both, n_pred) eye(n_both) zeros(n_both, n_both + n_fwrd)]];
- E = [-aa(row_indx,[index_m index_0p]) ; [zeros(n_both, n_both + n_pred) eye(n_both, n_both + n_fwrd) ] ];
-
- [ss, tt, w, sdim, data(i).eigval, info1] = mjdgges(E,D,options_.qz_criterium,options_.qz_zero_threshold);
-
- if (verbose)
- disp('eigval');
- disp(data(i).eigval);
- end
- if info1
- if info1 == -30
- % one eigenvalue is close to 0/0
- info(1) = 7;
- else
- info(1) = 2;
- info(2) = info1;
- info(3) = size(E,2);
- end
- return
- end
- nba = nd-sdim;
- if task == 1
- data(i).rank = rank(w(nd-nyf+1:end,nd-nyf+1:end));
- dr.full_rank = dr.full_rank && (rcond(w(nd-nyf+1:end,nd- ...
- nyf+1:end)) > 1e-9);
- dr.eigval = [dr.eigval ; data(i).eigval];
- end
- if (verbose)
- disp(['sum eigval > 1 = ' int2str(sum(abs(data(i).eigval) > 1.)) ' nyf=' int2str(nyf) ' and dr.rank=' int2str(data(i).rank)]);
- disp(['data(' int2str(i) ').eigval']);
- disp(data(i).eigval);
- end
-
- %First order approximation
- if task ~= 1
- if nba ~= nyf
- if isfield(options_,'indeterminacy_continuity')
- if options_.indeterminacy_msv == 1
- [ss,tt,w,q] = qz(E',D');
- [ss,tt,w,~] = reorder(ss,tt,w,q);
- ss = ss';
- tt = tt';
- w = w';
- nba = nyf;
- end
- else
- sorted_roots = sort(abs(data(i).eigval));
- if nba > nyf
- temp = sorted_roots(nd-nba+1:nd-nyf)-1-options_.qz_criterium;
- info(1) = 3;
- elseif nba < nyf
- temp = sorted_roots(nd-nyf+1:nd-nba)-1-options_.qz_criterium;
- info(1) = 4;
- end
- info(2) = temp'*temp;
- return
- end
- end
- indx_stable_root = 1: (nd - nyf); %=> index of stable roots
- indx_explosive_root = n_pred + n_both + 1:nd; %=> index of explosive roots
- % derivatives with respect to dynamic state variables
- % forward variables
- Z = w';
- Z11t = Z(indx_stable_root, indx_stable_root)';
- Z21 = Z(indx_explosive_root, indx_stable_root);
- Z22 = Z(indx_explosive_root, indx_explosive_root);
- if ~isfloat(Z21) && (condest(Z21) > 1e9)
- % condest() fails on a scalar under Octave
- info(1) = 5;
- info(2) = condest(Z21);
- return
- else
- %gx = -inv(Z22) * Z21;
- gx = - Z22 \ Z21;
- end
-
- % predetermined variables
- hx = Z11t * inv(tt(indx_stable_root, indx_stable_root)) * ss(indx_stable_root, indx_stable_root) * inv(Z11t);
-
- k1 = 1:(n_pred+n_both);
- k2 = 1:(n_fwrd+n_both);
-
- ghx = [hx(k1,:); gx(k2(n_both+1:end),:)];
- end
- end
-
- if task~= 1
- %lead variables actually present in the model
- j4 = n_static+n_pred+1:n_static+n_pred+n_both+n_fwrd; % Index on the forward and both variables
- j3 = nonzeros(lead_lag_incidence(2,j4)) - n_static - 2 * n_pred - n_both; % Index on the non-zeros forward and both variables
- j4 = find(lead_lag_incidence(2,j4));
-
- if n_static > 0
- B_static = B(:,1:n_static); % submatrix containing the derivatives w.r. to static variables
- else
- B_static = [];
- end
- %static variables, backward variable, mixed variables and forward variables
- B_pred = B(:,n_static+1:n_static+n_pred+n_both);
- B_fyd = B(:,n_static+n_pred+n_both+1:end);
-
- % static variables
- if n_static > 0
- temp = - C(1:n_static,j3)*gx(j4,:)*hx;
- j5 = index_m;
- b = aa(:,index_c);
- b10 = b(1:n_static, 1:n_static);
- b11 = b(1:n_static, n_static+1:n);
- temp(:,j5) = temp(:,j5)-A(1:n_static,:);
- temp = b10\(temp-b11*ghx);
- ghx = [temp; ghx];
- temp = [];
- end
-
- A_ = real([B_static C(:,j3)*gx+B_pred B_fyd]); % The state_variable of the block are located at [B_pred B_both]
-
- if other_endo_nbr
- if n_static > 0
- fx = Q' * data(i).g1_o;
- else
- fx = data(i).g1_o;
- end
- % retrieves the derivatives with respect to endogenous
- % variable belonging to previous blocks
- fx_tm1 = zeros(n,other_endo_nbr);
- fx_t = zeros(n,other_endo_nbr);
- fx_tp1 = zeros(n,other_endo_nbr);
- % stores in fx_tm1 the lagged values of fx
- [r, c, lag] = find(data(i).lead_lag_incidence_other(1,:));
- fx_tm1(:,c) = fx(:,lag);
- % stores in fx the current values of fx
- [r, c, curr] = find(data(i).lead_lag_incidence_other(2,:));
- fx_t(:,c) = fx(:,curr);
- % stores in fx_tp1 the leaded values of fx
- [r, c, lead] = find(data(i).lead_lag_incidence_other(3,:));
- fx_tp1(:,c) = fx(:,lead);
-
- l_x = dr.ghx(data(i).other_endogenous,:);
-
- l_x_sv = dr.ghx(dr.state_var, :);
-
- selector_tm1 = M_.block_structure.block(i).tm1;
-
- B_ = [zeros(size(B_static)) zeros(n,n_pred) C(:,j3) ];
- C_ = l_x_sv;
- D_ = (fx_t * l_x + fx_tp1 * l_x * l_x_sv + fx_tm1 * selector_tm1 );
- % Solve the Sylvester equation:
- % A_ * gx + B_ * gx * C_ + D_ = 0
- if block_type == 5
- vghx_other = - inv(kron(eye(size(D_,2)), A_) + kron(C_', B_)) * vec(D_);
- ghx_other = reshape(vghx_other, size(D_,1), size(D_,2));
- elseif options_.sylvester_fp
- ghx_other = gensylv_fp(A_, B_, C_, D_, i, options_.sylvester_fixed_point_tol);
- else
- ghx_other = gensylv(1, A_, B_, C_, -D_);
- end
- if options_.aim_solver ~= 1
- % Necessary when using Sims' routines for QZ
- ghx_other = real(ghx_other);
- end
-
- dr.ghx(endo, :) = dr.ghx(endo, :) + ghx_other;
- end
-
- if exo_nbr
- if n_static > 0
- fu = Q' * data(i).g1_x;
- else
- fu = data(i).g1_x;
- end
-
- if other_endo_nbr > 0
- l_u_sv = dr.ghu(dr.state_var,:);
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_u = dr.ghu(data(i).other_endogenous,:);
- fu_complet = zeros(n, M_.exo_nbr);
- fu_complet(:,data(i).exogenous) = fu;
- % Solve the equation in ghu:
- % A_ * ghu + (fu_complet + fx_tp1 * l_x * l_u_sv + (fx_t + B_ * ghx_other) * l_u ) = 0
-
- ghu = -A_\ (fu_complet + fx_tp1 * l_x * l_u_sv + fx_t * l_u + B_ * ghx_other * l_u_sv );
- exo = dr.exo_var;
- else
- ghu = - A_ \ fu;
- end
- else
- if other_endo_nbr > 0
- l_u_sv = dr.ghu(dr.state_var,:);
- l_x = dr.ghx(data(i).other_endogenous,:);
- l_u = dr.ghu(data(i).other_endogenous,:);
- % Solve the equation in ghu:
- % A_ * ghu + (fx_tp1 * l_x * l_u_sv + (fx_t + B_ * ghx_other) * l_u ) = 0
- ghu = -real(A_)\ (fx_tp1 * l_x * l_u_sv + (fx_t * l_u + B_ * ghx_other * l_u_sv) );
- exo = dr.exo_var;
- else
- ghu = [];
- end
- end
-
-
-
- if options_.loglinear
- error('The loglinear option is not yet supported in first order approximation for a block decomposed model');
- % k = find(dr.kstate(:,2) <= M_.maximum_endo_lag+1);
- % klag = dr.kstate(k,[1 2]);
- % k1 = dr.order_var;
- %
- % ghx = repmat(1./dr.ys(k1),1,size(ghx,2)).*ghx.* ...
