% mucsv_tvp.m
% Updated Sept 22, 2015
% pcomp data
% multivariate UCSV Model 
% TVP (Random Walk) Factor loadings

clear all;
small = 1.0e-10;
big = 1.0e+6;                  
rng(663436);

  % -- File Directories  
  outdir = '/Users/mwatson/Dropbox/p_comp/revision/matlab/out/';
  figdir = '/Users/mwatson/Dropbox/p_comp/revision/matlab/fig/';
  matdir = '/Users/mwatson/Dropbox/p_comp/revision/matlab/mat/';
  
%   % -- Read in Data --- 
%   load_data = 1;  % 1 if reloading data from Excel, etc 
%   mtoq_agg = 0;   % Temporal aggregation indicator of monthly to quarterly data
%   pcomp_data_calendar_m_and_q;  
%   % Data Series Used
%   dp_agg = dp_agg_q;
%   dp_agg_xfe = dp_agg_xfe_q;
%   dp_agg_xe = dp_agg_xe_q;
%   dp_disagg = dp_disagg_3comp_q;
%   share_avg = share_avg_3comp_q;
%   share_avg_xfe = share_avg_3comp_xfe_q;
%   share_avg_xe = share_avg_3comp_xe_q;
%   calvec = calvec_q;
%   dnobs = dnobs_q;
%   calds = calds_q;
%   nper = 4;
%   mlabel = '_mucsv_tvp_3c_Q0';  
  
% -- Read in Data --- 
  load_data = 1;  % 1 if reloading data from Excel, etc 
  mtoq_agg = 3;   % Temporal aggregation indicator of monthly to quarterly data
  pcomp_data_calendar_m_and_q;
  %  Data Series Used
  dp_agg = dp_agg_q;
  dp_agg_xfe = dp_agg_xfe_q;
  dp_agg_xe = dp_agg_xe_q;
  dp_disagg = dp_disagg_3comp_q;
  share_avg = share_avg_3comp_q;
  share_avg_xfe = share_avg_3comp_xfe_q;
  share_avg_xe = share_avg_3comp_xe_q;
  calvec = calvec_q;
  dnobs = dnobs_q;
  calds = calds_q;
  nper = 4;
  mlabel = '_mucsv_tvp_3c_Q3';
  
%  % -- Read in Data --- 
%   load_data = 1;  % 1 if reloading data from Excel, etc 
%   mtoq_agg = 0;   % Temporal aggregation indicator of monthly to quarterly data
%   pcomp_data_calendar_m_and_q;  
%   % Data Series Used
%   dp_agg = dp_agg_m;
%   dp_agg_xfe = dp_agg_xfe_m;
%   dp_agg_xe = dp_agg_xe_m;
%   dp_disagg = dp_disagg_3comp_m;
%   share_avg = share_avg_3comp_m;
%   share_avg_xfe = share_avg_3comp_xfe_m;
%   share_avg_xe = share_avg_3comp_xe_m;
%   calvec = calvec_m;
%   dnobs = dnobs_m;
%   calds = calds_m;
%   nper = 12;
%   mlabel = '_mucsv_tvp_3c_M';
 
  labelvec_disagg = labelvec_disagg_3comp;
  namevec_disagg = namevec_disagg_3comp;
  n_incl = n_incl_3comp;
  
  % --- Sample Period for Analyais 
  first_date = [1960 1];
  last_date = calds(end,:);
  ismpl = smpl(calvec,first_date,last_date,nper);
  calvec_ismpl = calvec(ismpl==1);
  notim = size(calvec_ismpl,1);
  str_tmp = [matdir 'calvec_ismpl' mlabel]; save(str_tmp,'calvec_ismpl');
 
  % Parameters for UCSV Draws
  n_burnin = 5000;  % Discarded Draws
  n_draws_save = 5000;  % Number of Draws to Save
  k_draws = 10;         % Save results every k_draws
  
  n_draws =  n_draws_save*k_draws;   % Total Number of Draws after burnin
  
  % Parameters for scale mixture of epsilon component
  scl_eps_vec = [1; linspace(2.0,10.0,9)'];
  ps_mean = 1 - 1/(4*nper);              % Outlier every 4 years
  ps_prior_obs = nper*10;                % Sample size of 10 years for prior
  ps_prior_a = ps_mean*ps_prior_obs;     % "alpha" in beta prior
  ps_prior_b = (1-ps_mean)*ps_prior_obs; % "beta" in beta prior
  ps_prior = [ps_prior_a ps_prior_b];
  
  % -- Data -- 
  dp_mat = dp_disagg(ismpl==1,:);
  n_y = size(dp_mat,2);
   
  % Scale data:  This scale matters because of the off-set parameters "c" in 
  % draw_lcs_indicators and draw_sigma;
  dy = dp_mat(2:end,:)-dp_mat(1:end-1,:);
  sd_ddp = NaN*zeros(n_y,1);
  for i = 1:n_y;
   tmp = dy(:,i);
   inan = isnan(tmp);
   sd_ddp(i) = std(tmp(inan==0));
  end;
  sd_ddp_median = median(sd_ddp);
  scale_y = sd_ddp_median/5;
  dp_mat_n = dp_mat/scale_y;
  
