diff --git a/src/DSMH_initialization.m b/src/DSMH_initialization.m
new file mode 100644
index 000000000..38e16da4e
--- /dev/null
+++ b/src/DSMH_initialization.m
@@ -0,0 +1,118 @@
+function [ ix2, temperedlogpost, loglik, ModelName, MetropolisFolder, npar, NumberOfParticles, bayestopt_] = ...
+    DSMH_initialization(TargetFun, xparam1, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
+% function [ ix2, ilogpo2, ModelName, MetropolisFolder, FirstBlock, FirstLine, npar, NumberOfParticles, bayestopt_] = ...
+%     DSMH_initialization(TargetFun, xparam1, mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
+% Dynamic Striated Metropolis-Hastings initialization.
+%
+% INPUTS
+%   o TargetFun  [char]     string specifying the name of the objective
+%                           function (tempered posterior kernel and likelihood).
+%   o xparam1    [double]   (p*1) vector of parameters to be estimated (initial values).
+%   o mh_bounds  [double]   (p*2) matrix defining lower and upper bounds for the parameters.
+%   o dataset_              data structure
+%   o dataset_info          dataset info structure
+%   o options_              options structure
+%   o M_                    model structure
+%   o estim_params_         estimated parameters structure
+%   o bayestopt_            estimation options structure
+%   o oo_                   outputs structure
+%
+% OUTPUTS
+%   o ix2                   [double]    (NumberOfParticles*npar) vector of starting points for different chains
+%   o ilogpo2               [double]    (NumberOfParticles*1) vector of initial posterior values for different chains
+%   o iloglik2              [double]    (NumberOfParticles*1) vector of initial likelihood values for different chains
+%   o ModelName             [string]    name of the mod-file
+%   o MetropolisFolder      [string]    path to the Metropolis subfolder
+%   o npar                  [scalar]    number of parameters estimated
+%   o NumberOfParticles     [scalar]    Number of particles requested for the parameters distributions
+%   o bayestopt_            [structure] estimation options structure
+%
+% SPECIAL REQUIREMENTS
+%   None.
+
+% Copyright (C) 2006-2017 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/>.
+
+%Initialize outputs
+ix2 = [];
+ilogpo2 = [];
+iloglik2 = [];
+ModelName = [];
+MetropolisFolder = [];
+npar = [];
+NumberOfParticles = [];
+
+ModelName = M_.fname;
+if ~isempty(M_.bvar)
+    ModelName = [ModelName '_bvar'];
+end
+
+MetropolisFolder = CheckPath('dsmh',M_.dname);
+BaseName = [MetropolisFolder filesep ModelName];
+
+NumberOfParticles = options_.dsmh.number_of_particles; %Number of particles for the parameters
+npar  = length(xparam1);
+
+% Here we start a new DS Metropolis-Hastings, previous draws are discarded.
+disp('Estimation::dsmh: Initialization...')
+% Delete old dsmh files if any...
+files = dir([BaseName '_dsmh*_blck*.mat']);
+%if length(files)
+%    delete([BaseName '_dsmh*_blck*.mat']);
+%    disp('Estimation::smc: Old dsmh-files successfully erased!')
+%end
+% Delete old log file.
+file = dir([ MetropolisFolder '/dsmh.log']);
+%if length(file)
+%    delete([ MetropolisFolder '/dsmh.log']);
+%    disp('Estimation::dsmh: Old dsmh.log file successfully erased!')
+%    disp('Estimation::dsmh: Creation of a new dsmh.log file.')
+%end
+fidlog = fopen([MetropolisFolder '/dsmh.log'],'w');
+fprintf(fidlog,'%% DSMH log file (Dynare).\n');
+fprintf(fidlog,['%% ' datestr(now,0) '.\n']);
+fprintf(fidlog,' \n\n');
+fprintf(fidlog,'%% Session 1.\n');
+fprintf(fidlog,' \n');
+prior_draw(bayestopt_,options_.prior_trunc);
+% Find initial values for the NumberOfParticles chains...
