diff --git a/matlab/UnivariateSpectralDensity.m b/matlab/UnivariateSpectralDensity.m
index 1e60bd47e65034179e3981a6cc699fcd1657856b..93d03c2eefb71725e5f8b2b0b27ed2ee639c5eac 100644
--- a/matlab/UnivariateSpectralDensity.m
+++ b/matlab/UnivariateSpectralDensity.m
@@ -93,7 +93,7 @@ for i=M_.maximum_lag:-1:2
i0 = i1;
end
Gamma = zeros(nvar,cutoff+1);
-[A,B] = kalman_transition_matrix(dr,ikx',1:nx,dr.transition_auxiliary_variables,M_.exo_nbr);
+[A,B] = kalman_transition_matrix(dr,ikx',1:nx,M_.exo_nbr);
[vx, u] = lyapunov_symm(A,B*M_.Sigma_e*B',options_.qz_criterium,options_.lyapunov_complex_threshold);
iky = iv(ivar);
if ~isempty(u)
diff --git a/matlab/display_conditional_variance_decomposition.m b/matlab/display_conditional_variance_decomposition.m
index a75b95820865e19145f3e41fdc90bcf047e34e3f..28d740d756468edf12c25f0dc0907e3a98d81138 100644
--- a/matlab/display_conditional_variance_decomposition.m
+++ b/matlab/display_conditional_variance_decomposition.m
@@ -42,7 +42,7 @@ StateSpaceModel.sigma_e_is_diagonal = M_.sigma_e_is_diagonal;
iv = (1:endo_nbr)';
ic = dr.nstatic+(1:dr.npred)';
-[StateSpaceModel.transition_matrix,StateSpaceModel.impulse_matrix] = kalman_transition_matrix(dr,iv,ic,[],exo_nbr);
+[StateSpaceModel.transition_matrix,StateSpaceModel.impulse_matrix] = kalman_transition_matrix(dr,iv,ic,exo_nbr);
StateSpaceModel.state_innovations_covariance_matrix = M_.Sigma_e;
StateSpaceModel.order_var = dr.order_var;
diff --git a/matlab/dsge_simulated_theoretical_conditional_variance_decomposition.m b/matlab/dsge_simulated_theoretical_conditional_variance_decomposition.m
index f7543882f33856376b0c9fcbe08655adf1b692bb..094d7999ce998018203b5812ebbd370d9715206f 100644
--- a/matlab/dsge_simulated_theoretical_conditional_variance_decomposition.m
+++ b/matlab/dsge_simulated_theoretical_conditional_variance_decomposition.m
@@ -111,10 +111,6 @@ for file = 1:NumberOfDrawsFiles
npred = dr.npred;
iv = (1:endo_nbr)';
ic = [ nstatic+(1:npred) endo_nbr+(1:size(dr.ghx,2)-npred) ]';
- aux = dr.transition_auxiliary_variables;
- k = find(aux(:,2) > npred);
- aux(:,2) = aux(:,2) + nstatic;
- aux(k,2) = aux(k,2) + dr.nfwrd;
StateSpaceModel.number_of_state_equations = M_.endo_nbr+rows(aux);
StateSpaceModel.number_of_state_innovations = M_.exo_nbr;
StateSpaceModel.sigma_e_is_diagonal = M_.sigma_e_is_diagonal;
@@ -122,7 +118,7 @@ for file = 1:NumberOfDrawsFiles
first_call = 0;
clear('endo_nbr','nstatic','npred','k');
end
- [StateSpaceModel.transition_matrix,StateSpaceModel.impulse_matrix] = kalman_transition_matrix(dr,iv,ic,aux,M_.exo_nbr);
+ [StateSpaceModel.transition_matrix,StateSpaceModel.impulse_matrix] = kalman_transition_matrix(dr,iv,ic,M_.exo_nbr);
StateSpaceModel.state_innovations_covariance_matrix = M_.Sigma_e;
clear('dr');
Conditional_decomposition_array(:,:,:,linea) = conditional_variance_decomposition(StateSpaceModel, Steps, ivar);
diff --git a/matlab/forcst.m b/matlab/forcst.m
index f996f785bbc0c6022514b8ea8063b9b0c8167ceb..d3135f0a13fa425476e1ef8dff6fefadcd6635ad 100644
--- a/matlab/forcst.m
+++ b/matlab/forcst.m
@@ -43,7 +43,7 @@ npred = dr.npred;
nc = size(dr.ghx,2);
endo_nbr = M_.endo_nbr;
inv_order_var = dr.inv_order_var;
-[A,B] = kalman_transition_matrix(dr,nstatic+(1:npred),1:nc,dr.transition_auxiliary_variables,M_.exo_nbr);
+[A,B] = kalman_transition_matrix(dr,nstatic+(1:npred),1:nc,M_.exo_nbr);
if size(var_list,1) == 0
var_list = M_.endo_names(1:M_.orig_endo_nbr,:);
diff --git a/matlab/get_variance_of_endogenous_variables.m b/matlab/get_variance_of_endogenous_variables.m
index f1ef501fdca98e6df17afb16a1d8bb1f246dff9a..1165d1aebeed1785f5d5687b2d42e0081e4a5695 100644
--- a/matlab/get_variance_of_endogenous_variables.m
+++ b/matlab/get_variance_of_endogenous_variables.m
@@ -43,7 +43,7 @@ ghu = dr.ghu(i_var,:);
