function M = set_all_parameters(xparam1,estim_params,M)

%@info:
%! @deftypefn {Function File} {@var{M} =} dynSeries (@var{xparams1},@var{estim_params},@var{M})
%! @anchor{set_all_parameters}
%! @sp 1
%! Update parameter values (deep parameters and covariance matrices).
%! @sp 2
%! @strong{Inputs}
%! @sp 1
%! @table @ @var
%! @item xparam1
%! N*1 vector of doubles, the values of the N estimated parameters.
%! @item estim_params
%! Dynare structure describing the estimated parameters.
%! @item M
%! Dynare structure describing the model.
%! @end table
%! @sp 1
%! @strong{Outputs}
%! @sp 1
%! @table @ @var
%! @item M
%! Dynare structure describing the model, with updated parameters and covariances matrices.
%! @end table
%! @sp 2
%! @strong{This function is called by:}
%! @sp 1
%! @ref{DsgeSmoother}, @ref{dynare_estimation_1}, @ref{@@gsa/filt_mc_}, @ref{identification_analysis}, @ref{PosteriorFilterSmootherAndForecast}, @ref{prior_posterior_statistics_core}, @ref{prior_sampler}
%! @sp 2
%! @strong{This function calls:}
%! @sp 2
%! @end deftypefn
%@eod:

% Copyright (C) 2003-2009, 2012 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/>.

nvx = estim_params.nvx;
ncx = estim_params.ncx;
nvn = estim_params.nvn;
ncn = estim_params.ncn;
np = estim_params.np;
Sigma_e = M.Sigma_e;
H = M.H;

% setting shocks variance
if nvx
    var_exo = estim_params.var_exo;
    for i=1:nvx
        k =var_exo(i,1);
        Sigma_e(k,k) = xparam1(i)^2;
    end
end
% update offset
offset = nvx;

% setting measument error variance
if nvn
    var_endo = estim_params.var_endo;
    for i=1:nvn
        k = var_endo(i,1);
        H(k,k) = xparam1(i+offset)^2;
    end
end

% update offset
offset = nvx+nvn;

% setting shocks covariances
if ~isempty(M.Correlation_matrix)
    Sigma_e = diag(sqrt(diag(Sigma_e)))*M.Correlation_matrix*diag(sqrt(diag(Sigma_e)));
end
if ncx
    corrx = estim_params.corrx;
    for i=1:ncx
        k1 = corrx(i,1);
        k2 = corrx(i,2);
        M.Correlation_matrix(k1,k2) = xparam1(i+offset);
        M.Correlation_matrix(k2,k1) = M.Correlation_matrix(k1,k2);
        Sigma_e(k1,k2) = xparam1(i+offset)*sqrt(Sigma_e(k1,k1)*Sigma_e(k2,k2));
        Sigma_e(k2,k1) = Sigma_e(k1,k2);
    end
end

% update offset
offset = nvx+nvn+ncx;
% setting measurement error covariances
if ncn
    corrn = estim_params.corrn;
    for i=1:ncn
        k1 = corr(i,1);
        k2 = corr(i,2);
        H(k1,k2) = xparam1(i+offset)*sqrt(H(k1,k1)*H(k2,k2));
        H(k2,k1) = H(k1,k2);
    end
end

% update offset
offset = nvx+ncx+nvn+ncn;
% setting structural parameters
%
if np
    M.params(estim_params.param_vals(:,1)) = xparam1(offset+1:end);
end

% updating matrices in M
if nvx || ncx
    M.Sigma_e = Sigma_e;
end
if nvn
    M.H = H;
end