+set_dynare_seed('default');
+fprintf(fidlog,['  Initial values of the parameters:\n']);
+disp('Estimation::dsmh: Searching for initial values...');
+ix2 = zeros(npar,NumberOfParticles);
+temperedlogpost = zeros(NumberOfParticles,1);
+loglik = zeros(NumberOfParticles,1);
+%stderr = sqrt(bsxfun(@power,mh_bounds.ub-mh_bounds.lb,2)/12)/10;
+for j=1:NumberOfParticles
+    validate = 0;
+    while validate == 0
+    	candidate = prior_draw()';
+%        candidate = xparam1(:) + 0.001*randn(npar,1);%bsxfun(@times,stderr,randn(npar,1)) ;
+        if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
+            ix2(:,j) = candidate ;
+            [temperedlogpost(j),loglik(j)] = tempered_likelihood(TargetFun,candidate,0.0,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
+            if isfinite(loglik(j)) % if returned log-density is Inf or Nan (penalized value)
+                validate = 1;
+            end
+        end
+    end
+end
+fprintf(fidlog,' \n');
+disp('Estimation::dsmh: Initial values found!')
+skipline()
+
+
diff --git a/src/DSMH_sampler.m b/src/DSMH_sampler.m
new file mode 100644
index 000000000..56c9185a0
--- /dev/null
+++ b/src/DSMH_sampler.m
@@ -0,0 +1,303 @@
+function DSMH_sampler(TargetFun,xparam1,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
+% function DSMH_sampler(TargetFun,ProposalFun,xparam1,sampler_options,mh_bounds,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_)
+% Dynamic Striated Metropolis-Hastings algorithm.
+%
+% INPUTS
+%   o TargetFun  [char]     string specifying the name of the objective
+%                           function (posterior kernel).
+%   o ProposalFun  [char]   string specifying the name of the proposal
+%                           density
+%   o xparam1    [double]   (p*1) vector of parameters to be estimated (initial values).
+%   o sampler_options       structure
+%            .invhess       [double]   (p*p) matrix, posterior covariance matrix (at the mode).
+%   o mh_bounds  [double]   (p*2) matrix defining lower and upper bounds for the parameters.
+%   o dataset_              data structure
+%   o dataset_info          dataset info structure
+%   o options_              options structure
+%   o M_                    model structure
+%   o estim_params_         estimated parameters structure
+%   o bayestopt_            estimation options structure
+%   o oo_                   outputs structure
+%
+% SPECIAL REQUIREMENTS
+%   None.
+%
+% PARALLEL CONTEXT
+% The most computationally intensive part of this function may be executed
+% in parallel. The code suitable to be executed in
+% parallel on multi core or cluster machine (in general a 'for' cycle)
+% has been removed from this function and been placed in the posterior_sampler_core.m funtion.
+%
+% The DYNARE parallel packages comprise a i) set of pairs of Matlab functions that can be executed in
+% parallel and called name_function.m and name_function_core.m and ii) a second set of functions used
+% to manage the parallel computations.
+%
+% This function was the first function to be parallelized. Later, other
+% functions have been parallelized using the same methodology.
+% Then the comments write here can be used for all the other pairs of
+% parallel functions and also for management functions.
+
+% Copyright (C) 2006-2017 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/>.
+
+
+lambda = exp(bsxfun(@minus,options_.dsmh.H,1:1:options_.dsmh.H)/(options_.dsmh.H-1)*log(options_.dsmh.lambda1));
+c = 55 ; 
+
+% Step 0: Initialization of the sampler
+[ param, tlogpost_iminus1, loglik, ~, ~, npar, nparticles, bayestopt_] = ...