  % -- Parameters for model
   % 10-component mixture approximation to log chi-squared(1) from Omori, Chib, Shephard, and Nakajima JOE (2007)
   r_p = [0.00609 0.04775 0.13057 0.20674 0.22715 0.18842 0.12047 0.05591 0.01575 0.00115]';
   r_m = [1.92677 1.34744 0.73504 0.02266 -0.85173 -1.97278 -3.46788 -5.55246 -8.68384 -14.65000]';
   r_v = [0.11265 0.17788 0.26768 0.40611 0.62699 0.98583 1.57469 2.54498 4.16591 7.33342]'; 
   r_s = sqrt(r_v);
   
   % Prior for g
   % Gvalues .. for evolution of standard deviations over a year (nper periods)
   ng = 5;      % Number of grid points for approximate uniform prior
   g_dtau_min = 0.001;
   g_dtau_max = 0.20;
   g_dtau_unique_values = linspace(g_dtau_min,g_dtau_max,ng)';
   p_g_dtau_unique_values = ones(ng,1)/ng;
   
   g_eps_min = 0.001;
   g_eps_max = 0.20;
   g_eps_unique_values = linspace(g_eps_min,g_eps_max,ng)';
   p_g_eps_unique_values = ones(ng,1)/ng;
   
   g_dtau_min = 0.001;
   g_dtau_max = 0.20;
   g_dtau_common_values = linspace(g_dtau_min,g_dtau_max,ng)';
   p_g_dtau_common_values = ones(ng,1)/ng;
   
   g_eps_min = 0.001;
   g_eps_max = 0.20;
   g_eps_common_values = linspace(g_eps_min,g_eps_max,ng)';
   p_g_eps_common_values = ones(ng,1)/ng;
   
   
   % Convert to standard deviation per period
   g_dtau_unique_values = g_dtau_unique_values/sqrt(nper);
   g_eps_unique_values = g_eps_unique_values/sqrt(nper);
   g_dtau_common_values = g_dtau_common_values/sqrt(nper);
   g_eps_common_values = g_eps_common_values/sqrt(nper);
   
   % Convert to g-values for variances instead of standard deviations (ln(s^2) = 2*ln(s))
   g_dtau_unique_values=2*g_dtau_unique_values;
   g_eps_unique_values=2*g_eps_unique_values;
   g_dtau_common_values=2*g_dtau_common_values;
   g_eps_common_values=2*g_eps_common_values;
   
   % Save priors
   g_eps_unique_prior = [g_eps_unique_values p_g_eps_unique_values];
   g_dtau_unique_prior = [g_dtau_unique_values p_g_dtau_unique_values];
   g_eps_common_prior = [g_eps_common_values p_g_eps_common_values];
   g_dtau_common_prior = [g_dtau_common_values p_g_dtau_common_values];
  
   % Parameters for prior for factor loadings -- note these
   % depend on scaling (scale_y) introduced above, so they are in scaled y
   % units
   % ... inital values of factor loadings
   omega_tau = 10/scale_y;
   omega_eps = 10/scale_y;
   sigma_tau = 0.4/scale_y;
   sigma_eps = 0.4/scale_y;
   var_alpha_tau = ((omega_tau^2)*ones(n_y,n_y)) + ((sigma_tau^2)*eye(n_y));
   var_alpha_eps = ((omega_eps^2)*ones(n_y,n_y)) + ((sigma_eps^2)*eye(n_y));
   prior_var_alpha = zeros(2*n_y,2*n_y);
   prior_var_alpha(1:n_y,1:n_y) = var_alpha_eps;
   prior_var_alpha(n_y+1:2*n_y,n_y+1:2*n_y) = var_alpha_tau;
   % Alpha TVP parameters -- use "Number of prior obs (nu) and prior squared (s2)" as parameters .. as in Del Negro and Otrok;
   nu_prior_alpha = 0.1*notim;             
   s2_prior_alpha = (0.25/sqrt(notim))^2;
   s2_prior_alpha = s2_prior_alpha/(scale_y^2);
   
   % Initial values for sigma_alpha
   a = nu_prior_alpha/2;
   ssr = nu_prior_alpha*s2_prior_alpha;
   b = 2/ssr;
   var_dalpha = 1./gamrnd(a,b,[2*n_y,1]);
   sigma_dalpha = sqrt(var_dalpha);
   
   % Initial Values of parameters -- I save alpha values for each date, so the
   % same program can be used when TVP is allowed
   alpha_eps = rand(notim,n_y);
   alpha_tau = rand(notim,n_y);
   sigma_dtau_unique = rand(notim,n_y);
   sigma_eps_unique = rand(notim,n_y);
   sigma_dtau_common = rand(notim,1);
   sigma_eps_common = rand(notim,1);
   scale_eps_unique = ones(notim,n_y);
   scale_eps_common = ones(notim,1);
   
