function [fval, info, exitflag, DLIK, Hess, model_solution] = ...
    dsge_conditional_likelihood_1(xparam1, dataset_, dataset_info, options_, M_, estim_params_, Prior, BoundsInfo, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, ~, measurement_info)
% Evaluates the posterior kernel of a DSGE model using conditional likelihood.
%
% INPUTS
% - xparam1             [double]        current values for the estimated parameters.
% - dataset_            [structure]     dataset after transformations
% - dataset_info        [structure]     storing information about the
%                                       sample; not used but required for interface
% - options_            [structure]     MATLAB's structure describing the current options
% - M_                  [structure]     MATLAB's structure describing the model
% - estim_params_       [structure]     characterizing parameters to be estimated
% - Prior               [@dprior]       prior information object
% - BoundsInfo          [structure]     containing prior bounds
% - dr                  [structure]     Reduced form model.
% - endo_steady_state   [vector]        steady state value for endogenous variables
% - exo_steady_state    [vector]        steady state value for exogenous variables
% - exo_det_steady_state [vector]       steady state value for exogenous deterministic variables
% - derivatives_info    [structure]     derivative info for identification
%
% OUTPUTS
% - fval                    [double]        scalar, value of the likelihood or posterior kernel.
% - info                    [integer]       4×1 vector, information on whether solution and likelihood could be computed
% - exit_flag               [integer]       scalar, equal to 1 (no issues when evaluating the likelihood) or 0 (not able to evaluate the likelihood).
% - DLIK                    [double]        Vector with score of the likelihood
% - Hess                    [double]        asymptotic Hessian matrix.
% - model_solution          [struct]        Structure containing the model solution at the current parameter
%                                           values with fields: dr, ys, trend_coeff, and the M_ fields
%                                           set by set_all_parameters (params, Sigma_e, Correlation_matrix,
%                                           H, Correlation_matrix_ME, Skew_e, endo_initial_state_values, csn).

% Copyright © 2017-2025 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 = [];
model_solution = [];
exitflag    = true;
info        = zeros(4,1);
DLIK        = [];
Hess        = [];

if nargin < 15 || isempty(measurement_info)
    measurement_info = set_measurement_info(M_, dr, options_);
end

% Exit with error if analytical_derivation option is used.
if options_.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
%------------------------------------------------------------------------------
M_ = set_all_parameters(xparam1,estim_params_,M_);

[fval, info, exitflag, Q, H] = check_bounds_and_definiteness_estimation(xparam1, M_, estim_params_, 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))) || estim_params_.ncn || estim_params_.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 steadystate and extract the matrices of the state equation (T and R).
[T, R, SteadyState, info,dr, M_.params] = ...
    dynare_resolve(M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, '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;
        if ~isfinite(info(2))
            info(4) = 0.1;
        else
            info(4) = info(2);
        end
        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, M_, options_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state);
if info(1)
    fval = Inf;
    if ~isfinite(info(2))
        info(4) = 0.1;
    else
        info(4) = info(2);
    end
    exitflag = false;
    return
end

% Define the constant vector of the measurement equation.
if ~options_.noconstant
    if options_.loglinear
        constant = log(SteadyState(measurement_info.mfys));
    else
        constant = SteadyState(measurement_info.mfys);
    end
end

% Define the deterministic linear trend of the measurement equation.
if M_.with_trend
    [trend_addition, trend_coeff] = compute_trend_coefficients(M_, options_, dataset_.vobs, dataset_.nobs);
    Y = bsxfun(@minus, transpose(dataset_.data), constant)-trend_addition;
else
    trend_coeff = zeros(dataset_.vobs, 1);
    if ~options_.noconstant
        Y = bsxfun(@minus, transpose(dataset_.data), constant);
    else
        Y = transpose(dataset_.data);
    end
end

% Return an error if some observations are missing.
if dataset_info.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 = measurement_info.mf1;

% Get the number of observed variables.
pp = dataset_.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

% Set state vector (deviation to steady state)
S = zeros(mm, 1);

%------------------------------------------------------------------------------
% 3. Evaluate the conditional likelihood
%------------------------------------------------------------------------------

[L, U] = lu(R(Z,:)); % note that det(L)={-1,1} depending on the number of permutations so we can forget it when we take the absolute value of the determinant of R(Z,:) below (in the constant).

const = -.5*rr*log(2*pi) - log(abs(prod(diag(U)))) + sum(log(diag(iQ_upper_chol)));

llik = zeros(size(Y, 2), 1);

Ytild = U\(L\Y);
Ttild = U\(L\T(Z,:));

for t = 1:options_.presample
    epsilon = Ytild(:,t) - Ttild*S;
    S = T*S + R*epsilon;
end

for t=(options_.presample+1):size(Y, 2)
    epsilon = Ytild(:,t) - Ttild*S;
    upsilon = iQ_upper_chol*epsilon;
    S = T*S + R*epsilon;
    llik(t) = const - .5*dot(upsilon, upsilon);
end

% Computes minus log-likelihood.
likelihood = -sum(llik);
if isinf(likelihood)
    fval = Inf; info(1) = 50; info(4) = 0.1; exitflag = false;
    return
end
if isnan(likelihood)
    fval = Inf; info(1) = 45; info(4) = 0.1; exitflag = false;
    return
end
if imag(likelihood)~=0
    fval = Inf; info(1) = 46; info(4) = 0.1; exitflag = false;
    return
end

% ------------------------------------------------------------------------------
% 5. Adds prior if necessary
% ------------------------------------------------------------------------------

lnprior = Prior.density(xparam1);
if isinf(lnprior)
    fval = Inf; info(1) = 40; info(4) = 0.1; exitflag = false;
    return
end
if isnan(lnprior)
    fval = Inf; info(1) = 47; info(4) = 0.1; exitflag = false;
    return
end
if imag(lnprior)~=0
    fval = Inf; info(1) = 48; info(4) = 0.1; exitflag = false;
    return
end

if options_.endogenous_prior==1
    [lnpriormom]  = endogenous_prior(Y, Pstar, H);
    fval = (likelihood-lnprior-lnpriormom);
else
    fval = (likelihood-lnprior);
end

if options_.prior_restrictions.status
    tmp = feval(options_.prior_restrictions.routine, M_, dr, endo_steady_state, exo_steady_state, exo_det_steady_state, options_, dataset_, dataset_info);
    fval = fval - tmp;
end

if nargout >= 6
    model_solution.dr = dr;
    model_solution.ys = SteadyState;
    model_solution.trend_coeff = trend_coeff;
    model_solution.params = M_.params;
    model_solution.Sigma_e = M_.Sigma_e;
    model_solution.Correlation_matrix = M_.Correlation_matrix;
    model_solution.H = M_.H;
    model_solution.Correlation_matrix_ME = M_.Correlation_matrix_ME;
    model_solution.Skew_e = M_.Skew_e;
    if isfield(M_,'endo_initial_state')
        model_solution.endo_initial_state_values = M_.endo_initial_state.values;
    end
    if isfield(M_,'csn')
        model_solution.csn = M_.csn;
    end
end