- % repmat(dr.ys(k1(klag(:,1)))',size(ghx,1),1);
- % ghu = repmat(1./dr.ys(k1),1,size(ghu,2)).*ghu;
- end
-
-
- if options_.aim_solver ~= 1
- % Necessary when using Sims' routines for QZ
- ghx = real(ghx);
- ghu = real(ghu);
- end
-
- %exogenous deterministic variables
- if exo_det_nbr > 0
- error('Deterministic exogenous variables are not yet implemented in first order approximation for a block decomposed model');
- % f1 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+2:end,order_var))));
- % f0 = sparse(jacobia_(:,nonzeros(M_.lead_lag_incidence(M_.maximum_endo_lag+1,order_var))));
- % fudet = data(i).g1_xd;
- % M1 = inv(f0+[zeros(n,n_static) f1*gx zeros(n,nyf-n_both)]);
- % M2 = M1*f1;
- % dr.ghud = cell(M_.exo_det_length,1);
- % dr.ghud{1} = -M1*fudet;
- % for i = 2:M_.exo_det_length
- % dr.ghud{i} = -M2*dr.ghud{i-1}(end-nyf+1:end,:);
- % end
- end
- end
- end
- if task ~=1
- if (maximum_lag > 0 && (n_pred > 0 || n_both > 0))
- sorted_col_dr_ghx = M_.block_structure.block(i).sorted_col_dr_ghx;
- dr.ghx(endo, sorted_col_dr_ghx) = dr.ghx(endo, sorted_col_dr_ghx) + ghx;
- data(i).ghx = ghx;
- data(i).pol.i_ghx = sorted_col_dr_ghx;
- else
- data(i).pol.i_ghx = [];
- end
- data(i).ghu = ghu;
- dr.ghu(endo, exo) = ghu;
- data(i).pol.i_ghu = exo;
- end
-
- if (verbose)
- disp('dr.ghx');
- dr.ghx
- disp('dr.ghu');
- dr.ghu
- end
-
-end
-M_.block_structure.block = data ;
-if (verbose)
- disp('dr.ghx');
- disp(real(dr.ghx));
- disp('dr.ghu');
- disp(real(dr.ghu));
-end
-if (task == 1)
- return
-end
diff --git a/matlab/dsge_likelihood.m b/matlab/dsge_likelihood.m
index f5dec3529..7c5bbac7c 100644
--- a/matlab/dsge_likelihood.m
+++ b/matlab/dsge_likelihood.m
@@ -115,7 +115,7 @@ function [fval,info,exit_flag,DLIK,Hess,SteadyState,trend_coeff,Model,DynareOpti
%! @end deftypefn
%@eod:
-% Copyright © 2004-2021 Dynare Team
+% Copyright © 2004-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -680,9 +680,7 @@ singularity_has_been_detected = false;
% First test multivariate filter if specified; potentially abort and use univariate filter instead
if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
if no_missing_data_flag && ~DynareOptions.occbin.likelihood.status
- if DynareOptions.block
- [LIK,lik] = block_kalman_filter(T,R,Q,H,Pstar,Y,start,Z,kalman_tol,riccati_tol, Model.nz_state_var, Model.n_diag, Model.nobs_non_statevar);
- elseif DynareOptions.fast_kalman_filter
+ if DynareOptions.fast_kalman_filter
if diffuse_periods
%kalman_algo==3 requires no diffuse periods (stationary
%observables) as otherwise FE matrix will not be positive
@@ -709,23 +707,18 @@ if ((kalman_algo==1) || (kalman_algo==3))% Multivariate Kalman Filter
analytic_deriv_info{:});
end
else
- if 0 %DynareOptions.block
- [LIK,lik] = block_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,...
- T,R,Q,H,Pstar,Y,start,Z,kalman_tol,riccati_tol, Model.nz_state_var, Model.n_diag, Model.nobs_non_statevar);
- else
- [LIK,lik] = missing_observations_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
- a_0_given_tm1, Pstar, ...
- kalman_tol, DynareOptions.riccati_tol, ...
- DynareOptions.rescale_prediction_error_covariance, ...
- DynareOptions.presample, ...
- T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods, occbin_);
- if occbin_.status && isinf(LIK)
- fval = Inf;
- info(1) = 320;
- info(4) = 0.1;
- exit_flag = 0;
- return
- end
+ [LIK,lik] = missing_observations_kalman_filter(DatasetInfo.missing.aindex,DatasetInfo.missing.number_of_observations,DatasetInfo.missing.no_more_missing_observations,Y,diffuse_periods+1,size(Y,2), ...
+ a_0_given_tm1, Pstar, ...
+ kalman_tol, DynareOptions.riccati_tol, ...
+ DynareOptions.rescale_prediction_error_covariance, ...
+ DynareOptions.presample, ...
+ T,Q,R,H,Z,mm,pp,rr,Zflag,diffuse_periods, occbin_);
+ if occbin_.status && isinf(LIK)
+ fval = Inf;
+ info(1) = 320;
+ info(4) = 0.1;
+ exit_flag = 0;
+ return
end
end
if analytic_derivation
diff --git a/matlab/dynare_estimation_init.m b/matlab/dynare_estimation_init.m
index b4b68d1da..2be5b45fd 100644
--- a/matlab/dynare_estimation_init.m
+++ b/matlab/dynare_estimation_init.m
@@ -32,7 +32,7 @@ function [dataset_, dataset_info, xparam1, hh, M_, options_, oo_, estim_params_,
% SPECIAL REQUIREMENTS
% none
-% Copyright © 2003-2021 Dynare Team
+% Copyright © 2003-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -120,14 +120,9 @@ if options_.analytic_derivation && options_.fast_kalman_filter
end
% fast kalman filter is only available with kalman_algo == 0,1,3
-if options_.fast_kalman_filter
- if ~ismember(options_.kalman_algo, [0,1,3])
- error(['estimation option conflict: fast_kalman_filter is only available ' ...
+if options_.fast_kalman_filter && ~ismember(options_.kalman_algo, [0,1,3])
+ error(['estimation option conflict: fast_kalman_filter is only available ' ...
'with kalman_algo = 0, 1 or 3'])
- elseif options_.block
- error(['estimation option conflict: fast_kalman_filter is not available ' ...
- 'with block'])
- end
end
% Set options_.lik_init equal to 3 if diffuse filter is used or kalman_algo refers to a diffuse filter algorithm.
@@ -456,45 +451,23 @@ else
end
% Define union of observed and state variables
-if options_.block
- k1 = k1';
- [k2, i_posA, i_posB] = union(k1', M_.state_var', 'rows');
- % Set restrict_state to postion of observed + state variables in expanded state vector.
- oo_.dr.restrict_var_list = [k1(i_posA) M_.state_var(sort(i_posB))];
- % set mf0 to positions of state variables in restricted state vector for likelihood computation.
- [~,bayestopt_.mf0] = ismember(M_.state_var',oo_.dr.restrict_var_list);
- % Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
- [~,bayestopt_.mf1] = ismember(k1,oo_.dr.restrict_var_list);
- % Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
- bayestopt_.mf2 = var_obs_index_dr;
- bayestopt_.mfys = k1;
- oo_.dr.restrict_columns = [size(i_posA,1)+(1:size(M_.state_var,2))];
- [k2, i_posA, i_posB] = union(k3p, M_.state_var', 'rows');
- bayestopt_.smoother_var_list = [k3p(i_posA); M_.state_var(sort(i_posB))'];
- [~,~,bayestopt_.smoother_saved_var_list] = intersect(k3p,bayestopt_.smoother_var_list(:));
- [~,ic] = intersect(bayestopt_.smoother_var_list,M_.state_var);
- bayestopt_.smoother_restrict_columns = ic;
- [~,bayestopt_.smoother_mf] = ismember(k1, bayestopt_.smoother_var_list);
-else
- % Define union of observed and state variables
- k2 = union(var_obs_index_dr,[M_.nstatic+1:M_.nstatic+M_.nspred]', 'rows');
- % Set restrict_state to postion of observed + state variables in expanded state vector.
- oo_.dr.restrict_var_list = k2;
- % set mf0 to positions of state variables in restricted state vector for likelihood computation.
- [~,bayestopt_.mf0] = ismember([M_.nstatic+1:M_.nstatic+M_.nspred]',k2);
- % Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
- [~,bayestopt_.mf1] = ismember(var_obs_index_dr,k2);
- % Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
- bayestopt_.mf2 = var_obs_index_dr;
- bayestopt_.mfys = k1;
- [~,ic] = intersect(k2,nstatic+(1:npred)');
- oo_.dr.restrict_columns = [ic; length(k2)+(1:nspred-npred)'];
- bayestopt_.smoother_var_list = union(k2,k3);
- [~,~,bayestopt_.smoother_saved_var_list] = intersect(k3,bayestopt_.smoother_var_list(:));
- [~,ic] = intersect(bayestopt_.smoother_var_list,nstatic+(1:npred)');
- bayestopt_.smoother_restrict_columns = ic;
- [~,bayestopt_.smoother_mf] = ismember(var_obs_index_dr, bayestopt_.smoother_var_list);
-end
+k2 = union(var_obs_index_dr,[M_.nstatic+1:M_.nstatic+M_.nspred]', 'rows');
+% Set restrict_state to postion of observed + state variables in expanded state vector.
+oo_.dr.restrict_var_list = k2;
+% set mf0 to positions of state variables in restricted state vector for likelihood computation.
+[~,bayestopt_.mf0] = ismember([M_.nstatic+1:M_.nstatic+M_.nspred]',k2);
+% Set mf1 to positions of observed variables in restricted state vector for likelihood computation.
+[~,bayestopt_.mf1] = ismember(var_obs_index_dr,k2);
+% Set mf2 to positions of observed variables in expanded state vector for filtering and smoothing.
+bayestopt_.mf2 = var_obs_index_dr;
+bayestopt_.mfys = k1;
+[~,ic] = intersect(k2,nstatic+(1:npred)');
+oo_.dr.restrict_columns = [ic; length(k2)+(1:nspred-npred)'];
+bayestopt_.smoother_var_list = union(k2,k3);
+[~,~,bayestopt_.smoother_saved_var_list] = intersect(k3,bayestopt_.smoother_var_list(:));
+[~,ic] = intersect(bayestopt_.smoother_var_list,nstatic+(1:npred)');
+bayestopt_.smoother_restrict_columns = ic;
+[~,bayestopt_.smoother_mf] = ismember(var_obs_index_dr, bayestopt_.smoother_var_list);
if options_.analytic_derivation
if options_.lik_init == 3
@@ -661,9 +634,6 @@ if options_.heteroskedastic_filter
if options_.fast_kalman_filter
error('estimation option conflict: "heteroskedastic_filter" incompatible with "fast_kalman_filter"')
end
- if options_.block
- error('estimation option conflict: "heteroskedastic_filter" incompatible with block kalman filter')
- end
if options_.analytic_derivation
error(['estimation option conflict: analytic_derivation isn''t available ' ...