   % Initial Value of ps
   n_scl_eps = length(scl_eps_vec);
   ps = ps_prior(1)/(ps_prior(1)+ps_prior(2));
   ps2 = (1-ps)/(n_scl_eps-1);
   ps2 = ps2*ones(n_scl_eps-1,1);
   prob_scl_eps_vec = [ps ; ps2];
   prob_scl_eps_vec_common = prob_scl_eps_vec;
   prob_scl_eps_vec_unique = repmat(prob_scl_eps_vec,1,n_y);
   
   % Matrices for saving draws
   % -- Standard Deviations 
   sigma_eps_common_draws = NaN*ones(notim,n_draws_save);
   sigma_dtau_common_draws = NaN*ones(notim,n_draws_save);
   sigma_eps_unique_draws = NaN*ones(notim,n_y,n_draws_save);
   sigma_dtau_unique_draws = NaN*ones(notim,n_y,n_draws_save);
   % --- Scale for outliers in eps_unique --
   scale_eps_unique_draws = NaN*ones(notim,n_y,n_draws_save);
   sigma_eps_unique_total_draws = NaN*ones(notim,n_y,n_draws_save);
   ps_unique_draws = NaN*zeros(n_draws_save,n_y);
   % --- Scale for outliers in eps_common --
   scale_eps_common_draws = NaN*ones(notim,n_draws_save);
   sigma_eps_common_total_draws = NaN*ones(notim,n_draws_save);
   ps_common_draws = NaN*zeros(n_draws_save,1);
   % Factor Loadings
   alpha_eps_draws = NaN*ones(notim,n_y,n_draws_save);
   alpha_tau_draws = NaN*ones(notim,n_y,n_draws_save);
   % g-values 
   g_eps_common_draws = NaN*ones(n_draws_save,1);
   g_dtau_common_draws = NaN*ones(n_draws_save,1);
   g_eps_unique_draws = NaN*ones(n_y,n_draws_save);
   g_dtau_unique_draws = NaN*ones(n_y,n_draws_save);
   % Decomposition of series
   y_tau_common_draws = NaN*ones(notim,n_y,n_draws_save);
   y_eps_common_draws = NaN*ones(notim,n_y,n_draws_save);
   y_tau_unique_draws = NaN*ones(notim,n_y,n_draws_save);
   y_eps_unique_draws = NaN*ones(notim,n_y,n_draws_save);
   y_f_tau_common_draws = NaN*ones(notim,n_y,n_draws_save);
   y_f_tau_unique_draws = NaN*ones(notim,n_y,n_draws_save);
   % Decomposition of variance for each series
   var_y_dtau_common_draws = NaN*ones(notim,n_y,n_draws_save);
   var_y_eps_common_draws = NaN*ones(notim,n_y,n_draws_save);
   var_y_eps_common_total_draws = NaN*ones(notim,n_y,n_draws_save);
   var_y_dtau_unique_draws = NaN*ones(notim,n_y,n_draws_save);
   var_y_eps_unique_draws = NaN*ones(notim,n_y,n_draws_save);
   var_y_eps_unique_total_draws = NaN*ones(notim,n_y,n_draws_save);
   % Sigma dalpha
   sigma_dalpha_draws = NaN*ones(2*n_y,n_draws_save);
   
  itmp = 0;
  tic;
  kk_draw  = 0;
  jj_draw = 0;
  for idraw=1:n_burnin+n_draws;
  	sigma_eps_unique_scl = sigma_eps_unique.*scale_eps_unique;  % SD of eps_unique, which is stochastic volatility times scale in mixture distribution 
  	sigma_eps_common_scl = sigma_eps_common.*scale_eps_common;  % SD of eps_common, which is stochastic volatility times scale in mixture distribution 
  	% Step 1.a.1: draw tau, tau_f, dtau, and eps; 	
    [eps_common,tau_a_common,tau_a_unique,tau_f_common,tau_f_unique] = mdraw_eps_tau(dp_mat_n,alpha_eps,alpha_tau,sigma_eps_common_scl,sigma_dtau_common,sigma_eps_unique_scl,sigma_dtau_unique);
    dtau_common = tau_a_common(2:end)-tau_a_common(1:end-1);
    dtau_unique = tau_a_unique(2:end,:)-tau_a_unique(1:end-1,:);
    tau_common = tau_a_common(2:end);
    tau_unique = tau_a_unique(2:end,:);
    
    
    % Step 1.a.2 : Draw Factor Loadings 
     % -- Draw alpha_eps and alpha_tau;
     [alpha_eps, alpha_tau, dalpha_eps, dalpha_tau] = draw_alpha_tvp(dp_mat_n,prior_var_alpha,sigma_dalpha,tau_unique,eps_common,tau_common,sigma_eps_unique_scl); 
     
