diff --git a/matlab/default_option_values.m b/matlab/default_option_values.m
index d44766494..de0114c59 100644
--- a/matlab/default_option_values.m
+++ b/matlab/default_option_values.m
@@ -780,4 +780,5 @@ options_.varobs_id=[]; %initialize field
 options_.pac.estimation.ols.share_of_optimizing_agents.lb = 0.0;
 options_.pac.estimation.ols.share_of_optimizing_agents.ub = 1.0;
 
-end
+options_.conditional_likelihood.status = false;
+options_.conditional_likelihood.order = 1;
\ No newline at end of file
diff --git a/matlab/dsge_conditional_likelihood_1.m b/matlab/dsge_conditional_likelihood_1.m
new file mode 100644
index 000000000..f8a16e4b8
--- /dev/null
+++ b/matlab/dsge_conditional_likelihood_1.m
@@ -0,0 +1,204 @@
+function [fval, info, exitflag, DLIK, Hess, SteadyState, trend_coeff, Model, DynareOptions, BayesInfo, DynareResults] = ...
+    dsge_conditional_likelihood_1(xparam1, DynareDataset, DatasetInfo, DynareOptions, Model, EstimatedParameters, BayesInfo, BoundsInfo, DynareResults, derivatives_info)
+
+% Copyright (C) 2017-2023 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 <http://www.gnu.org/licenses/>.
+
+% Initialization of the returned variables and others...
+fval        = [];
+SteadyState = [];
+trend_coeff = [];
+exitflag    = true;
+info        = zeros(4,1);
+DLIK        = [];
+Hess        = [];
+
+% Exit with error if analytical_derivation option is used.
+if DynareOptions.analytic_derivation
+    error('The analytic_derivation and conditional_likelihood are not compatible!')
+end
+
+% Ensure that xparam1 is a column vector.
+% (Don't do the transformation if xparam1 is empty, otherwise it would become a
+%  0×1 matrix, which create issues with older MATLABs when comparing with [] in
+%  check_bounds_and_definiteness_estimation)
+if ~isempty(xparam1)
+    xparam1 = xparam1(:);
+end
+
+%------------------------------------------------------------------------------
+% 1. Get the structural parameters & define penalties
+%------------------------------------------------------------------------------
+Model = set_all_parameters(xparam1,EstimatedParameters,Model);
+
+[fval, info, exitflag, Q, H] = check_bounds_and_definiteness_estimation(xparam1, Model, EstimatedParameters, BoundsInfo);
+if info(1)
+    return
+end
+
+iQ_upper_chol = chol(inv(Q));
+
+% Return an error if the interface for measurement errors is used.
+if ~isequal(H, zeros(size(H))) || EstimatedParameters.ncn || EstimatedParameters.ncx
+    error('Option conditional_likelihood does not support declaration of measurement errors. You can specify the measurement errors in the model block directly by adding measurement equations.')
+end
+
+%------------------------------------------------------------------------------
+% 2. call model setup & reduction program
+%------------------------------------------------------------------------------
+
+% Linearize the model around the deterministic sdteadystate and extract the matrices of the state equation (T and R).
+[T, R, SteadyState, info, Model, DynareResults] = ...
+    dynare_resolve(Model, DynareOptions, DynareResults, 'restrict');
+
+% Return, with endogenous penalty when possible, if dynare_resolve issues an error code (defined in resol).
+if info(1)
+    if info(1) == 3 || info(1) == 4 || info(1) == 5 || info(1)==6 ||info(1) == 19 ||...
+                info(1) == 20 || info(1) == 21 || info(1) == 23 || info(1) == 26 || ...
+                info(1) == 81 || info(1) == 84 ||  info(1) == 85 ||  info(1) == 86 || ...
+                info(1) == 401 || info(1) == 402 || info(1) == 403 || ... %cycle reduction
+                info(1) == 411 || info(1) == 412 || info(1) == 413 % logarithmic reduction 
+        %meaningful second entry of output that can be used
+        fval = Inf;
+        info(4) = info(2);
+        exitflag = false;
+        return
+    else
+        fval = Inf;
+        info(4) = 0.1;
+        exitflag = false;
+        return
+    end
+end
+
+% check endogenous prior restrictions
+info = endogenous_prior_restrictions(T, R, Model, DynareOptions, DynareResults);
+if info(1)
+    fval = Inf;
+    info(4)=info(2);
+    exitflag = false;
+    return
+end
+
+% Define a vector of indices for the observed variables. Is this really usefull?...