+    DSMH_initialization(TargetFun, xparam1, mh_bounds, dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,oo_);
+
+ESS = zeros(options_.dsmh.H,1) ;
+zhat = 1 ;
+
+% The DSMH starts here 
+  for i=1:options_.dsmh.H
+    disp('');
+    disp('Tempered iteration');  
+	disp(i) ;
+    % Step 1: sort the densities and compute IS weigths 
+    [tlogpost_iminus1,loglik,param] = sort_matrices(tlogpost_iminus1,loglik,param) ;
+    [tlogpost_i,weights,zhat,ESS,mu,Omegachol] = compute_IS_weights_and_moments(param,tlogpost_iminus1,loglik,lambda,i,zhat,ESS) ;
+    % Step 2: tune c_i
+    c = tune_c(TargetFun,param,tlogpost_i,lambda,i,c,Omegachol,weights,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
+    % Step 3: Metropolis step
+    [param,tlogpost_iminus1,loglik] = mutation_DSMH(TargetFun,param,tlogpost_i,tlogpost_iminus1,loglik,lambda,i,c,Omegachol,weights,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
+  end
+  
+weights = exp(loglik*(lambda(end)-lambda(end-1)));
+weights = weights/sum(weights);
+indx_resmpl = DSMH_resampling(weights,rand(1,1),nparticles);
+distrib_param = param(:,indx_resmpl);   
+
+%% Plot parameters densities
+TeX = options_.TeX;
+
+[nbplt,nr,nc,lr,lc,nstar] = pltorg(npar);
+
+if TeX
+    fidTeX = fopen([M_.fname '_param_density.tex'],'w');
+    fprintf(fidTeX,'%% TeX eps-loader file generated by DSMH.m (Dynare).\n');
+    fprintf(fidTeX,['%% ' datestr(now,0) '\n']);
+    fprintf(fidTeX,' \n');
+end
+
+number_of_grid_points = 2^9;      % 2^9 = 512 !... Must be a power of two.
+bandwidth = 0;                    % Rule of thumb optimal bandwidth parameter.
+kernel_function = 'gaussian';     % Gaussian kernel for Fast Fourier Transform approximation.
+for plt = 1:nbplt,
+    if TeX
+        NAMES = [];
+        TeXNAMES = [];
+    end
+    hh = dyn_figure(options_.nodisplay,'Name','Parameters Densities');
+    for k=1:min(nstar,npar-(plt-1)*nstar)
+        subplot(nr,nc,k)
+        kk = (plt-1)*nstar+k;
+        [name,texname] = get_the_name(kk,TeX,M_,estim_params_,options_);
+        if TeX
+            if isempty(NAMES)
+                NAMES = name;
+                TeXNAMES = texname;
+            else
+                NAMES = char(NAMES,name);
+                TeXNAMES = char(TeXNAMES,texname);
+            end
+        end
+        optimal_bandwidth = mh_optimal_bandwidth(distrib_param(kk,:)',nparticles,bandwidth,kernel_function);
+        [density(:,1),density(:,2)] = kernel_density_estimate(distrib_param(kk,:)',number_of_grid_points,...
+                                                          nparticles,optimal_bandwidth,kernel_function);
+        plot(density(:,1),density(:,2));
+        hold on
+        title(name,'interpreter','none')
+        hold off
+        axis tight
+        drawnow
+    end
+    dyn_saveas(hh,[ M_.fname '_param_density' int2str(plt) ],options_.nodisplay,options_.graph_format);
+    if TeX
+        % TeX eps loader file
+        fprintf(fidTeX,'\\begin{figure}[H]\n');
+        for jj = 1:min(nstar,length(x)-(plt-1)*nstar)
+            fprintf(fidTeX,'\\psfrag{%s}[1][][0.5][0]{%s}\n',deblank(NAMES(jj,:)),deblank(TeXNAMES(jj,:)));
+        end
+        fprintf(fidTeX,'\\centering \n');
+        fprintf(fidTeX,'\\includegraphics[scale=0.5]{%s_ParametersDensities%s}\n',M_.fname,int2str(plt));
+        fprintf(fidTeX,'\\caption{ParametersDensities.}');
+        fprintf(fidTeX,'\\label{Fig:ParametersDensities:%s}\n',int2str(plt));
+        fprintf(fidTeX,'\\end{figure}\n');
