function [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK,T,R,P,PK,decomp] = DsgeSmoother(xparam1,gend,Y,data_index,missing_value)
% Estimation of the smoothed variables and innovations. 
% 
% INPUTS 
%   o xparam1       [double]   (p*1) vector of (estimated) parameters. 
%   o gend          [integer]  scalar specifying the number of observations ==> varargin{1}.
%   o data          [double]   (T*n) matrix of data.
%   o data_index    [cell]      1*smpl cell of column vectors of indices.
%   o missing_value 1 if missing values, 0 otherwise
%  
% OUTPUTS 
%   o alphahat      [double]  (m*T) matrix, smoothed endogenous variables.
%   o etahat        [double]  (r*T) matrix, smoothed structural shocks (r>n is the umber of shocks).
%   o epsilonhat    [double]  (n*T) matrix, smoothed measurement errors.
%   o ahat          [double]  (m*T) matrix, one step ahead filtered (endogenous) variables.
%   o SteadyState   [double]  (m*1) vector specifying the steady state level of each endogenous variable.
%   o trend_coeff   [double]  (n*1) vector, parameters specifying the slope of the trend associated to each observed variable.
%   o aK            [double]  (K,n,T+K) array, k (k=1,...,K) steps ahead filtered (endogenous) variables.
%   o T and R       [double]  Matrices defining the state equation (T is the (m*m) transition matrix).
%   o P:            3D array of one-step ahead forecast error variance
%                       matrices
%   o PK:           4D array of k-step ahead forecast error variance
%                       matrices (meaningless for periods 1:d)
%   o decomp        4D array of shock decomposition of k-step ahead
%                       filtered variables
% 
% ALGORITHM 
%   Diffuse Kalman filter (Durbin and Koopman)       
%
% SPECIAL REQUIREMENTS
%   None

% Copyright (C) 2006-2014 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/>.

global bayestopt_ M_ oo_ estim_params_ options_

alphahat        = [];
etahat  = [];
epsilonhat      = [];
ahat          = [];
SteadyState   = [];
trend_coeff   = [];
aK            = [];
T             = [];
R             = [];
P             = [];
PK            = [];
decomp        = [];
vobs            = length(options_.varobs);
smpl          = size(Y,2);

if ~isempty(xparam1) %not calibrated model
    M_ = set_all_parameters(xparam1,estim_params_,M_);
end

%------------------------------------------------------------------------------
% 2. call model setup & reduction program
%------------------------------------------------------------------------------
oldoo.restrict_var_list = oo_.dr.restrict_var_list; 
oldoo.restrict_columns = oo_.dr.restrict_columns;
oo_.dr.restrict_var_list = bayestopt_.smoother_var_list;
oo_.dr.restrict_columns = bayestopt_.smoother_restrict_columns;

[T,R,SteadyState,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_);

oo_.dr.restrict_var_list = oldoo.restrict_var_list;
oo_.dr.restrict_columns = oldoo.restrict_columns;

bayestopt_.mf = bayestopt_.smoother_mf;
if options_.noconstant
    constant = zeros(vobs,1);
else
    if options_.loglinear
        constant = log(SteadyState(bayestopt_.mfys));
    else
        constant = SteadyState(bayestopt_.mfys);
    end
end
trend_coeff = zeros(vobs,1);
if bayestopt_.with_trend == 1
    trend_coeff = zeros(vobs,1);
    t = options_.trend_coeffs;
    for i=1:length(t)
        if ~isempty(t{i})
            trend_coeff(i) = evalin('base',t{i});
        end
    end
    trend = constant*ones(1,gend)+trend_coeff*(1:gend);
else
    trend = constant*ones(1,gend);
end
start = options_.presample+1;
np    = size(T,1);
mf    = bayestopt_.smoother_mf;
% ------------------------------------------------------------------------------
%  3. Initial condition of the Kalman filter
% ------------------------------------------------------------------------------
% 
%  C'est ici qu'il faut déterminer Pinf et Pstar. Si le modèle est stationnaire,
%  alors il suffit de poser Pstar comme la solution de l'éuation de Lyapounov et
%  Pinf=[].
%
Q = M_.Sigma_e;
H = M_.H;