'for heteroskedastic_filter'])
@@ -717,4 +687,4 @@ if options_.occbin.smoother.status && options_.occbin.smoother.inversion_filter
fprintf('dynare_estimation_init: the inversion filter does not support smoothed_state_uncertainty. Disabling the option.\n')
options_.smoothed_state_uncertainty=false;
end
-end
\ No newline at end of file
+end
diff --git a/matlab/dynare_resolve.m b/matlab/dynare_resolve.m
index 727d9f38b..aca22c68a 100644
--- a/matlab/dynare_resolve.m
+++ b/matlab/dynare_resolve.m
@@ -16,7 +16,7 @@ function [A,B,ys,info,M_,oo_] = dynare_resolve(M_,options_,oo_,mode)
% - M_ [structure] Matlab's structure describing the model
% - oo_ [structure] Matlab's structure containing the results
-% Copyright © 2001-2021 Dynare Team
+% Copyright © 2001-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -52,11 +52,7 @@ switch nargin
nstatic = M_.nstatic;
nspred = M_.nspred;
iv = (1:endo_nbr)';
- if ~options_.block
- ic = [ nstatic+(1:nspred) endo_nbr+(1:size(oo_.dr.ghx,2)-nspred) ]';
- else
- ic = oo_.dr.restrict_columns;
- end
+ ic = [ nstatic+(1:nspred) endo_nbr+(1:size(oo_.dr.ghx,2)-nspred) ]';
case 4
iv = oo_.dr.restrict_var_list;
ic = oo_.dr.restrict_columns;
diff --git a/matlab/dynvars_from_endo_simul.m b/matlab/dynvars_from_endo_simul.m
deleted file mode 100644
index ceddbefe0..000000000
--- a/matlab/dynvars_from_endo_simul.m
+++ /dev/null
@@ -1,26 +0,0 @@
-function y2 = dynvars_from_endo_simul(y, it_, M_)
-% Given the matrix y of paths for all endogenous (same format as
-% oo_.endo_simul), and an iteration number (first simulation period corresponds
-% to it_=M_.maximum_lag+1), return a vector of endogenous values in the format
-% expected by the dynamic.m file (i.e. whose indices are described by
-% M_.lead_lag_incidence)
-
-% Copyright © 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 .
-
-y2 = y(:, it_+(-M_.maximum_endo_lag:M_.maximum_endo_lead));
-y2 = y2(find(M_.lead_lag_incidence'));
diff --git a/matlab/evaluate_steady_state.m b/matlab/evaluate_steady_state.m
index eae8bf782..2dba4e288 100644
--- a/matlab/evaluate_steady_state.m
+++ b/matlab/evaluate_steady_state.m
@@ -69,11 +69,6 @@ if length(M.aux_vars) > 0 && ~steadystate_flag && M.set_auxiliary_variables
end
if options.ramsey_policy
- if options.block
- % The current implementation needs the Jacobian of the full model, which is not
- % provided by the block-decomposed routines.
- error('The ''block'' option is not compatible with ''ramsey_model''/''ramsey_policy''');
- end
if ~isfinite(M.params(strmatch('optimal_policy_discount_factor',M.param_names,'exact')))
fprintf('\nevaluate_steady_state: the planner_discount is NaN/Inf. That will cause problems.\n')
end
diff --git a/matlab/model_diagnostics.m b/matlab/model_diagnostics.m
index 3abbb06c0..8336767ef 100644
--- a/matlab/model_diagnostics.m
+++ b/matlab/model_diagnostics.m
@@ -256,51 +256,46 @@ iyr0 = find(iyv) ;
it_ = M.maximum_lag + 1;
z = repmat(dr.ys,1,klen);
-if ~options.block
- if options.order == 1
- if (options.bytecode)
- [~, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ...
- M.params, dr.ys, 1);
- jacobia_ = [loc_dr.g1 loc_dr.g1_x loc_dr.g1_xd];
- else
- [~,jacobia_] = feval([M.fname '.dynamic'],z(iyr0),exo_simul, ...
- M.params, dr.ys, it_);
- end
- elseif options.order >= 2
- if (options.bytecode)
- [~, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ...
- M.params, dr.ys, 1);
- jacobia_ = [loc_dr.g1 loc_dr.g1_x];
- else
- [~,jacobia_,hessian1] = feval([M.fname '.dynamic'],z(iyr0),...
- exo_simul, ...
- M.params, dr.ys, it_);
- end
+if options.order == 1
+ if (options.bytecode)
+ [~, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ...
+ M.params, dr.ys, 1);
+ jacobia_ = [loc_dr.g1 loc_dr.g1_x loc_dr.g1_xd];
+ else
+ [~,jacobia_] = feval([M.fname '.dynamic'],z(iyr0),exo_simul, ...
+ M.params, dr.ys, it_);
end
+elseif options.order >= 2
+ if (options.bytecode)
+ [~, loc_dr] = bytecode('dynamic','evaluate', z,exo_simul, ...
+ M.params, dr.ys, 1);
+ jacobia_ = [loc_dr.g1 loc_dr.g1_x];
+ else
+ [~,jacobia_,hessian1] = feval([M.fname '.dynamic'],z(iyr0),...
+ exo_simul, ...
+ M.params, dr.ys, it_);
+ end
+end
- if any(any(isinf(jacobia_) | isnan(jacobia_)))
+if any(any(isinf(jacobia_) | isnan(jacobia_)))
+ problem_dummy=1;
+ [infrow,infcol]=find(isinf(jacobia_) | isnan(jacobia_));
+ fprintf('\nMODEL_DIAGNOSTICS: The Jacobian of the dynamic model contains Inf or NaN. The problem arises from: \n\n')
+ display_problematic_vars_Jacobian(infrow,infcol,M,dr.ys,'dynamic','MODEL_DIAGNOSTICS: ')
+end
+if any(any(~isreal(jacobia_)))
+ [imagrow,imagcol]=find(abs(imag(jacobia_))>1e-15);
+ if ~isempty(imagrow)
problem_dummy=1;
- [infrow,infcol]=find(isinf(jacobia_) | isnan(jacobia_));
- fprintf('\nMODEL_DIAGNOSTICS: The Jacobian of the dynamic model contains Inf or NaN. The problem arises from: \n\n')
- display_problematic_vars_Jacobian(infrow,infcol,M,dr.ys,'dynamic','MODEL_DIAGNOSTICS: ')
+ fprintf('\nMODEL_DIAGNOSTICS: The Jacobian of the dynamic model contains imaginary parts. The problem arises from: \n\n')
+ display_problematic_vars_Jacobian(imagrow,imagcol,M,dr.ys,'dynamic','MODEL_DIAGNOSTICS: ')
end
- if any(any(~isreal(jacobia_)))
- [imagrow,imagcol]=find(abs(imag(jacobia_))>1e-15);
- if ~isempty(imagrow)
- problem_dummy=1;
- fprintf('\nMODEL_DIAGNOSTICS: The Jacobian of the dynamic model contains imaginary parts. The problem arises from: \n\n')
- display_problematic_vars_Jacobian(imagrow,imagcol,M,dr.ys,'dynamic','MODEL_DIAGNOSTICS: ')
- end
+end
+if exist('hessian1','var')
+ if any(any(isinf(hessian1) | isnan(hessian1)))
+ problem_dummy=1;
+ fprintf('\nMODEL_DIAGNOSTICS: The Hessian of the dynamic model contains Inf or NaN.\n')
end
- if exist('hessian1','var')
- if any(any(isinf(hessian1) | isnan(hessian1)))
- problem_dummy=1;
- fprintf('\nMODEL_DIAGNOSTICS: The Hessian of the dynamic model contains Inf or NaN.\n')
- end
- end
-else
- fprintf('\nMODEL_DIAGNOSTICS: This command currently does not support the block option for checking.\n')
- fprintf('\nMODEL_DIAGNOSTICS: the dynamic model. You may want to disable it for doing model_diagnostics. Skipping this part.\n')
end
if problem_dummy==0
diff --git a/matlab/perfect-foresight-models/solve_block_decomposed_problem.m b/matlab/perfect-foresight-models/solve_block_decomposed_problem.m
index 79a74aa5b..85989f652 100644
--- a/matlab/perfect-foresight-models/solve_block_decomposed_problem.m
+++ b/matlab/perfect-foresight-models/solve_block_decomposed_problem.m
@@ -87,7 +87,7 @@ for blk = 1:length(M_.block_structure.block)
[y, T, oo_] = solve_one_boundary(fh_dynamic, y, oo_.exo_simul, M_.params, oo_.steady_state, T, y_index, M_.block_structure.block(blk).NNZDerivatives, options_.periods, M_.block_structure.block(blk).is_linear, blk, M_.maximum_lag, options_.simul.maxit, options_.dynatol.f, cutoff, options_.stack_solve_algo, is_forward, true, false, M_, options_, oo_);
elseif M_.block_structure.block(blk).Simulation_Type == 5 || ... % solveTwoBoundariesSimple
M_.block_structure.block(blk).Simulation_Type == 8 % solveTwoBoundariesComplete
- [y, T, oo_] = solve_two_boundaries(fh_dynamic, y, oo_.exo_simul, M_.params, oo_.steady_state, T, y_index, M_.block_structure.block(blk).NNZDerivatives, options_.periods, M_.block_structure.block(blk).maximum_lag, M_.block_structure.block(blk).maximum_lead, M_.block_structure.block(blk).is_linear, blk, M_.maximum_lag, options_.simul.maxit, options_.dynatol.f, cutoff, options_.stack_solve_algo, options_, M_, oo_);
+ [y, T, oo_] = solve_two_boundaries(fh_dynamic, y, oo_.exo_simul, M_.params, oo_.steady_state, T, y_index, M_.block_structure.block(blk).NNZDerivatives, options_.periods, M_.block_structure.block(blk).is_linear, blk, M_.maximum_lag, options_.simul.maxit, options_.dynatol.f, cutoff, options_.stack_solve_algo, options_, M_, oo_);
end
tmp = y(M_.block_structure.block(blk).variable, :);
diff --git a/matlab/resol.m b/matlab/resol.m
index 805ac3d46..6c3fd30e5 100644
--- a/matlab/resol.m
+++ b/matlab/resol.m
@@ -32,7 +32,7 @@ function [dr, info, M, oo] = resol(check_flag, M, options, oo)
% info(1)=24 -> M_.params has been updated in the steadystate routine and has some NaNs.
% info(1)=30 -> Ergodic variance can't be computed.