+        fprintf(fidTeX,' \n');
+    end
+end
+
+function indx = DSMH_resampling(weights,noise,number)
+    indx = zeros(number,1);
+    cumweights = cumsum(weights);
+    randvec = (transpose(1:number)-1+noise(:))/number;
+    j = 1;
+    for i=1:number
+        while (randvec(i)>cumweights(j))
+            j = j+1;
+        end
+        indx(i) = j;
+    end
+    
+function [tlogpost_iminus1,loglik,param] = sort_matrices(tlogpost_iminus1,loglik,param)
+    [~,indx_ord] = sortrows(tlogpost_iminus1);
+    tlogpost_iminus1 = tlogpost_iminus1(indx_ord);
+    param = param(:,indx_ord);
+    loglik = loglik(indx_ord);
+
+function [tlogpost_i,weights,zhat,ESS,mu,Omegachol] = compute_IS_weights_and_moments(param,tlogpost_iminus1,loglik,lambda,i,zhat,ESS)    
+    if i==1 
+		tlogpost_i = tlogpost_iminus1 + loglik*lambda(i); 
+    else 
+		tlogpost_i = tlogpost_iminus1 + loglik*(lambda(i)-lambda(i-1));
+    end
+    weights = exp(tlogpost_i-tlogpost_iminus1);
+    zhat = (mean(weights))*zhat ;
+    weights = weights/sum(weights);
+    ESS(i) = 1/sum(weights.^2);
+    % estimates of mean and variance
+    mu = param*weights;
+    z = bsxfun(@minus,param,mu);
+    Omega = z*diag(weights)*z';
+    Omegachol = chol(Omega)';
+
+function c = tune_c(TargetFun,param,tlogpost_i,lambda,i,c,Omegachol,weights,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_)
+	disp('tuning c_i...');
+    disp('Initial value =');
+    disp(c) ;
+    npar = size(param,1);
+    lower_prob = (.5*(options_.dsmh.alpha0+options_.dsmh.alpha1))^5;
+    upper_prob = (.5*(options_.dsmh.alpha0+options_.dsmh.alpha1))^(1/5);
+	stop=0 ;
+	while stop==0
+		acpt = 0.0;
+		indx_resmpl = DSMH_resampling(weights,rand(1,1),options_.dsmh.G);
+		param0 = param(:,indx_resmpl);
+		tlogpost0 = tlogpost_i(indx_resmpl);
+		for j=1:options_.dsmh.G
+			for l=1:options_.dsmh.K
+			    validate = 0;
+			    while validate == 0 
+					candidate = param0(:,j) + sqrt(c)*Omegachol*randn(npar,1);
+					if all(candidate >= mh_bounds.lb) && all(candidate <= mh_bounds.ub)
+						[tlogpostx,loglikx] = tempered_likelihood(TargetFun,candidate,lambda(i),dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
+						if isfinite(loglikx) % if returned log-density is not Inf or Nan (penalized value)
+						    validate = 1;
+            				if rand(1,1)<exp(tlogpostx-tlogpost0(j)) % accept
+                        	  acpt = acpt + 1/(options_.dsmh.G*options_.dsmh.K);
+                              param0(:,j)= candidate;
+                              tlogpost0(j) = tlogpostx;
+            				end
+						end
+					end
+ 				end
+			end 
+        end
+        disp('Acceptation rate =') ;
+        disp(acpt) ;
+		if options_.dsmh.alpha0<=acpt && acpt<=options_.dsmh.alpha1
+			disp('done!');
+			stop=1;
+		else
+			if acpt<lower_prob
+			  c = c/5;
+			elseif lower_prob<=acpt && acpt<=upper_prob
+			  c = c*log(.5*(options_.dsmh.alpha0+options_.dsmh.alpha1))/log(acpt);
+			else
+			  c = 5*c;
+            end
+            disp('Trying with c= ') ;
+            disp(c) 
+		end
+	end
+
+function [out_param,out_tlogpost_iminus1,out_loglik] = mutation_DSMH(TargetFun,param,tlogpost_i,tlogpost_iminus1,loglik,lambda,i,c,Omegachol,weights,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_)
+    indx_levels = (1:1:options_.dsmh.M-1)*options_.dsmh.N*options_.dsmh.G/options_.dsmh.M;
+    npar = size(param,1) ;
+    p = 1/(10*options_.dsmh.tau);
+    disp('Metropolis step...'); 