if isequal(H,0)
    H = zeros(vobs,vobs);
end

kalman_algo = options_.kalman_algo;
if options_.lik_init == 1               % Kalman filter
    if kalman_algo ~= 2
        kalman_algo = 1;
    end
	if options_.lyapunov_fp == 1
        Pstar = lyapunov_symm(T,R*Q*R',options_.lyapunov_fixed_point_tol,options_.qz_criterium,options_.lyapunov_complex_threshold, 3, [], options_.debug);
    elseif options_.lyapunov_db == 1
        Pstar = disclyap_fast(T,R*Q*R',options_.lyapunov_doubling_tol);
    elseif options_.lyapunov_srs == 1
        Pstar = lyapunov_symm(T,Q,options_.lyapunov_fixed_point_tol,options_.qz_criterium,options_.lyapunov_complex_threshold, 4, R, options_.debug);
    else
        Pstar = lyapunov_symm(T,R*Q*R',options_.lyapunov_fixed_point_tol,options_.qz_criterium,options_.lyapunov_complex_threshold, [], [], options_.debug);
    end;
    Pinf        = [];
elseif options_.lik_init == 2           % Old Diffuse Kalman filter
    if kalman_algo ~= 2
        kalman_algo = 1;
    end
    Pstar = options_.Harvey_scale_factor*eye(np);
    Pinf        = [];
elseif options_.lik_init == 3           % Diffuse Kalman filter
    if kalman_algo ~= 4
        kalman_algo = 3;
    end
    [Z,ST,R1,QT,Pstar,Pinf] = schur_statespace_transformation(mf,T,R,Q,options_.qz_criterium,oo_.dr.restrict_var_list);
    Pinf = QT*Pinf*QT';
    Pstar = QT*Pstar*QT';
elseif options_.lik_init == 4           % Start from the solution of the Riccati equation.
    [err, Pstar] = kalman_steady_state(transpose(T),R*Q*transpose(R),transpose(build_selection_matrix(mf,np,vobs)),H);
    mexErrCheck('kalman_steady_state',err);
    Pinf  = [];
    if kalman_algo~=2
        kalman_algo = 1;
    end
elseif options_.lik_init == 5            % Old diffuse Kalman filter only for the non stationary variables
    [eigenvect, eigenv] = eig(T);
    eigenv = diag(eigenv);
    nstable = length(find(abs(abs(eigenv)-1) > 1e-7));
    unstable = find(abs(abs(eigenv)-1) < 1e-7);
    V = eigenvect(:,unstable);
    indx_unstable = find(sum(abs(V),2)>1e-5);
    stable = find(sum(abs(V),2)<1e-5);
    nunit = length(eigenv) - nstable;
    Pstar = options_.Harvey_scale_factor*eye(np);
    if kalman_algo ~= 2
        kalman_algo = 1;
    end
    R_tmp = R(stable, :);
    T_tmp = T(stable,stable);
    if options_.lyapunov_fp == 1
        Pstar_tmp = lyapunov_symm(T_tmp,R_tmp*Q*R_tmp',options_.lyapunov_fixed_point_tol,options_.qz_criterium,options_.lyapunov_complex_threshold, 3, [], options_.debug);
    elseif options_.lyapunov_db == 1
        Pstar_tmp = disclyap_fast(T_tmp,R_tmp*Q*R_tmp',options_.lyapunov_doubling_tol);
    elseif options_.lyapunov_srs == 1
        Pstar_tmp = lyapunov_symm(T_tmp,Q,options_.lyapunov_fixed_point_tol,options_.qz_criterium,options_.lyapunov_complex_threshold, 4, R_tmp, options_.debug);
    else
        Pstar_tmp = lyapunov_symm(T_tmp,R_tmp*Q*R_tmp',options_.lyapunov_fixed_point_tol,options_.qz_criterium,options_.lyapunov_complex_threshold, [], [], options_.debug);
    end
    Pstar(stable, stable) = Pstar_tmp;
    Pinf  = [];
end
kalman_tol = options_.kalman_tol;
diffuse_kalman_tol = options_.diffuse_kalman_tol;
riccati_tol = options_.riccati_tol;
data1 = Y-trend;
% -----------------------------------------------------------------------------
%  4. Kalman smoother
% -----------------------------------------------------------------------------

if ~missing_value
    for i=1:smpl
        data_index{i}=(1:vobs)';
    end
end

ST = T;
R1 = R;
Z = zeros(vobs,size(T,2));
for i=1:vobs
    Z(i,mf(i)) = 1;
end

if kalman_algo == 1 || kalman_algo == 3
    [alphahat,epsilonhat,etahat,ahat,P,aK,PK,decomp] = missing_DiffuseKalmanSmootherH1_Z(ST, ...
                                                      Z,R1,Q,H,Pinf,Pstar, ...
                                                      data1,vobs,np,smpl,data_index, ...
                                                      options_.nk,kalman_tol,diffuse_kalman_tol,options_.filter_decomposition);
    if isinf(alphahat)
        if kalman_algo == 1
            kalman_algo = 2;
        elseif kalman_algo == 3
            kalman_algo = 4;
        else
            error('This case shouldn''t happen')
        end
    end
end

if kalman_algo == 2 || kalman_algo == 4
    if estim_params_.ncn
        ST = [ zeros(vobs,vobs) Z; zeros(np,vobs) T];
        ns = size(Q,1);
        R1 = [ eye(vobs) zeros(vobs, ns); zeros(np,vobs) R];
        Q = [H zeros(vobs,ns); zeros(ns,vobs) Q]; 
        Z = [eye(vobs) zeros(vobs, np)];
        if kalman_algo == 4
            [Z,ST,R1,QT,Pstar,Pinf] = schur_statespace_transformation((1:vobs)',ST,R1,Q,options_.qz_criterium);
        end
        
    end
    [alphahat,epsilonhat,etahat,ahat,P,aK,PK,decomp] = missing_DiffuseKalmanSmootherH3_Z(ST, ...
                                                      Z,R1,Q,diag(H), ...
                                                      Pinf,Pstar,data1,vobs,np,smpl,data_index, ...
                                                      options_.nk,kalman_tol,diffuse_kalman_tol, ...
                                                      options_.filter_decomposition);
end


if estim_params_.ncn && (kalman_algo == 2 || kalman_algo == 4)
    % extracting measurement errors
    % removing observed variables from the state vector
    k = vobs+(1:np);
    alphahat = alphahat(k,:);
    ahat = ahat(k,:);
    aK = aK(:,k,:,:);
    if ~isempty(PK)
        PK = PK(:,k,k,:);
    end
    if ~isempty(decomp)
        decomp = decomp(:,k,:,:);
    end
    if ~isempty(P)
        P = P(k,k,:);
    end
end