-% Copyright © 2001-2018 Dynare Team
+% Copyright © 2001-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -111,11 +111,5 @@ if options.loglinear
end
end
-if options.block
- [dr,info,M,oo] = dr_block(dr,check_flag,M,options,oo);
- dr.edim = nnz(abs(dr.eigval) > options.qz_criterium);
- dr.sdim = dr.nd-dr.edim;
-else
- [dr,info] = stochastic_solvers(dr,check_flag,M,options,oo);
-end
+[dr, info] = stochastic_solvers(dr, check_flag, M, options, oo);
oo.dr = dr;
diff --git a/matlab/set_state_space.m b/matlab/set_state_space.m
index f3fe17dae..e442a2c16 100644
--- a/matlab/set_state_space.m
+++ b/matlab/set_state_space.m
@@ -34,7 +34,7 @@ function dr=set_state_space(dr,DynareModel,DynareOptions)
%! @end deftypefn
%@eod:
-% Copyright © 1996-2017 Dynare Team
+% Copyright © 1996-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -69,11 +69,7 @@ else
both_var = [];
stat_var = setdiff([1:endo_nbr]',fwrd_var);
end
-if DynareOptions.block
- order_var = DynareModel.block_structure.variable_reordered;
-else
- order_var = [ stat_var(:); pred_var(:); both_var(:); fwrd_var(:)];
-end
+order_var = [ stat_var(:); pred_var(:); both_var(:); fwrd_var(:)];
inv_order_var(order_var) = (1:endo_nbr);
% building kmask for z state vector in t+1
diff --git a/matlab/simult_.m b/matlab/simult_.m
index df98de1f3..37ac55d2e 100644
--- a/matlab/simult_.m
+++ b/matlab/simult_.m
@@ -17,7 +17,7 @@ function y_=simult_(M_,options_,y0,dr,ex_,iorder)
% SPECIAL REQUIREMENTS
% none
-% Copyright © 2001-2021 Dynare Team
+% Copyright © 2001-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -63,19 +63,9 @@ if options_.k_order_solver && ~options_.pruning % Call dynare++ routines.
y_(dr.order_var,:) = y_;
y_=[y_start y_];
else
- if options_.block
- if M_.maximum_lag > 0
- k2 = dr.state_var;
- else
- k2 = [];
- end
- order_var = 1:endo_nbr;
- dr.order_var = order_var;
- else
- k2 = dr.kstate(find(dr.kstate(:,2) <= M_.maximum_lag+1),[1 2]);
- k2 = k2(:,1)+(M_.maximum_lag+1-k2(:,2))*endo_nbr;
- order_var = dr.order_var;
- end
+ k2 = dr.kstate(find(dr.kstate(:,2) <= M_.maximum_lag+1),[1 2]);
+ k2 = k2(:,1)+(M_.maximum_lag+1-k2(:,2))*endo_nbr;
+ order_var = dr.order_var;
switch iorder
case 1
diff --git a/matlab/solve_two_boundaries.m b/matlab/solve_two_boundaries.m
index 946e34e6f..7ad7a4f40 100644
--- a/matlab/solve_two_boundaries.m
+++ b/matlab/solve_two_boundaries.m
@@ -1,4 +1,4 @@
-function [y, T, oo]= solve_two_boundaries(fh, y, x, params, steady_state, T, y_index, nze, periods, y_kmin_l, y_kmax_l, is_linear, Block_Num, y_kmin, maxit_, solve_tolf, cutoff, stack_solve_algo,options,M, oo)
+function [y, T, oo]= solve_two_boundaries(fh, y, x, params, steady_state, T, y_index, nze, periods, is_linear, Block_Num, y_kmin, maxit_, solve_tolf, cutoff, stack_solve_algo,options,M, oo)
% Computes the deterministic simulation of a block of equation containing
% both lead and lag variables using relaxation methods
%
@@ -14,8 +14,6 @@ function [y, T, oo]= solve_two_boundaries(fh, y, x, params, steady_state, T, y_i
% nze [integer] number of non-zero elements in the
% jacobian matrix
% periods [integer] number of simulation periods
-% y_kmin_l [integer] maximum number of lag in the block
-% y_kmax_l [integer] maximum number of lead in the block
% is_linear [logical] Whether the block is linear
% Block_Num [integer] block number
% y_kmin [integer] maximum number of lag in the model
diff --git a/matlab/th_autocovariances.m b/matlab/th_autocovariances.m
index e18f66982..3a01d9be4 100644
--- a/matlab/th_autocovariances.m
+++ b/matlab/th_autocovariances.m
@@ -42,7 +42,7 @@ function [Gamma_y,stationary_vars] = th_autocovariances(dr,ivar,M_,options_,node
% E(x_t) = (I - {g_x}\right)^{- 1} 0.5\left( g_{\sigma\sigma} \sigma^2 + g_{xx} Var(\hat x_t) + g_{uu} Var(u_t) \right)
% \]
%
-% Copyright © 2001-2020 Dynare Team
+% Copyright © 2001-2023 Dynare Team
%
% This file is part of Dynare.
%
@@ -92,42 +92,32 @@ nspred = M_.nspred;
nstatic = M_.nstatic;
nx = size(ghx,2);
-if ~options_.block
- %order_var = dr.order_var;
- inv_order_var = dr.inv_order_var;
- kstate = dr.kstate;
- ikx = [nstatic+1:nstatic+nspred];
- k0 = kstate(find(kstate(:,2) <= M_.maximum_lag+1),:);
- i0 = find(k0(:,2) == M_.maximum_lag+1);
- i00 = i0;
- n0 = length(i0);
- AS = ghx(:,i0);
- ghu1 = zeros(nx,M_.exo_nbr);
- ghu1(i0,:) = ghu(ikx,:);
- for i=M_.maximum_lag:-1:2
- i1 = find(k0(:,2) == i);
- n1 = size(i1,1);
- j1 = zeros(n1,1);
- for k1 = 1:n1
- j1(k1) = find(k0(i00,1)==k0(i1(k1),1));
- end
- AS(:,j1) = AS(:,j1)+ghx(:,i1);
- i0 = i1;
+
+inv_order_var = dr.inv_order_var;
+kstate = dr.kstate;
+ikx = [nstatic+1:nstatic+nspred];
+k0 = kstate(find(kstate(:,2) <= M_.maximum_lag+1),:);
+i0 = find(k0(:,2) == M_.maximum_lag+1);
+i00 = i0;
+n0 = length(i0);
+AS = ghx(:,i0);
+ghu1 = zeros(nx,M_.exo_nbr);
+ghu1(i0,:) = ghu(ikx,:);
+for i=M_.maximum_lag:-1:2
+ i1 = find(k0(:,2) == i);
+ n1 = size(i1,1);
+ j1 = zeros(n1,1);
+ for k1 = 1:n1
+ j1(k1) = find(k0(i00,1)==k0(i1(k1),1));
end
-else
- ghu1 = zeros(nx,M_.exo_nbr);
- trend = 1:M_.endo_nbr;
- inv_order_var = trend(M_.block_structure.variable_reordered);
- ghu1(1:length(dr.state_var),:) = ghu(dr.state_var,:);
+ AS(:,j1) = AS(:,j1)+ghx(:,i1);
+ i0 = i1;
end
b = ghu1*M_.Sigma_e*ghu1';
-if ~options_.block
- ipred = nstatic+(1:nspred)';
-else
- ipred = dr.state_var;
-end
+ipred = nstatic+(1:nspred)';
+
% state space representation for state variables only
[A,B] = kalman_transition_matrix(dr,ipred,1:nx,M_.exo_nbr);
% Compute stationary variables (before HP filtering),
@@ -135,11 +125,7 @@ end
% HP filtering, this mean correction is computed *before* filtering)
if local_order == 2 || options_.hp_filter == 0
[vx, u] = lyapunov_symm(A,B*M_.Sigma_e*B',options_.lyapunov_fixed_point_tol,options_.qz_criterium,options_.lyapunov_complex_threshold,[],options_.debug);
- if ~options_.block
- iky = inv_order_var(ivar);
- else
- iky = ivar;
- end
+ iky = inv_order_var(ivar);
stationary_vars = (1:length(ivar))';
if ~isempty(u)
x = abs(ghx*u);
diff --git a/mex/build/block_kalman_filter.am b/mex/build/block_kalman_filter.am
deleted file mode 100644
index eaf08bde2..000000000
--- a/mex/build/block_kalman_filter.am
+++ /dev/null
@@ -1,15 +0,0 @@
-mex_PROGRAMS = block_kalman_filter
-
-TOPDIR = $(top_srcdir)/../../sources/block_kalman_filter
-
-block_kalman_filter_CPPFLAGS = $(AM_CPPFLAGS) -I$(TOPDIR)
-block_kalman_filter_CXXFLAGS = $(AM_CXXFLAGS) -fopenmp
-block_kalman_filter_LDFLAGS = $(AM_LDFLAGS) $(OPENMP_LDFLAGS)
-
-nodist_block_kalman_filter_SOURCES = block_kalman_filter.cc
-
-BUILT_SOURCES = $(nodist_block_kalman_filter_SOURCES)
-CLEANFILES = $(nodist_block_kalman_filter_SOURCES)
-
-%.cc: $(TOPDIR)/%.cc
- $(LN_S) -f $< $@
diff --git a/mex/build/matlab/Makefile.am b/mex/build/matlab/Makefile.am
index 9ffb4102b..54a7dc1f6 100644
--- a/mex/build/matlab/Makefile.am
+++ b/mex/build/matlab/Makefile.am
@@ -1,6 +1,6 @@
ACLOCAL_AMFLAGS = -I ../../../m4
-SUBDIRS = mjdgges kronecker bytecode block_kalman_filter sobol perfect_foresight_problem num_procs block_trust_region disclyap_fast libkordersim local_state_space_iterations folded_to_unfolded_dr k_order_simul k_order_mean cycle_reduction logarithmic_reduction riccati_update
+SUBDIRS = mjdgges kronecker bytecode sobol perfect_foresight_problem num_procs block_trust_region disclyap_fast libkordersim local_state_space_iterations folded_to_unfolded_dr k_order_simul k_order_mean cycle_reduction logarithmic_reduction riccati_update