    % Step 1.a.3: Draw Standard Deviations of Alpha TVPs;
     dalpha = [dalpha_eps dalpha_tau];
     sigma_dalpha = draw_dalpha_sigma(dalpha,nu_prior_alpha, s2_prior_alpha);
    
    % Save some values
    y_eps_common = alpha_eps.*repmat(eps_common,1,n_y);
    y_tau_common = alpha_tau.*repmat(tau_common,1,n_y);
    y_tau_unique = tau_unique;
    eps_unique = dp_mat_n - y_eps_common - y_tau_common-y_tau_unique;
    y_eps_unique = eps_unique;
    y_f_tau_common = alpha_tau.*repmat(tau_f_common,1,n_y);
    y_f_tau_unique = tau_f_unique;
    
     
    % Step 1(b): Draw mixture indicators for log chi-squared(1)
    eps_unique = dp_mat_n - y_eps_common - y_tau_common-y_tau_unique;
    eps_unique_scaled = eps_unique./scale_eps_unique;
    eps_common_scaled = eps_common./scale_eps_common;
    ind_eps_common = draw_lcs_indicators(eps_common_scaled,sigma_eps_common,r_p,r_m,r_s); 
    ind_dtau_common = draw_lcs_indicators(dtau_common,sigma_dtau_common,r_p,r_m,r_s);
    ind_eps_unique = NaN*ones(notim,size(r_p,1),n_y);
    ind_dtau_unique = NaN*ones(notim,size(r_p,1),n_y);
    for i = 1:n_y;
     tmp = draw_lcs_indicators(eps_unique_scaled(:,i),sigma_eps_unique(:,i),r_p,r_m,r_s); 
     ind_eps_unique(:,:,i) = tmp;
     tmp = draw_lcs_indicators(dtau_unique(:,i),sigma_dtau_unique(:,i),r_p,r_m,r_s); 
     ind_dtau_unique(:,:,i) = tmp;
    end;
     
    % Step 2(a): Draw G
    i_init = 0;   % Variance = 1 as initial condition to identify factor loadings
    g_eps_common = draw_g(eps_common_scaled,g_eps_common_prior,ind_eps_common,r_m,r_s,i_init);
    g_dtau_common = draw_g(dtau_common,g_dtau_common_prior,ind_dtau_common,r_m,r_s,i_init);
    
    i_init = 1;   % Vague prior for initial variance;
    g_eps_unique = NaN*zeros(n_y,1);
    g_dtau_unique = NaN*zeros(n_y,1);
    for i = 1:n_y;
      g_eps_unique(i) = draw_g(eps_unique_scaled(:,i),g_eps_unique_prior,ind_eps_unique(:,:,i),r_m,r_s,i_init);
      g_dtau_unique(i) = draw_g(dtau_unique(:,i),g_dtau_unique_prior,ind_dtau_unique(:,:,i),r_m,r_s,i_init);
    end;
     
    % Step 2(b): Draw Volatilities
    i_init = 0;   % Variance = 1 as initial condition to identify factor loadings
    sigma_eps_common = draw_sigma(eps_common_scaled,g_eps_common,ind_eps_common,r_m,r_s,i_init);	
    sigma_dtau_common = draw_sigma(dtau_common,g_dtau_common,ind_dtau_common,r_m,r_s,i_init);	
    
    i_init = 1;  % Vague prior for initial variance;
    sigma_eps_unique = NaN*ones(notim,n_y);
    sigma_dtau_unique = NaN*ones(notim,n_y);
    for i = 1:n_y;
      sigma_eps_unique(:,i) = draw_sigma(eps_unique_scaled(:,i),g_eps_unique(i),ind_eps_unique(:,:,i),r_m,r_s,i_init);  
      sigma_dtau_unique(:,i) = draw_sigma(dtau_unique(:,i),g_dtau_unique(i),ind_dtau_unique(:,:,i),r_m,r_s,i_init);
    end;
    
    % Step 3: Draw Scale of epsilon
    scale_eps_common = draw_scale_eps(eps_common,sigma_eps_common,ind_eps_common,r_m,r_s,scl_eps_vec,prob_scl_eps_vec_common);
    scale_eps_unique = NaN*ones(notim,n_y);
    for i = 1:n_y; 
      scale_eps_unique(:,i) = draw_scale_eps(eps_unique(:,i),sigma_eps_unique(:,i),ind_eps_unique(:,:,i),r_m,r_s,scl_eps_vec,prob_scl_eps_vec_unique(:,i));
    end;
   