+    % build the dynamic grid of levels
+    levels = [0.0;tlogpost_iminus1(indx_levels)];
+	% initialize the outputs 
+	out_param = param;
+	out_tlogpost_iminus1 = tlogpost_i;
+	out_loglik = loglik;
+	% resample and initialize the starting groups 
+	indx_resmpl = DSMH_resampling(weights,rand(1,1),options_.dsmh.G);
+    param0 = param(:,indx_resmpl);
+    tlogpost_iminus10 = tlogpost_iminus1(indx_resmpl);
+    tlogpost_i0 = tlogpost_i(indx_resmpl);
+    loglik0 = loglik(indx_resmpl);
+    % Start the Metropolis 
+    for l=1:options_.dsmh.N*options_.dsmh.tau
+      for j=1:options_.dsmh.G
+        u1 = rand(1,1);
+        u2 = rand(1,1);
+        if u1<p
+            k=1 ;
+            for m=1:options_.dsmh.M-1
+                if levels(m)<=tlogpost_iminus10(j) && tlogpost_iminus10(j)<levels(m+1)
+                    k = m+1;
+                    break
+                end
+            end
+            indx = floor( (k-1)*options_.dsmh.N*options_.dsmh.G/options_.dsmh.M+1 + u2*(options_.dsmh.N*options_.dsmh.G/options_.dsmh.M-1) );
+            if i==1
+                alp = (loglik(indx)-loglik0(j))*lambda(i);
+            else 
+                alp = (loglik(indx)-loglik0(j))*(lambda(i)-lambda(i-1));
+            end 
+            if u2<exp(alp)
+                param0(:,j) = param(:,indx);
+                tlogpost_i0(j) = tlogpost_i(indx);
+                loglik0(j) = loglik(indx);
+                tlogpost_iminus10(j) = tlogpost_iminus1(indx);
+            end
+        else
+		    validate= 0;
+		    while validate==0 
+				candidate = param0(:,j) + sqrt(c)*Omegachol*randn(npar,1);
+				if all(candidate(:) >= mh_bounds.lb) && all(candidate(:) <= mh_bounds.ub)
+					[tlogpostx,loglikx] = tempered_likelihood(TargetFun,candidate,lambda(i),dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,mh_bounds,oo_);
+					if isfinite(loglikx) % if returned log-density is not Inf or Nan (penalized value)
+					    validate = 1;
+                        if u2<exp(tlogpostx-tlogpost_i0(j)) % accept
+                        	param0(:,j) = candidate;
+                        	tlogpost_i0(j) = tlogpostx;
+                            loglik0(j) = loglikx;
+            				if i==1 
+                            	tlogpost_iminus10(j) = tlogpostx-loglikx*lambda(i);
+                            else                 	
+                        		tlogpost_iminus10(j) = tlogpostx-loglikx*(lambda(i)-lambda(i-1));
+                            end     
+                        end
+					end
+				end
+			end
+        end
+      end
+      if mod(l,options_.dsmh.tau)==0 
+        rang = (l/options_.dsmh.tau-1)*options_.dsmh.G+1:l*options_.dsmh.G/options_.dsmh.tau;
+        out_param(:,rang) = param0;
+        out_tlogpost_iminus1(rang) = tlogpost_i0;
+        out_loglik(rang) = loglik0;
+      end
+    end
+    
\ No newline at end of file
diff --git a/src/tempered_likelihood.m b/src/tempered_likelihood.m
new file mode 100644
index 000000000..4af11080a
--- /dev/null
+++ b/src/tempered_likelihood.m
@@ -0,0 +1,5 @@
+    function [tlogpostkern,loglik] = tempered_likelihood(TargetFun,xparam1,lambda,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_)
+       logpostkern = -feval(TargetFun,xparam1,dataset_,dataset_info,options_,M_,estim_params_,bayestopt_,bounds,oo_);
+       logprior = priordens(xparam1,bayestopt_.pshape,bayestopt_.p6,bayestopt_.p7,bayestopt_.p3,bayestopt_.p4);
+       loglik = logpostkern-logprior ;
+       tlogpostkern = lambda*loglik + logprior;