# libdynare++ must come before gensylv and k_order_perturbation
if ENABLE_MEX_DYNAREPLUSPLUS
diff --git a/mex/build/matlab/block_kalman_filter/Makefile.am b/mex/build/matlab/block_kalman_filter/Makefile.am
deleted file mode 100644
index 1c603a9d8..000000000
--- a/mex/build/matlab/block_kalman_filter/Makefile.am
+++ /dev/null
@@ -1,2 +0,0 @@
-include ../mex.am
-include ../../block_kalman_filter.am
diff --git a/mex/build/matlab/configure.ac b/mex/build/matlab/configure.ac
index 901576c47..67fa9913e 100644
--- a/mex/build/matlab/configure.ac
+++ b/mex/build/matlab/configure.ac
@@ -157,7 +157,6 @@ AC_CONFIG_FILES([Makefile
k_order_welfare/Makefile
kalman_steady_state/Makefile
ms_sbvar/Makefile
- block_kalman_filter/Makefile
sobol/Makefile
local_state_space_iterations/Makefile
libkordersim/Makefile
diff --git a/mex/build/octave/Makefile.am b/mex/build/octave/Makefile.am
index 9f936fb2b..c89c9de0b 100644
--- a/mex/build/octave/Makefile.am
+++ b/mex/build/octave/Makefile.am
@@ -1,6 +1,6 @@
ACLOCAL_AMFLAGS = -I ../../../m4
-SUBDIRS = mjdgges kronecker bytecode block_kalman_filter sobol perfect_foresight_problem num_procs block_trust_region disclyap_fast libkordersim local_state_space_iterations folded_to_unfolded_dr k_order_simul k_order_mean cycle_reduction logarithmic_reduction riccati_update
+SUBDIRS = mjdgges kronecker bytecode sobol perfect_foresight_problem num_procs block_trust_region disclyap_fast libkordersim local_state_space_iterations folded_to_unfolded_dr k_order_simul k_order_mean cycle_reduction logarithmic_reduction riccati_update
# libdynare++ must come before gensylv and k_order_perturbation
if ENABLE_MEX_DYNAREPLUSPLUS
diff --git a/mex/build/octave/block_kalman_filter/Makefile.am b/mex/build/octave/block_kalman_filter/Makefile.am
deleted file mode 100644
index aa99d4233..000000000
--- a/mex/build/octave/block_kalman_filter/Makefile.am
+++ /dev/null
@@ -1,3 +0,0 @@
-EXEEXT = .mex
-include ../mex.am
-include ../../block_kalman_filter.am
diff --git a/mex/build/octave/configure.ac b/mex/build/octave/configure.ac
index 815f1decc..10a582e08 100644
--- a/mex/build/octave/configure.ac
+++ b/mex/build/octave/configure.ac
@@ -160,7 +160,6 @@ AC_CONFIG_FILES([Makefile
k_order_welfare/Makefile
kalman_steady_state/Makefile
ms_sbvar/Makefile
- block_kalman_filter/Makefile
sobol/Makefile
local_state_space_iterations/Makefile
libkordersim/Makefile
diff --git a/mex/sources/Makefile.am b/mex/sources/Makefile.am
index fc179768e..dc9ec6abd 100644
--- a/mex/sources/Makefile.am
+++ b/mex/sources/Makefile.am
@@ -14,7 +14,6 @@ EXTRA_DIST = \
k_order_welfare \
kalman_steady_state \
ms-sbvar \
- block_kalman_filter \
sobol \
local_state_space_iterations \
libkordersim \
diff --git a/mex/sources/block_kalman_filter/block_kalman_filter.cc b/mex/sources/block_kalman_filter/block_kalman_filter.cc
deleted file mode 100644
index 536d8c140..000000000
--- a/mex/sources/block_kalman_filter/block_kalman_filter.cc
+++ /dev/null
@@ -1,850 +0,0 @@
-/*
- * Copyright © 2007-2022 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 .
- */
-
-#include
-#include
-#include
-
-#include "block_kalman_filter.hh"
-
-#define BLAS
-//#define CUBLAS
-
-#ifdef CUBLAS
-# include
-# include
-#endif
-
-void
-mexDisp(const mxArray *P)
-{
- size_t n = mxGetN(P);
- size_t m = mxGetM(P);
- const double *M = mxGetPr(P);
- mexPrintf("%d x %d\n", m, n);
- mexEvalString("drawnow;");
- for (size_t i = 0; i < m; i++)
- {
- for (size_t j = 0; j < n; j++)
- mexPrintf(" %9.4f", M[i+ j * m]);
- mexPrintf("\n");
- }
- mexEvalString("drawnow;");
-}
-
-void
-mexDisp(const double *M, int m, int n)
-{
- mexPrintf("%d x %d\n", m, n);
- mexEvalString("drawnow;");
- for (int i = 0; i < m; i++)
- {
- for (int j = 0; j < n; j++)
- mexPrintf(" %9.4f", M[i+ j * m]);
- mexPrintf("\n");
- }
- mexEvalString("drawnow;");
-}
-
-/*if block
- %nz_state_var = M_.nz_state_var;
- while notsteady && t riccati_tol;
- oldK = K(:);
- end
- end;
- else
- while notsteady && t riccati_tol;
- oldK = K(:);
-
- end
- end
- end
-*/
-
-bool
-not_all_abs_F_bellow_crit(const double *F, int size, double crit)
-{
- int i = 0;
- while (i < size && abs(F[i]) < crit)
- i++;
-
- if (i < size)
- return false;
- else
- return true;
-}
-
-double
-det(const double *F, int dim, const lapack_int *ipiv)
-{
- double det = 1.0;
- for (int i = 0; i < dim; i++)
- if (ipiv[i] - 1 == i)
- det *= F[i + i * dim];
- else
- det *= -F[i + i * dim];
- return det;
-}
-
-BlockKalmanFilter::BlockKalmanFilter(int nrhs, const mxArray *prhs[])
-{
- if (nrhs != 13 && nrhs != 16)
- mexErrMsgTxt("block_kalman_filter requires exactly \n 13 input arguments for standard Kalman filter \nor\n 16 input arguments for missing observations Kalman filter.");
- if (nrhs == 16)
- missing_observations = true;
- else
- missing_observations = false;
- if (missing_observations)
- {
- if (!mxIsCell(prhs[0]))
- mexErrMsgTxt("the first input argument of block_missing_observations_kalman_filter must be a Cell Array.");
- pdata_index = prhs[0];
- if (!mxIsDouble(prhs[1]))
- mexErrMsgTxt("the second input argument of block_missing_observations_kalman_filter must be a scalar.");
- number_of_observations = ceil(mxGetScalar(prhs[1]));
- if (!mxIsDouble(prhs[2]))
- mexErrMsgTxt("the third input argument of block_missing_observations_kalman_filter must be a scalar.");
- no_more_missing_observations = ceil(mxGetScalar(prhs[2]));
- pT = mxDuplicateArray(prhs[3]);
- pR = mxDuplicateArray(prhs[4]);
- pQ = mxDuplicateArray(prhs[5]);
- pH = mxDuplicateArray(prhs[6]);
- pP = mxDuplicateArray(prhs[7]);
- pY = mxDuplicateArray(prhs[8]);
- start = mxGetScalar(prhs[9]);
- mfd = mxGetPr(prhs[10]);
- kalman_tol = mxGetScalar(prhs[11]);
- riccati_tol = mxGetScalar(prhs[12]);
- nz_state_var = mxGetPr(prhs[13]);
- n_diag = mxGetScalar(prhs[14]);
- pure_obs = mxGetScalar(prhs[15]);
- }
- else
- {
- no_more_missing_observations = 0;
- pT = mxDuplicateArray(prhs[0]);
- pR = mxDuplicateArray(prhs[1]);
- pQ = mxDuplicateArray(prhs[2]);
- pH = mxDuplicateArray(prhs[3]);
- pP = mxDuplicateArray(prhs[4]);
- pY = mxDuplicateArray(prhs[5]);
- start = mxGetScalar(prhs[6]);
- /*Defining the initials values*/
- n = mxGetN(pT); // Number of state variables.
- pp = mxGetM(pY); // Maximum number of observed variables.
- smpl = mxGetN(pY); // Sample size. ;
- mfd = mxGetPr(prhs[7]);
- kalman_tol = mxGetScalar(prhs[8]);
- riccati_tol = mxGetScalar(prhs[9]);
- nz_state_var = mxGetPr(prhs[10]);
- n_diag = mxGetScalar(prhs[11]);
- pure_obs = mxGetScalar(prhs[12]);
- }
- T = mxGetPr(pT);
- R = mxGetPr(pR);
- Q = mxGetPr(pQ);
- H = mxGetPr(pH);
- P = mxGetPr(pP);
- Y = mxGetPr(pY);
-
- n = mxGetN(pT); // Number of state variables.
- pp = mxGetM(pY); // Maximum number of observed variables.
- smpl = mxGetN(pY); // Sample size. ;
- n_state = n - pure_obs;
-
- /*mexPrintf("T\n");
- mexDisp(pT);*/
-
- H_size = mxGetN(pH) * mxGetM(pH);
-
- n_shocks = mxGetM(pQ);
-
- if (missing_observations)
- if (mxGetNumberOfElements(pdata_index) != static_cast(smpl))
- mexErrMsgTxt("the number of element in the cell array passed to block_missing_observation_kalman_filter as first argument has to be equal to the smpl size");
-
- i_nz_state_var = std::make_unique(n);
- for (int i = 0; i < n; i++)
- i_nz_state_var[i] = nz_state_var[i];
-
- pa = mxCreateDoubleMatrix(n, 1, mxREAL); // State vector.
- a = mxGetPr(pa);
- tmp_a = std::make_unique(n);
- dF = 0.0; // det(F).
-
- p_tmp1 = mxCreateDoubleMatrix(n, n_shocks, mxREAL);
- tmp1 = mxGetPr(p_tmp1);
- t = 0; // Initialization of the time index.