    % Step 4; Draw probability of outlier;
    prob_scl_eps_vec_common = draw_ps(scale_eps_common,ps_prior,n_scl_eps);
    for i = 1:n_y;
     prob_scl_eps_vec_unique(:,i) = draw_ps(scale_eps_unique(:,i),ps_prior,n_scl_eps);
    end;
     
    if idraw > n_burnin;
      kk_draw = kk_draw+1;
      if kk_draw == k_draws;
          jj_draw = jj_draw+1;
          kk_draw = 0;
          sigma_eps_common_draws(:,jj_draw) = sigma_eps_common;
          sigma_dtau_common_draws(:,jj_draw) = sigma_dtau_common;
          sigma_eps_unique_draws(:,:,jj_draw) = sigma_eps_unique;
          sigma_dtau_unique_draws(:,:,jj_draw) = sigma_dtau_unique;
          scale_eps_unique_draws(:,:,jj_draw) = scale_eps_unique;
          scale_eps_common_draws(:,jj_draw) = scale_eps_common;
          sigma_eps_unique_total_draws(:,:,jj_draw) = sigma_eps_unique.*scale_eps_unique;
          sigma_eps_common_total_draws(:,jj_draw) = sigma_eps_common.*scale_eps_common;
          alpha_eps_draws(:,:,jj_draw) = alpha_eps;
          alpha_tau_draws(:,:,jj_draw) = alpha_tau;
          g_eps_common_draws(jj_draw) = g_eps_common;
          ps_common_draws(jj_draw) = prob_scl_eps_vec_common(1);
          g_dtau_common_draws(jj_draw) = g_dtau_common;
          g_eps_unique_draws(:,jj_draw) = g_eps_unique;
          ps_unique_draws(jj_draw,:) = prob_scl_eps_vec_unique(1,:);
          g_dtau_unique_draws(:,jj_draw) = g_dtau_unique;
          y_eps_common_draws(:,:,jj_draw) = y_eps_common;
          y_tau_common_draws(:,:,jj_draw) = y_tau_common;
          y_tau_unique_draws(:,:,jj_draw) = y_tau_unique;
          y_eps_unique_draws(:,:,jj_draw) = y_eps_unique;
          y_f_tau_common_draws(:,:,jj_draw) = y_f_tau_common;
          y_f_tau_unique_draws(:,:,jj_draw) = y_f_tau_unique;
          var_y_eps_common_draws(:,:,jj_draw) = (alpha_eps.*repmat(sigma_eps_common,1,n_y)).^2;
          var_y_eps_common_total_draws(:,:,jj_draw) = (alpha_eps.*repmat(sigma_eps_common.*scale_eps_common,1,n_y)).^2;
          var_y_dtau_common_draws(:,:,jj_draw) = (alpha_tau.*repmat(sigma_dtau_common,1,n_y)).^2;
          var_y_eps_unique_draws(:,:,jj_draw) = sigma_eps_unique.^2;
          var_y_eps_unique_total_draws(:,:,jj_draw) = (sigma_eps_unique.*scale_eps_unique).^2;
          var_y_dtau_unique_draws(:,:,jj_draw) = sigma_dtau_unique.^2;
          sigma_dalpha_draws(:,jj_draw) = sigma_dalpha;
      end;
    end;
    
   itmp=itmp+1;
    if itmp == 500;
      itmp=0;
      fprintf('  Number of Draws: %6i    ',idraw);
      toc
      tic;
    end;
 
  end;  
  
  % Rescale so that things are in units of y 
  % Normalization set variance of common shocks to unity in initial period
  % Rescale factor loading and idiosynchratic shocks
  alpha_eps_draws = alpha_eps_draws*scale_y;
  alpha_tau_draws = alpha_tau_draws*scale_y;
  sigma_eps_unique_draws = sigma_eps_unique_draws*scale_y;
  sigma_eps_unique_total_draws = sigma_eps_unique_total_draws*scale_y;
  sigma_dtau_unique_draws = sigma_dtau_unique_draws*scale_y;
  y_eps_common_draws = y_eps_common_draws*scale_y;
  y_tau_common_draws = y_tau_common_draws*scale_y;
  y_eps_unique_draws = y_eps_unique_draws*scale_y;
  y_tau_unique_draws = y_tau_unique_draws*scale_y;
  y_f_tau_common_draws = y_f_tau_common_draws*scale_y;
  y_f_tau_unique_draws = y_f_tau_unique_draws*scale_y;
  var_y_eps_common_draws = var_y_eps_common_draws*scale_y*scale_y;
  var_y_eps_common_total_draws = var_y_eps_common_total_draws*scale_y*scale_y;
  var_y_eps_unique_draws = var_y_eps_unique_draws*scale_y*scale_y;
  var_y_eps_unique_total_draws = var_y_eps_unique_total_draws*scale_y*scale_y;
  var_y_dtau_common_draws = var_y_dtau_common_draws*scale_y*scale_y;
  var_y_dtau_unique_draws = var_y_dtau_unique_draws*scale_y*scale_y;
  sigma_dalpha_draws = sigma_dalpha_draws*scale_y;
  
  
  % ---------------- Summarize and save results ------------
  fprintf('Computing and saving summaries of draws \n');
   
