diff --git a/matlab/nonlinear-filters/auxiliary_initialization.m b/matlab/nonlinear-filters/auxiliary_initialization.m
deleted file mode 100644
index 3f4f03147685089ed3d8bd84f2d65c68261784e0..0000000000000000000000000000000000000000
--- a/matlab/nonlinear-filters/auxiliary_initialization.m
+++ /dev/null
@@ -1,116 +0,0 @@
-function initial_distribution = auxiliary_initialization(ReducedForm,Y,start,ParticleOptions,ThreadsOptions)
-
-% Evaluates the likelihood of a nonlinear model with a particle filter allowing eventually resampling.
-
-% Copyright © 2011-2022 Dynare Team
-%
-% This file is part of Dynare (particles module).
-%
-% 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 particles module 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 <https://www.gnu.org/licenses/>.
-
-persistent init_flag mf1 number_of_particles
-persistent number_of_observed_variables number_of_structural_innovations
-
-% Set default
-if isempty(start)
-    start = 1;
-end
-
-% Set flag for prunning
-%pruning = ParticleOptions.pruning;
-
-% Get steady state and mean.
-%steadystate = ReducedForm.steadystate;
-constant = ReducedForm.constant;
-ss = ReducedForm.ys;
-state_variables_steady_state = ReducedForm.state_variables_steady_state;
-
-% Set persistent variables.
-if isempty(init_flag)
-    mf1 = ReducedForm.mf1;
-    number_of_observed_variables = length(mf1);
-    number_of_structural_innovations = length(ReducedForm.Q);
-    number_of_particles = ParticleOptions.number_of_particles;
-    init_flag = 1;
-end
-
-order = options_.order;
-
-% Set local state space model (first order approximation).
-ghx  = ReducedForm.ghx;
-ghu  = ReducedForm.ghu;
-% Set local state space model (second order approximation).
-ghxx = ReducedForm.ghxx;
-ghuu = ReducedForm.ghuu;
-ghxu = ReducedForm.ghxu;
-ghs2 = ReducedForm.ghs2;
-if (order == 3)
-   ghxxx = ReducedForm.ghxxx;
-   ghuuu = ReducedForm.ghuuu;
-   ghxxu = ReducedForm.ghxxu;
-   ghxuu = ReducedForm.ghxuu;
-   ghxss = ReducedForm.ghxss;
-   ghuss = ReducedForm.ghuss;
-end
-
-% Get covariance matrices
-Q = ReducedForm.Q;
-H = ReducedForm.H;
-if isempty(H)
-    H = 0;
-end
-
-% Get initial condition for the state vector.
-StateVectorMean = ReducedForm.StateVectorMean;
-StateVectorVarianceSquareRoot = reduced_rank_cholesky(ReducedForm.StateVectorVariance)';
-state_variance_rank = size(StateVectorVarianceSquareRoot,2);
-%Q_lower_triangular_cholesky = chol(Q)';
-%if pruning
-%    StateVectorMean_ = StateVectorMean;
-%    StateVectorVarianceSquareRoot_ = StateVectorVarianceSquareRoot;
-%end
-
-% Set seed for randn().
-options_=set_dynare_seed_local_options(options_,'default');
-
-% Initialization of the likelihood.
-const_lik = log(2*pi)*number_of_observed_variables;
-
-% Initialization of the weights across particles.
-weights = ones(1,number_of_particles)/number_of_particles ;
-StateVectors = bsxfun(@plus,StateVectorVarianceSquareRoot*randn(state_variance_rank,number_of_particles),StateVectorMean);
-%if pruning
-%    StateVectors_ = StateVectors;
-%end
-yhat = bsxfun(@minus,StateVectors,state_variables_steady_state);
-%if pruning
-%    yhat_ = bsxfun(@minus,StateVectors_,state_variables_steady_state);
-%    [tmp, tmp_] = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,ThreadsOptions.local_state_space_iteration_2);
-%else
-if (order == 2)
-   tmp = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
-elseif (order == 3)
-   tmp = local_state_space_iteration_3(yhat, zeros(number_of_structural_innovations,number_of_particles), ghx, ghu, ghxx, ghuu, ghxu, ghs2, ghxxx, ghuuu, ghxxu, ghxuu, ghxss, ghuss, ss, options_.threads.local_state_space_iteration_3, false);
-else
-   error('Orders > 3 not allowed');
-end
-%end
-PredictedObservedMean = weights*(tmp(mf1,:)');
-PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));
-dPredictedObservedMean = bsxfun(@minus,tmp(mf1,:),PredictedObservedMean');
-PredictedObservedVariance = bsxfun(@times,weights,dPredictedObservedMean)*dPredictedObservedMean' + H;
-wtilde = exp(-.5*(const_lik+log(det(PredictedObservedVariance))+sum(PredictionError.*(PredictedObservedVariance\PredictionError),1))) ;
-tau_tilde = weights.*wtilde ;
-tau_tilde = tau_tilde/sum(tau_tilde);
-initial_distribution = resample(StateVectors',tau_tilde',ParticleOptions)' ;