- plik = mxCreateDoubleMatrix(smpl, 1, mxREAL);
- lik = mxGetPr(plik);
- Inf = mxGetInf();
- LIK = 0.0; // Default value of the log likelihood.
- notsteady = true; // Steady state flag.
- F_singular = true;
- v_pp = std::make_unique(pp);
- v_n = std::make_unique(n);
- mf = std::make_unique(pp);
- for (int i = 0; i < pp; i++)
- mf[i] = mfd[i] - 1;
-
- /*compute QQ = R*Q*transpose(R)*/ // Variance of R times the vector of structural innovations.;
- // tmp = R * Q;
- for (int i = 0; i < n; i++)
- for (int j = 0; j < n_shocks; j++)
- {
- double res = 0.0;
- for (int k = 0; k < n_shocks; k++)
- res += R[i + k * n] * Q[j * n_shocks + k];
- tmp1[i + j * n] = res;
- }
-
- // QQ = tmp * transpose(R)
- pQQ = mxCreateDoubleMatrix(n, n, mxREAL);
- QQ = mxGetPr(pQQ);
- for (int i = 0; i < n; i++)
- for (int j = i; j < n; j++)
- {
- double res = 0.0;
- for (int k = 0; k < n_shocks; k++)
- res += tmp1[i + k * n] * R[k * n + j];
- QQ[i + j * n] = QQ[j + i * n] = res;
- }
- mxDestroyArray(p_tmp1);
-
- pv = mxCreateDoubleMatrix(pp, 1, mxREAL);
- v = mxGetPr(pv);
- pF = mxCreateDoubleMatrix(pp, pp, mxREAL);
- F = mxGetPr(pF);
- piF = mxCreateDoubleMatrix(pp, pp, mxREAL);
- iF = mxGetPr(piF);
- lw = pp * 4;
- w = std::make_unique(lw);
- iw = std::make_unique(pp);
- ipiv = std::make_unique(pp);
- info = 0;
-#if defined(BLAS) || defined(CUBLAS)
- p_tmp = mxCreateDoubleMatrix(n, n, mxREAL);
- tmp = mxGetPr(p_tmp);
- p_P_t_t1 = mxCreateDoubleMatrix(n, n, mxREAL);
- P_t_t1 = mxGetPr(p_P_t_t1);
- pK = mxCreateDoubleMatrix(n, n, mxREAL);
- K = mxGetPr(pK);
- p_K_P = mxCreateDoubleMatrix(n, n, mxREAL);
- K_P = mxGetPr(p_K_P);
- oldK = std::make_unique(n * n);
- P_mf = std::make_unique(n * n);
- for (int i = 0; i < n * n; i++)
- oldK[i] = Inf;
-#else
- p_tmp = mxCreateDoubleMatrix(n, n_state, mxREAL);
- tmp = mxGetPr(p_tmp);
- p_P_t_t1 = mxCreateDoubleMatrix(n_state, n_state, mxREAL);
- P_t_t1 = mxGetPr(p_P_t_t1);
- pK = mxCreateDoubleMatrix(n, pp, mxREAL);
- K = mxGetPr(pK);
- p_K_P = mxCreateDoubleMatrix(n_state, n_state, mxREAL);
- K_P = mxGetPr(p_K_P);
- oldK = std::make_unique(n * pp);
- P_mf = std::make_unique(n * pp);
- for (int i = 0; i < n * pp; i++)
- oldK[i] = Inf;
-#endif
-}
-
-void
-BlockKalmanFilter::block_kalman_filter_ss()
-{
- if (t+1 < smpl)
- while (t < smpl)
- {
- //v = Y(:,t)-a(mf);
- for (int i = 0; i < pp; i++)
- v[i] = Y[i + t * pp] - a[mf[i]];
-
- //a = T*(a+K*v);
- for (int i = pure_obs; i < n; i++)
- {
- double res = 0.0;
- for (int j = 0; j < pp; j++)
- res += K[j * n + i] * v[j];
- v_n[i] = res + a[i];
- }
- for (int i = 0; i < n; i++)
- {
- double res = 0.0;
- for (int j = pure_obs; j < n; j++)
- res += T[j * n + i] * v_n[j];
- a[i] = res;
- }
-
- //lik(t) = transpose(v)*iF*v;
- for (int i = 0; i < pp; i++)
- {
- double res = 0.0;
- for (int j = 0; j < pp; j++)
- res += v[j] * iF[j * pp + i];
- v_pp[i] = res;
- }
- double res = 0.0;
- for (int i = 0; i < pp; i++)
- res += v_pp[i] * v[i];
-
- lik[t] = (log(dF) + res + pp * log(2.0*M_PI))/2;
- if (t + 1 > start)
- LIK += lik[t];
-
- t++;
- }
-}
-
-bool
-BlockKalmanFilter::block_kalman_filter(int nlhs, mxArray *plhs[])
-{
- while (notsteady && t < smpl)
- {
- if (missing_observations)
- {
- // retrieve the d_index
- pd_index = mxGetCell(pdata_index, t);
- dd_index = mxGetPr(pd_index);
- size_d_index = mxGetM(pd_index);
- d_index.resize(size_d_index);
- for (int i = 0; i < size_d_index; i++)
- d_index[i] = ceil(dd_index[i]) - 1;
-
- //v = Y(:,t) - a(mf)
- int i_i = 0;
- //#pragma omp parallel for shared(v, i_i, d_index)
- for (auto i = d_index.begin(); i != d_index.end(); i++)
- {
- //mexPrintf("i_i=%d, omp_get_max_threads()=%d\n",i_i,omp_get_max_threads());
- v[i_i] = Y[*i + t * pp] - a[mf[*i]];
- i_i++;
- }
-
- //F = P(mf,mf) + H;
- i_i = 0;
- if (H_size == 1)
- //#pragma omp parallel for shared(iF, F, i_i)
- for (auto i = d_index.begin(); i != d_index.end(); i++, i_i++)
- {
- int j_j = 0;
- for (auto j = d_index.begin(); j != d_index.end(); j++, j_j++)
- iF[i_i + j_j * size_d_index] = F[i_i + j_j * size_d_index] = P[mf[*i] + mf[*j] * n] + H[0];
- }
- else
- //#pragma omp parallel for shared(iF, F, P, H, mf, i_i)
- for (auto i = d_index.begin(); i != d_index.end(); i++, i_i++)
- {
- int j_j = 0;
- for (auto j = d_index.begin(); j != d_index.end(); j++, j_j++)
- iF[i_i + j_j * size_d_index] = F[i_i + j_j * size_d_index] = P[mf[*i] + mf[*j] * n] + H[*i + *j * pp];
- }
- }
- else
- {
- size_d_index = pp;
-
- //v = Y(:,t) - a(mf)
- for (int i = 0; i < pp; i++)
- v[i] = Y[i + t * pp] - a[mf[i]];
-
- //F = P(mf,mf) + H;
- if (H_size == 1)
- for (int i = 0; i < pp; i++)
- for (int j = 0; j < pp; j++)
- iF[i + j * pp] = F[i + j * pp] = P[mf[i] + mf[j] * n] + H[0];
- else
- for (int i = 0; i < pp; i++)
- for (int j = 0; j < pp; j++)
- iF[i + j * pp] = F[i + j * pp] = P[mf[i] + mf[j] * n] + H[i + j * pp];
- }
-
- /* Computes the norm of iF */
- double anorm = dlange("1", &size_d_index, &size_d_index, iF, &size_d_index, w.get());
- //mexPrintf("anorm = %f\n",anorm);
-
- /* Modifies F in place with a LU decomposition */
- dgetrf(&size_d_index, &size_d_index, iF, &size_d_index, ipiv.get(), &info);
- if (info != 0)
- mexPrintf("dgetrf failure with error %d\n", static_cast(info));
-
- /* Computes the reciprocal norm */
- dgecon("1", &size_d_index, iF, &size_d_index, &anorm, &rcond, w.get(), iw.get(), &info);
- if (info != 0)
- mexPrintf("dgecon failure with error %d\n", static_cast(info));
-
- if (rcond < kalman_tol)
- if (not_all_abs_F_bellow_crit(F, size_d_index * size_d_index, kalman_tol)) //~all(abs(F(:))(info));
-
- //lik(t) = log(dF)+transpose(v)*iF*v;
-#pragma omp parallel for shared(v_pp)
- for (int i = 0; i < size_d_index; i++)
- {
- double res = 0.0;
- for (int j = 0; j < size_d_index; j++)
- res += v[j] * iF[j * size_d_index + i];
- v_pp[i] = res;
- }
- double res = 0.0;
- for (int i = 0; i < size_d_index; i++)
- res += v_pp[i] * v[i];
-
- lik[t] = (log(dF) + res + size_d_index * log(2.0*M_PI))/2;
- if (t + 1 >= start)
- LIK += lik[t];
-
- if (missing_observations)
- //K = P(:,mf)*iF;
-#pragma omp parallel for shared(P_mf)
- for (int i = 0; i < n; i++)
- {
- int j_j = 0;
- //for (int j = 0; j < pp; j++)
- for (auto j = d_index.begin(); j != d_index.end(); j++, j_j++)
- P_mf[i + j_j * n] = P[i + mf[*j] * n];
- }
- else
- //K = P(:,mf)*iF;
- for (int i = 0; i < n; i++)
- for (int j = 0; j < pp; j++)
- P_mf[i + j * n] = P[i + mf[j] * n];
-
-#pragma omp parallel for shared(K)
- for (int i = 0; i < n; i++)
- for (int j = 0; j < size_d_index; j++)
- {
- double res = 0.0;
- int j_pp = j * size_d_index;
- for (int k = 0; k < size_d_index; k++)
- res += P_mf[i + k * n] * iF[j_pp + k];
- K[i + j * n] = res;
- }
-
- //a = T*(a+K*v);
-#pragma omp parallel for shared(v_n)
- for (int i = pure_obs; i < n; i++)
- {
- double res = 0.0;
- for (int j = 0; j < size_d_index; j++)
- res += K[j * n + i] * v[j];
- v_n[i] = res + a[i];
- }
-
-#pragma omp parallel for shared(a)
- for (int i = 0; i < n; i++)
- {
- double res = 0.0;
- for (int j = pure_obs; j < n; j++)
- res += T[j * n + i] * v_n[j];
- a[i] = res;
- }
-
- if (missing_observations)
- {
- //P = T*(P-K*P(mf,:))*transpose(T)+QQ;
- int i_i = 0;
- //#pragma omp parallel for shared(P_mf)
- for (auto i = d_index.begin(); i != d_index.end(); i++, i_i++)
- for (int j = pure_obs; j < n; j++)
- P_mf[i_i + j * size_d_index] = P[mf[*i] + j * n];
- }
- else
- //P = T*(P-K*P(mf,:))*transpose(T)+QQ;