+BayesInfo.mf = BayesInfo.mf1;
+
+% Define the constant vector of the measurement equation.
+if DynareOptions.noconstant
+    constant = zeros(DynareDataset.vobs, 1);
+else
+    if DynareOptions.loglinear
+        constant = log(SteadyState(BayesInfo.mfys));
+    else
+        constant = SteadyState(BayesInfo.mfys);
+    end
+end
+
+% Define the deterministic linear trend of the measurement equation.
+if BayesInfo.with_trend
+    [trend_addition, trend_coeff] = compute_trend_coefficients(Model, DynareOptions, DynareDataset.vobs, DynareDataset.nobs);
+    trend = repmat(constant, 1, DynareDataset.nobs)+trend_addition;
+else
+   trend_coeff = zeros(DynareDataset.vobs, 1);
+   trend = repmat(constant, 1, DynareDataset.nobs);
+end
+
+% Return an error if some observations are missing.
+if DatasetInfo.missing.state
+    error('Option conditional_likelihood is not compatible with missing observations.')
+end
+
+% Get the selection matrix (vector of row indices for T and R)
+Z = BayesInfo.mf;
+
+% Get the number of observed variables.
+pp = DynareDataset.vobs;
+
+% Get the number of variables in the state equations (state variables plus observed variables).
+mm = size(T, 1);
+
+% Get the number of innovations.
+rr = length(Q);
+
+% Return an error if the number of shocks is not equal to the number of observations.
+if ~isequal(pp, rr)
+    error('With conditional_likelihood the number of innovations must be equal to the number of observed varilables!')
+end
+
+% Remove the trend.
+Y = transpose(DynareDataset.data)-trend;
+
+% Set state vector (deviation to steady state)
+S = zeros(mm, 1);
+
+%------------------------------------------------------------------------------
+% 3. Evaluate the conditional likelihood
+%------------------------------------------------------------------------------
+
+Rtild = inv(R(Z,:));
+const = -.5*rr*log(2*pi);
+const = const + log(abs(det(Rtild))) + sum(log(diag(iQ_upper_chol)));
+
+llik = zeros(size(Y, 2));
+
+Ytild = Rtild*Y;
+Ttild = Rtild*T(Z,:);
+
+for t=1:size(Y, 2)
+    epsilon = Ytild(:,t) - Ttild*S;
+    upsilon = iQ_upper_chol*epsilon;
+    S = T*S + R*epsilon;
+    llik(t) = const - .5*(upsilon'*upsilon);
+end
+
+% Computes minus log-likelihood.
+likelihood = -sum(llik(DynareOptions.presample+1:size(Y, 2)));
+
+
+% ------------------------------------------------------------------------------
+% 5. Adds prior if necessary
+% ------------------------------------------------------------------------------
+
+lnprior = priordens(xparam1, BayesInfo.pshape, BayesInfo.p6, BayesInfo.p7, BayesInfo.p3, BayesInfo.p4);
+
+if DynareOptions.endogenous_prior==1
+    [lnpriormom]  = endogenous_prior(Y, Pstar, BayesInfo, H);
+    fval = (likelihood-lnprior-lnpriormom);
+else
+    fval = (likelihood-lnprior);
+end
+
+if DynareOptions.prior_restrictions.status
+    tmp = feval(DynareOptions.prior_restrictions.routine, Model, DynareResults, DynareOptions, DynareDataset, DatasetInfo);
+    fval = fval - tmp;
+end
+
+if isnan(fval)
+    fval = Inf;
+    info(1) = 47;
+    info(4) = 0.1;
+    exitflag = false;
+    return
+end
+
+if imag(fval)~=0
+    fval = Inf;
+    info(1) = 48;
+    info(4) = 0.1;
+    exitflag = false;
+    return
+end
\ No newline at end of file
diff --git a/matlab/dynare_estimation_1.m b/matlab/dynare_estimation_1.m
index 9e8d1af5e..9de11f35b 100644
--- a/matlab/dynare_estimation_1.m
+++ b/matlab/dynare_estimation_1.m
@@ -98,6 +98,8 @@ if ~options_.dsge_var
     else
         if options_.occbin.likelihood.status && options_.occbin.likelihood.inversion_filter
             objective_function = str2func('occbin.IVF_posterior');
+        elseif options_.conditional_likelihood.status && options_.conditional_likelihood.order==1
+            objective_function = str2func('dsge_conditional_likelihood_1');
         else
             objective_function = str2func('dsge_likelihood');
         end