  % --------- Percentiles for Some of Posteriors 
  % Posterior Mean and Quantiles of Alpha's
  pctvec = [0.05 1/6 0.50 5/6 0.95]';
  n_p = size(pctvec,1);
  
  % ---------------------- Compute Posterior for G's ---------------------
  g_eps_common_prior_post = g_summary(g_eps_common_prior,g_eps_common_draws);
  g_dtau_common_prior_post = g_summary(g_dtau_common_prior,g_dtau_common_draws);
  g_eps_unique_prior_post = g_summary(g_eps_unique_prior,g_eps_unique_draws');
  g_dtau_unique_prior_post = g_summary(g_dtau_unique_prior,g_dtau_unique_draws');
  str_tmp = [matdir 'g_eps_common_prior_post' mlabel]; save(str_tmp,'g_eps_common_prior_post');
  str_tmp = [matdir 'g_dtau_common_prior_post' mlabel]; save(str_tmp,'g_dtau_common_prior_post');
  str_tmp = [matdir 'g_eps_unique_prior_post' mlabel]; save(str_tmp,'g_eps_unique_prior_post');
  str_tmp = [matdir 'g_dtau_unique_prior_post' mlabel]; save(str_tmp,'g_dtau_unique_prior_post');
  
  % -------------------- Compute Posterior for PS -------------------------
  ps_common_mean_pct = post_mean_pct(ps_common_draws,pctvec);
  str_tmp = [matdir 'ps_common_mean_pct' mlabel]; save(str_tmp,'ps_common_mean_pct');
  ps_unique_mean_pct = post_mean_pct(ps_unique_draws,pctvec);
  str_tmp = [matdir 'ps_unique_mean_pct' mlabel]; save(str_tmp,'ps_unique_mean_pct');
  
  % Free up memory
  g_eps_common_draws = 1;
  g_dtau_common_draws = 1;
  g_eps_unique_draws = 1;
  g_dtau_unique_draws = 1;
  
  % ----------------------- Compute Posteriors for Alphas ----------------
  alpha_eps_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  alpha_tau_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  for i = 1:n_y
     tmp = alpha_eps_draws(:,i,:);
     tmp = reshape(tmp,notim,n_draws_save);
     tmp1 = post_mean_pct(tmp',pctvec);
     alpha_eps_mean_pct(:,:,i) = tmp1;
     
     tmp = alpha_tau_draws(:,i,:);
     tmp = reshape(tmp,notim,n_draws_save);
     tmp1 = post_mean_pct(tmp',pctvec);
     alpha_tau_mean_pct(:,:,i) = tmp1; 
  end;
  str_tmp = [matdir 'alpha_eps_mean_pct' mlabel]; save(str_tmp,'alpha_eps_mean_pct');
  str_tmp = [matdir 'alpha_tau_mean_pct' mlabel]; save(str_tmp,'alpha_tau_mean_pct');
  
  % Posterior for Scales -- unique
  scale_eps_unique_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  sigma_eps_unique_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  sigma_dtau_unique_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  for i = 1:n_y
     tmp = scale_eps_unique_draws(:,i,:);
     tmp = reshape(tmp,notim,n_draws_save);
     tmp1 = post_mean_pct(tmp',pctvec);
     scale_eps_unique_mean_pct(:,:,i) = tmp1;
     
     tmp = sigma_eps_unique_draws(:,i,:);
     tmp = reshape(tmp,notim,n_draws_save);
     tmp1 = post_mean_pct(tmp',pctvec);
     sigma_eps_unique_mean_pct(:,:,i) = tmp1;
     
     tmp = sigma_dtau_unique_draws(:,i,:);
     tmp = reshape(tmp,notim,n_draws_save);
     tmp1 = post_mean_pct(tmp',pctvec);
     sigma_dtau_unique_mean_pct(:,:,i) = tmp1;
  end;
  str_tmp = [matdir 'scale_eps_unique_mean_pct' mlabel]; save(str_tmp,'scale_eps_unique_mean_pct');
  str_tmp = [matdir 'sigma_eps_unique_mean_pct' mlabel]; save(str_tmp,'sigma_eps_unique_mean_pct');
  str_tmp = [matdir 'sigma_dtau_unique_mean_pct' mlabel]; save(str_tmp,'sigma_dtau_unique_mean_pct');
  
  
  % Posteriors for parameters on common components
  scale_eps_common_mean_pct = post_mean_pct(scale_eps_common_draws',pctvec);
  sigma_eps_common_mean_pct = post_mean_pct(sigma_eps_common_draws',pctvec);
  sigma_dtau_common_mean_pct = post_mean_pct(sigma_dtau_common_draws',pctvec);
  str_tmp = [matdir 'scale_eps_common_mean_pct' mlabel]; save(str_tmp,'scale_eps_common_mean_pct');
  str_tmp = [matdir 'sigma_eps_common_mean_pct' mlabel]; save(str_tmp,'sigma_eps_common_mean_pct');
  str_tmp = [matdir 'sigma_dtau_common_mean_pct' mlabel]; save(str_tmp,'sigma_dtau_common_mean_pct');
  