-#pragma omp parallel for shared(P_mf)
- for (int i = 0; i < pp; i++)
- for (int j = pure_obs; j < n; j++)
- P_mf[i + j * pp] = P[mf[i] + j * n];
-
-#ifdef BLAS
-# pragma omp parallel for shared(K_P)
- for (int i = 0; i < n; i++)
- for (int j = i; j < n; j++)
- {
- double res = 0.0;
- //int j_pp = j * pp;
- for (int k = 0; k < size_d_index; k++)
- res += K[i + k * n] * P_mf[k + j * size_d_index];
- K_P[i * n + j] = K_P[j * n + i] = res;
- }
- //#pragma omp parallel for shared(P, K_P, P_t_t1)
- for (int i = size_d_index; i < n; i++)
- for (int j = i; j < n; j++)
- {
- unsigned int k = i * n + j;
- P_t_t1[j * n + i] = P_t_t1[k] = P[k] - K_P[k];
- }
- double one = 1.0;
- double zero = 0.0;
- std::copy_n(QQ, n * n, P);
- blas_int n_b = n;
- /*mexPrintf("sizeof(n_b)=%d, n_b=%d, sizeof(n)=%d, n=%d\n",sizeof(n_b),n_b,sizeof(n),n);
- mexEvalString("drawnow;");*/
- dsymm("R", "U", &n_b, &n_b,
- &one, P_t_t1, &n_b,
- T, &n_b, &zero,
- tmp, &n_b);
- dgemm("N", "T", &n_b, &n_b,
- &n_b, &one, tmp, &n_b,
- T, &n_b, &one,
- P, &n_b);
-#else
-# ifdef CUBLAS
- for (int i = 0; i < n; i++)
- for (int j = i; j < n; j++)
- {
- double res = 0.0;
- //int j_pp = j * pp;
- for (int k = 0; k < size_d_index; k++)
- res += K[i + k * n] * P_mf[k + j * size_d_index];
- K_P[i * n + j] = K_P[j * n + i] = res;
- }
- //#pragma omp parallel for shared(P, K_P, P_t_t1)
- for (int i = size_d_index; i < n; i++)
- for (int j = i; j < n; j++)
- {
- unsigned int k = i * n + j;
- P_t_t1[j * n + i] = P_t_t1[k] = P[k] - K_P[k];
- }
- mexPrintf("CudaBLAS\n");
- mexEvalString("drawnow;");
- double one = 1.0;
- double zero = 0.0;
- cublasStatus_t status;
- cublasHandle_t handle;
- status = cublasCreate(&handle);
- if (status != CUBLAS_STATUS_SUCCESS)
- {
- mexPrintf("!!!! CUBLAS initialization error\n");
- return false;
- }
- /*int device;
- cudaGetDevice(&device);*/
- int n2 = n * n;
- double *d_A = nullptr, *d_B = nullptr, *d_C = nullptr, *d_D = nullptr;
- // Allocate device memory for the matrices
- if (cudaMalloc(static_cast(&d_A), n2 * sizeof(double)) != cudaSuccess)
- {
- mexPrintf("!!!! device memory allocation error (allocate A)\n");
- return false;
- }
- if (cudaMalloc(static_cast(&d_B), n2 * sizeof(d_B[0])) != cudaSuccess)
- {
- mexPrintf("!!!! device memory allocation error (allocate B)\n");
- return false;
- }
- if (cudaMalloc(static_cast(&d_C), n2 * sizeof(d_C[0])) != cudaSuccess)
- {
- mexPrintf("!!!! device memory allocation error (allocate C)\n");
- return false;
- }
- if (cudaMalloc(static_cast(&d_D), n2 * sizeof(d_D[0])) != cudaSuccess)
- {
- mexPrintf("!!!! device memory allocation error (allocate D)\n");
- return false;
- }
- // Initialize the device matrices with the host matrices
- status = cublasSetVector(n2, sizeof(P_t_t1[0]), P_t_t1, 1, d_A, 1);
- if (status != CUBLAS_STATUS_SUCCESS)
- {
- mexPrintf("!!!! device access error (write A)\n");
- return false;
- }
- status = cublasSetVector(n2, sizeof(T[0]), T, 1, d_B, 1);
- if (status != CUBLAS_STATUS_SUCCESS)
- {
- mexPrintf("!!!! device access error (write B)\n");
- return false;
- }
- status = cublasSetVector(n2, sizeof(tmp[0]), tmp, 1, d_C, 1);
- if (status != CUBLAS_STATUS_SUCCESS)
- {
- mexPrintf("!!!! device access error (write C)\n");
- return false;
- }
- mexPrintf("just before calling\n");
- mexEvalString("drawnow;");
- status = cublasSetVector(n2, sizeof(QQ[0]), QQ, 1, d_D, 1);
- if (status != CUBLAS_STATUS_SUCCESS)
- {
- mexPrintf("!!!! device access error (write D)\n");
- return false;
- }
-
- // Performs operation using plain C code
-
- cublasDsymm(handle, CUBLAS_SIDE_RIGHT, CUBLAS_FILL_MODE_UPPER, n, n,
- &one, d_A, n,
- d_B, n, &zero,
- d_C, n);
- /*dgemm("N", "T", &n_b, &n_b,
- &n_b, &one, tmp, &n_b,
- T, &n_b, &one,
- P, &n_b);*/
- cublasDgemm(handle, CUBLAS_OP_N, CUBLAS_OP_T, n, n,
- n, &one, d_C, n,
- d_B, n, &one,
- d_D, n);
- //double_symm(n, &one, h_A, h_B, &zero, h_C);
-
- status = cublasGetVector(n2, sizeof(P[0]), d_D, 1, P, 1);
- if (status != CUBLAS_STATUS_SUCCESS)
- {
- mexPrintf("!!!! device access error (read P)\n");
- return false;
- }
-
-# else
-# pragma omp parallel for shared(K_P)
- for (int i = pure_obs; i < n; i++)
- {
- unsigned int i1 = i - pure_obs;
- for (int j = i; j < n; j++)
- {
- unsigned int j1 = j - pure_obs;
- double res = 0.0;
- int j_pp = j * size_d_index;
- for (int k = 0; k < size_d_index; k++)
- res += K[i + k * n] * P_mf[k + j_pp];
- K_P[i1 * n_state + j1] = K_P[j1 * n_state + i1] = res;
- }
- }
-
-# pragma omp parallel for shared(P_t_t1)
- for (int i = pure_obs; i < n; i++)
- {
- unsigned int i1 = i - pure_obs;
- for (int j = i; j < n; j++)
- {
- unsigned int j1 = j - pure_obs;
- unsigned int k1 = i1 * n_state + j1;
- P_t_t1[j1 * n_state + i1] = P_t_t1[k1] = P[i * n + j] - K_P[k1];
- }
- }
-
- fill_n(tmp, 0, n * n_state);
-
-# pragma omp parallel for shared(tmp)
- for (int i = 0; i < n; i++)
- {
- int max_k = i_nz_state_var[i];
- for (int j = pure_obs; j < n; j++)
- {
- int j1 = j - pure_obs;
- int j1_n_state = j1 * n_state - pure_obs;
- int indx_tmp = i + j1 * n;
- for (int k = pure_obs; k < max_k; k++)
- tmp[indx_tmp] += T[i + k * n] * P_t_t1[k + j1_n_state];
- }
- }
-
- fill_n(P, 0, n * n);
-
- int n_n_obs = -n * pure_obs;
-# pragma omp parallel for shared(P)
- for (int i = 0; i < n; i++)
- {
- for (int j = i; j < n; j++)
- {
- int max_k = i_nz_state_var[j];
- int P_indx = i * n + j;
- for (int k = pure_obs; k < max_k; k++)
- {
- int k_n = k * n;
- P[P_indx] += tmp[i + k_n + n_n_obs] * T[j + k_n];
- }
- }
- }
-
-# pragma omp parallel for shared(P)
- for (int i = 0; i < n; i++)
- {
- for (int j = i; j < n; j++)
- P[j + i * n] += QQ[j + i * n];
- for (int j = i + 1; j < n; j++)
- P[i + j * n] = P[j + i * n];
- }
-# endif
-#endif
- if (t >= no_more_missing_observations)
- {
- double max_abs = 0.0;
- for (int i = 0; i < n * size_d_index; i++)
- {
- double res = abs(K[i] - oldK[i]);
- max_abs = std::max(res, max_abs);
- }
- notsteady = max_abs > riccati_tol;
-
- //oldK = K(:);
-
- std::copy_n(K, n * pp, oldK.get());
- }
- }
- t++;
- }
-
- if (F_singular)
- mexErrMsgTxt("The variance of the forecast error remains singular until the end of the sample\n");
- if (t < smpl)
- block_kalman_filter_ss();
- return true;
-}
-
-void
-BlockKalmanFilter::return_results_and_clean(int nlhs, mxArray *plhs[])
-{
- if (nlhs > 2)
- mexErrMsgTxt("block_kalman_filter provides at most 2 output argument.");
-
- if (nlhs >= 1)
- {
- plhs[0] = mxCreateDoubleMatrix(1, 1, mxREAL);
- double *pind = mxGetPr(plhs[0]);
- pind[0] = LIK;
- }
-
- if (nlhs == 2)
- plhs[1] = plik;
- else
- mxDestroyArray(plik);
-
- mxDestroyArray(pa);
- mxDestroyArray(p_tmp);
- mxDestroyArray(pQQ);
- mxDestroyArray(pv);
- mxDestroyArray(pF);
- mxDestroyArray(piF);
- mxDestroyArray(p_P_t_t1);
- mxDestroyArray(pK);
- mxDestroyArray(p_K_P);
-}
-
-void
-mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
-{
- BlockKalmanFilter block_kalman_filter(nrhs, prhs);
- if (block_kalman_filter.block_kalman_filter(nlhs, plhs))
- block_kalman_filter.return_results_and_clean(nlhs, plhs);
-}
diff --git a/mex/sources/block_kalman_filter/block_kalman_filter.hh b/mex/sources/block_kalman_filter/block_kalman_filter.hh
deleted file mode 100644
index 8687e92a7..000000000
--- a/mex/sources/block_kalman_filter/block_kalman_filter.hh
+++ /dev/null
@@ -1,68 +0,0 @@
-/*
- * Copyright © 2007-2022 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 .