nc = size(ghx,2);
n = length(i_var);
-[A,B] = kalman_transition_matrix(dr,nstatic+(1:npred),1:nc,dr.transition_auxiliary_variables,M_.exo_nbr);
+[A,B] = kalman_transition_matrix(dr,nstatic+(1:npred),1:nc,M_.exo_nbr);
[vx,u] = lyapunov_symm(A,B*Sigma_e*B',options_.qz_criterium,options_.lyapunov_complex_threshold);
diff --git a/matlab/kalman_transition_matrix.m b/matlab/kalman_transition_matrix.m
index b0152cf181ae9691b85a17dd0c67dca5a4c40c83..ca38eb3357f66718173b245afe50f7cfeefbd549 100644
--- a/matlab/kalman_transition_matrix.m
+++ b/matlab/kalman_transition_matrix.m
@@ -1,11 +1,11 @@
-function [A,B] = kalman_transition_matrix(dr,iv,ic,aux,exo_nbr)
+function [A,B] = kalman_transition_matrix(dr,iv,ic,exo_nbr)
+%function [A,B] = kalman_transition_matrix(dr,iv,ic,exo_nbr)
% Builds the transition equation of the state space representation out of ghx and ghu for Kalman filter
%
% INPUTS
% dr: structure of decisions rules for stochastic simulations
% iv: selected variables
% ic: state variables position in the transition matrix columns
-% aux: indices for auxiliary equations
% exo_nbr: number of exogenous variables
%
% OUTPUTS
@@ -33,47 +33,14 @@ function [A,B] = kalman_transition_matrix(dr,iv,ic,aux,exo_nbr)
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
n_iv = length(iv);
-n_ir1 = size(aux,1);
-nr = n_iv + n_ir1;
-A = zeros(nr,nr);
+A = zeros(n_iv,n_iv);
i_n_iv = 1:n_iv;
A(i_n_iv,ic) = dr.ghx(iv,:);
-if n_ir1 > 0
- A(n_iv+1:end,:) = sparse(aux(:,1),aux(:,2),ones(n_ir1,1),n_ir1,nr);
-end
if nargout>1
- B = zeros(nr,exo_nbr);
+ B = zeros(n_iv,exo_nbr);
B(i_n_iv,:) = dr.ghu(iv,:);
end
-% $$$ function [A,B] = kalman_transition_matrix(dr)
-% $$$ global M_
-% $$$ nx = size(dr.ghx,2)+dr.nfwrd+dr.nstatic;
-% $$$ kstate = dr.kstate;
-% $$$ ikx = [dr.nstatic+1:dr.nstatic+dr.npred];
-% $$$
-% $$$ A = zeros(nx,nx);
-% $$$ k0 = kstate(find(kstate(:,2) <= M_.maximum_lag+1),:);
-% $$$ i0 = find(k0(:,2) == M_.maximum_lag+1);
-% $$$ n0 = size(dr.ghx,1);
-% $$$ A(1:n0,dr.nstatic+1:dr.nstatic+dr.npred) = dr.ghx(:,1:dr.npred);
-% $$$ A(1:n0,dr.nstatic+dr.npred+dr.nfwrd+1:end) = dr.ghx(:,dr.npred+1:end);
-% $$$ B = zeros(nx,M_.exo_nbr);
-% $$$ B(1:n0,:) = dr.ghu;
-% $$$ offset_col = dr.nstatic;
-% $$$ for i=M_.maximum_lag:-1:2
-% $$$ i1 = find(k0(:,2) == i);
-% $$$ n1 = size(i1,1);
-% $$$ j = zeros(n1,1);
-% $$$ for j1 = 1:n1
-% $$$ j(j1) = find(k0(i0,1)==k0(i1(j1),1));
-% $$$ end
-% $$$ if i == M_.maximum_lag-1
-% $$$ offset_col = dr.nstatic+dr.nfwrd;
-% $$$ end
-% $$$ A(n0+i1-dr.npred,offset_col+i0(j))=eye(n1);
-% $$$ i0 = i1;
-% $$$ end
diff --git a/matlab/simultxdet.m b/matlab/simultxdet.m
index 4d7732e83394eb70e059cea74a56bacd48653ad6..c3396998363707fcad5cecd5e35a7875e87646a8 100644
--- a/matlab/simultxdet.m
+++ b/matlab/simultxdet.m
@@ -114,7 +114,7 @@ elseif iorder == 2
end
end
-[A,B] = kalman_transition_matrix(dr,nstatic+(1:npred),1:nc,dr.transition_auxiliary_variables,M_.exo_nbr);
+[A,B] = kalman_transition_matrix(dr,nstatic+(1:npred),1:nc,M_.exo_nbr);
inv_order_var = dr.inv_order_var;
ghx1 = dr.ghx(inv_order_var(ivar),:);
diff --git a/matlab/th_autocovariances.m b/matlab/th_autocovariances.m
index 5e7701421db3a7b4410b9157d42dbea51e174ff0..a0ded813d08ab5343da9c2068970bb16c61b8f71 100644
--- a/matlab/th_autocovariances.m
+++ b/matlab/th_autocovariances.m
@@ -92,7 +92,7 @@ b = ghu1*M_.Sigma_e*ghu1';
ipred = nstatic+(1:npred)';
% state space representation for state variables only
-[A,B] = kalman_transition_matrix(dr,ipred,1:nx,dr.transition_auxiliary_variables,M_.exo_nbr);
+[A,B] = kalman_transition_matrix(dr,ipred,1:nx,M_.exo_nbr);
% Compute stationary variables (before HP filtering),
% and compute 2nd order mean correction on stationary variables (in case of
% HP filtering, this mean correction is computed *before* filtering)