  
  % -------------------- Compute Posteriors for Historical Decomposition of Y variables ---- 
  y_eps_common_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  y_eps_unique_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  y_tau_common_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  y_tau_unique_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  y_f_tau_common_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  y_f_tau_unique_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  for i = 1:n_y;
      tmp = y_eps_common_draws(:,i,:);
      tmp = reshape(tmp,notim,n_draws_save);
      tmp1 = post_mean_pct(tmp',pctvec);
      y_eps_common_mean_pct(:,:,i) = tmp1;
      
      tmp = y_tau_common_draws(:,i,:);
      tmp = reshape(tmp,notim,n_draws_save);
      tmp1 = post_mean_pct(tmp',pctvec);
      y_tau_common_mean_pct(:,:,i) = tmp1;
      
      tmp = y_f_tau_common_draws(:,i,:);
      tmp = reshape(tmp,notim,n_draws_save);
      tmp1 = post_mean_pct(tmp',pctvec);
      y_f_tau_common_mean_pct(:,:,i) = tmp1;
      
      tmp = y_eps_unique_draws(:,i,:);
      tmp = reshape(tmp,notim,n_draws_save);
      tmp1 = post_mean_pct(tmp',pctvec);
      y_eps_unique_mean_pct(:,:,i) = tmp1;
      
      tmp = y_tau_unique_draws(:,i,:);
      tmp = reshape(tmp,notim,n_draws_save);
      tmp1 = post_mean_pct(tmp',pctvec);
      y_tau_unique_mean_pct(:,:,i) = tmp1;
      
      tmp = y_f_tau_unique_draws(:,i,:);
      tmp = reshape(tmp,notim,n_draws_save);
      tmp1 = post_mean_pct(tmp',pctvec);
      y_f_tau_unique_mean_pct(:,:,i) = tmp1;
  end;
  str_tmp = [matdir 'y_eps_common_mean_pct' mlabel]; save(str_tmp,'y_eps_common_mean_pct');
  str_tmp = [matdir 'y_eps_unique_mean_pct' mlabel]; save(str_tmp,'y_eps_unique_mean_pct');
  str_tmp = [matdir 'y_tau_common_mean_pct' mlabel]; save(str_tmp,'y_tau_common_mean_pct');
  str_tmp = [matdir 'y_tau_unique_mean_pct' mlabel]; save(str_tmp,'y_tau_unique_mean_pct'); 
 
 % -------------------- Compute Posteriors for Variance Decomposition of Y variables ---- 
  var_y_eps_common_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  var_y_eps_common_total_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  var_y_dtau_common_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  var_y_eps_unique_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  var_y_eps_unique_total_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  var_y_dtau_unique_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  var_y_eps_total_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  var_y_dtau_total_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  scale_eps_unique_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  scale_eps_common_mean_pct = NaN*zeros(notim,1+n_p,n_y);
  
  scale_eps_unique_draws(:,:,jj_draw) = scale_eps_unique;
          scale_eps_common_draws(:,jj_draw) = scale_eps_common;
  
  for i = 1:n_y;
      
      tmp = var_y_eps_common_draws(:,i,:);
      tmp = reshape(tmp,notim,n_draws_save);
      tmp1 = post_mean_pct(tmp',pctvec);
      var_y_eps_common_mean_pct(:,:,i) = tmp1;
      
      tmp = var_y_eps_common_total_draws(:,i,:);
      tmp = reshape(tmp,notim,n_draws_save);
      tmp1 = post_mean_pct(tmp',pctvec);
      var_y_eps_common_total_mean_pct(:,:,i) = tmp1;
      
      tmp = var_y_dtau_common_draws(:,i,:);
      tmp = reshape(tmp,notim,n_draws_save);
      tmp1 = post_mean_pct(tmp',pctvec);
      var_y_dtau_common_mean_pct(:,:,i) = tmp1;
      
      tmp = var_y_eps_unique_draws(:,i,:);
      tmp = reshape(tmp,notim,n_draws_save);
      tmp1 = post_mean_pct(tmp',pctvec);
      var_y_eps_unique_mean_pct(:,:,i) = tmp1;
      
      tmp = var_y_eps_unique_total_draws(:,i,:);
      tmp = reshape(tmp,notim,n_draws_save);
      tmp1 = post_mean_pct(tmp',pctvec);
      var_y_eps_unique_total_mean_pct(:,:,i) = tmp1;
      
      tmp = var_y_dtau_unique_draws(:,i,:);
      tmp = reshape(tmp,notim,n_draws_save);
      tmp1 = post_mean_pct(tmp',pctvec);
      var_y_dtau_unique_mean_pct(:,:,i) = tmp1;
      