- */
-
-#ifndef BLOCK_KALMAN_FILTER
-#define BLOCK_KALMAN_FILTER
-
-#ifndef DEBUG_EX
-# include
-#else
-# include "mex_interface.hh"
-#endif
-
-#include
-#include
-
-#include
-#include
-
-class BlockKalmanFilter
-{
-public:
- mxArray *pT, *pR, *pQ, *pH, *pP, *pY, *pQQ, *pv, *pF, *piF, *p_P_t_t1, *pK, *p_K_P;
- double *T, *R, *Q, *H, *Y, *mfd, *QQ, *v, *F, *iF;
- int start, pure_obs, smpl, n, n_state, n_shocks, H_size;
- double kalman_tol, riccati_tol, dF, LIK, Inf, pi;
- lapack_int pp, lw, info;
-
- double *nz_state_var;
- std::unique_ptr i_nz_state_var, mf;
- int n_diag, t;
- mxArray *M_;
- mxArray *pa, *p_tmp, *p_tmp1, *plik;
- std::unique_ptr tmp_a;
- double *tmp1, *lik;
- std::unique_ptr v_n, v_pp, w, oldK, P_mf;
- bool notsteady, F_singular, missing_observations;
- std::unique_ptr iw, ipiv;
- double anorm, rcond;
- lapack_int size_d_index;
- int no_more_missing_observations, number_of_observations;
- const mxArray *pdata_index;
- std::vector d_index;
- const mxArray *pd_index;
- double *dd_index;
- double *K, *a, *K_P, *P_t_t1, *tmp, *P;
-public:
- BlockKalmanFilter(int nrhs, const mxArray *prhs[]);
- bool block_kalman_filter(int nlhs, mxArray *plhs[]);
- void block_kalman_filter_ss();
- void return_results_and_clean(int nlhs, mxArray *plhs[]);
-};
-#endif
diff --git a/preprocessor b/preprocessor
index 35ac73fad..93b9ed695 160000
--- a/preprocessor
+++ b/preprocessor
@@ -1 +1 @@
-Subproject commit 35ac73fad8645302c10d0cd0068710ad3847366a
+Subproject commit 93b9ed69577246cb13eef1890d52708f2aea4a9d
diff --git a/tests/Makefile.am b/tests/Makefile.am
index 478a2e4a4..b6ed5b9ae 100644
--- a/tests/Makefile.am
+++ b/tests/Makefile.am
@@ -198,8 +198,6 @@ MODFILES = \
block_bytecode/lola_solve_one_boundary_mfs1.mod \
block_bytecode/lola_solve_one_boundary_mfs2.mod \
block_bytecode/lola_solve_one_boundary_mfs3.mod \
- block_bytecode/lola_stochastic.mod \
- block_bytecode/lola_stochastic_block.mod \
k_order_perturbation/fs2000k2a.mod \
k_order_perturbation/fs2000k2_use_dll.mod \
k_order_perturbation/fs2000k_1_use_dll.mod \
@@ -837,8 +835,6 @@ block_bytecode/lola_solve_one_boundary_mfs2.m.trs: block_bytecode/lola_solve_one
block_bytecode/lola_solve_one_boundary_mfs2.o.trs: block_bytecode/lola_solve_one_boundary.o.trs
block_bytecode/lola_solve_one_boundary_mfs3.m.trs: block_bytecode/lola_solve_one_boundary.m.trs
block_bytecode/lola_solve_one_boundary_mfs3.o.trs: block_bytecode/lola_solve_one_boundary.o.trs
-block_bytecode/lola_stochastic_block.m.trs: block_bytecode/lola_stochastic.m.trs
-block_bytecode/lola_stochastic_block.o.trs: block_bytecode/lola_stochastic.o.trs
histval_initval_file/ramst_initval_file.m.trs: histval_initval_file/ramst_initval_file_data.m.tls histval_initval_file/ramst_data_generate.m.trs
histval_initval_file/ramst_initval_file.o.trs: histval_initval_file/ramst_initval_file_data.o.tls histval_initval_file/ramst_data_generate.o.trs
diff --git a/tests/block_bytecode/lola_common.inc b/tests/block_bytecode/lola_common.inc
index ae582cea5..2c3bab87e 100644
--- a/tests/block_bytecode/lola_common.inc
+++ b/tests/block_bytecode/lola_common.inc
@@ -6,7 +6,6 @@
Macro-variables that can be defined to tune the computations:
- block
- mfs
- - deterministic
*/
load lola_data.mat
@@ -723,8 +722,6 @@ end;
resid;
steady(solve_algo=3);
-@#if deterministic
-
endval;
@#for i in wg
n@{i}=n@{i}_fss;
@@ -983,7 +980,3 @@ perfect_foresight_solver(maxit=100);
if ~oo_.deterministic_simulation.status
error('Perfect foresight simulation failed')
end
-
-@#else // stochastic case, used by files under tests/block_bytecode/lola_*
-stoch_simul(order=1);
-@#endif
diff --git a/tests/block_bytecode/lola_solve_one_boundary.mod b/tests/block_bytecode/lola_solve_one_boundary.mod
index f72b8afca..01378dc2d 100644
--- a/tests/block_bytecode/lola_solve_one_boundary.mod
+++ b/tests/block_bytecode/lola_solve_one_boundary.mod
@@ -1,6 +1,5 @@
// Involves a call to solve_one_boundary.m that is tested here
-@#define deterministic = true
@#define block = true
@#define mfs = 0
@#include "lola_common.inc"
diff --git a/tests/block_bytecode/lola_solve_one_boundary_mfs1.mod b/tests/block_bytecode/lola_solve_one_boundary_mfs1.mod
index 64ff378bf..8cd8a2e5c 100644
--- a/tests/block_bytecode/lola_solve_one_boundary_mfs1.mod
+++ b/tests/block_bytecode/lola_solve_one_boundary_mfs1.mod
@@ -1,6 +1,5 @@
// Tests option mfs=1 with block
-@#define deterministic = true
@#define block = true
@#define mfs = 1
@#include "lola_common.inc"
diff --git a/tests/block_bytecode/lola_solve_one_boundary_mfs2.mod b/tests/block_bytecode/lola_solve_one_boundary_mfs2.mod
index 92a9c0f07..7a76efb28 100644
--- a/tests/block_bytecode/lola_solve_one_boundary_mfs2.mod
+++ b/tests/block_bytecode/lola_solve_one_boundary_mfs2.mod
@@ -1,6 +1,5 @@
// Tests option mfs=2 with block
-@#define deterministic = true
@#define block = true
@#define mfs = 2
@#include "lola_common.inc"
diff --git a/tests/block_bytecode/lola_solve_one_boundary_mfs3.mod b/tests/block_bytecode/lola_solve_one_boundary_mfs3.mod
index 16b227463..fa549b68b 100644
--- a/tests/block_bytecode/lola_solve_one_boundary_mfs3.mod
+++ b/tests/block_bytecode/lola_solve_one_boundary_mfs3.mod
@@ -1,6 +1,5 @@
// Tests option mfs=3 with block
-@#define deterministic = true
@#define block = true
@#define mfs = 3
@#include "lola_common.inc"
diff --git a/tests/block_bytecode/lola_stochastic.mod b/tests/block_bytecode/lola_stochastic.mod
deleted file mode 100644
index e6c0071fc..000000000
--- a/tests/block_bytecode/lola_stochastic.mod
+++ /dev/null
@@ -1,5 +0,0 @@
-// Stochastic version of LOLA model, as benchmark for lola_stochastic_block.mod
-
-@#define deterministic = false
-@#define block = false
-@#include "lola_common.inc"
diff --git a/tests/block_bytecode/lola_stochastic_block.mod b/tests/block_bytecode/lola_stochastic_block.mod
deleted file mode 100644
index c4db5454f..000000000
--- a/tests/block_bytecode/lola_stochastic_block.mod
+++ /dev/null
@@ -1,26 +0,0 @@
-/* Stochastic version of block decomposed LOLA model.
- Check that policy functions are the same as in non-block version. */
-
-@#define deterministic = false
-@#define block = true
-@#define mfs = 0
-@#include "lola_common.inc"
-
-[~, state_reorder] = sort(oo_.dr.state_var);
-
-ref = load(['lola_stochastic' filesep 'Output' filesep 'lola_stochastic_results.mat']);
-
-[~, ref_state_reorder] = sort(ref.oo_.dr.state_var);
-
-/* NB: With block, the rows of ghx and ghu are in declaration order (and not in
- DR-order as in non-block mode) */
-
-if max(max(abs(oo_.dr.ghx(:, state_reorder) - ref.oo_.dr.ghx(ref.oo_.dr.inv_order_var, ref_state_reorder)))) > 3e-9
- error('Error in ghx')
-end
-
-if max(max(abs(oo_.dr.ghu - ref.oo_.dr.ghu(ref.oo_.dr.inv_order_var, :)))) > 5e-8
- error('Error in ghu')
-end
-
-