      tmp = var_y_eps_unique_total_draws(:,i,:)+var_y_eps_common_total_draws(:,i,:);
      tmp = reshape(tmp,notim,n_draws_save);
      tmp1 = post_mean_pct(tmp',pctvec);
      var_y_eps_total_mean_pct(:,:,i) = tmp1;
      
      tmp = var_y_dtau_unique_draws(:,i,:)+var_y_dtau_common_draws(:,i,:);
      tmp = reshape(tmp,notim,n_draws_save);
      tmp1 = post_mean_pct(tmp',pctvec);
      var_y_dtau_total_mean_pct(:,:,i) = tmp1;
      
  end; 
  str_tmp = [matdir 'var_y_eps_common_mean_pct' mlabel]; save(str_tmp,'var_y_eps_common_mean_pct');
  str_tmp = [matdir 'var_y_eps_common_total_mean_pct' mlabel]; save(str_tmp,'var_y_eps_common_total_mean_pct');
  str_tmp = [matdir 'var_y_dtau_common_mean_pct' mlabel]; save(str_tmp,'var_y_dtau_common_mean_pct');
  str_tmp = [matdir 'var_y_eps_unique_mean_pct' mlabel]; save(str_tmp,'var_y_eps_unique_mean_pct');
  str_tmp = [matdir 'var_y_eps_unique_total_mean_pct' mlabel]; save(str_tmp,'var_y_eps_unique_total_mean_pct');
  str_tmp = [matdir 'var_y_dtau_unique_mean_pct' mlabel]; save(str_tmp,'var_y_dtau_unique_mean_pct');
  str_tmp = [matdir 'var_y_eps_total_mean_pct' mlabel]; save(str_tmp,'var_y_eps_total_mean_pct');
  str_tmp = [matdir 'var_y_dtau_total_mean_pct' mlabel]; save(str_tmp,'var_y_dtau_total_mean_pct');
  
  str_tmp = [matdir 'var_y_eps_common_mean_pct' mlabel]; save(str_tmp,'var_y_eps_common_mean_pct');
  str_tmp = [matdir 'var_y_eps_common_total_mean_pct' mlabel]; save(str_tmp,'var_y_eps_common_total_mean_pct');
  str_tmp = [matdir 'var_y_dtau_common_mean_pct' mlabel]; save(str_tmp,'var_y_dtau_common_mean_pct');
  str_tmp = [matdir 'var_y_eps_unique_mean_pct' mlabel]; save(str_tmp,'var_y_eps_unique_mean_pct');
  str_tmp = [matdir 'var_y_eps_unique_total_mean_pct' mlabel]; save(str_tmp,'var_y_eps_unique_total_mean_pct');
  str_tmp = [matdir 'var_y_dtau_unique_mean_pct' mlabel]; save(str_tmp,'var_y_dtau_unique_mean_pct');
  str_tmp = [matdir 'var_y_eps_total_mean_pct' mlabel]; save(str_tmp,'var_y_eps_total_mean_pct');
  str_tmp = [matdir 'var_y_dtau_total_mean_pct' mlabel]; save(str_tmp,'var_y_dtau_total_mean_pct');
  
  
  % ---------------- Posteriors for Aggregate, Chain Weighted, inflation ---------------
  % Chain Weights over this sample period 
  cw = share_avg(ismpl==1,:);
  cw_sq = cw.^2;
  cw_xfe = share_avg_xfe(ismpl==1,:);
  cw_xe = share_avg_xe(ismpl==1,:);
  
  
  agg_tau_common_draws = NaN*ones(notim,n_draws_save);
  agg_tau_unique_draws = NaN*ones(notim,n_draws_save);
  agg_f_tau_common_draws = NaN*ones(notim,n_draws_save);
  agg_f_tau_unique_draws = NaN*ones(notim,n_draws_save);
  
  agg_tau_common_xfe_draws = NaN*ones(notim,n_draws_save);
  agg_tau_unique_xfe_draws = NaN*ones(notim,n_draws_save);
  
  agg_tau_common_xe_draws = NaN*ones(notim,n_draws_save);
  agg_tau_unique_xe_draws = NaN*ones(notim,n_draws_save);
  
  var_agg_eps_common_total_draws = NaN*ones(notim,n_draws_save);
  var_agg_eps_unique_total_draws = NaN*ones(notim,n_draws_save);
  var_agg_dtau_common_draws = NaN*ones(notim,n_draws_save);
  var_agg_dtau_unique_draws = NaN*ones(notim,n_draws_save);
  for t = 1:notim;
      tmp = y_tau_common_draws(t,:,:);
      tmp = reshape(tmp,n_y,n_draws_save);
      agg_tau_common_draws(t,:) = cw(t,:)*tmp;
      agg_tau_common_xfe_draws(t,:) = cw_xfe(t,:)*tmp;
      agg_tau_common_xe_draws(t,:) = cw_xe(t,:)*tmp;
      
      tmp = y_tau_unique_draws(t,:,:);
      tmp = reshape(tmp,n_y,n_draws_save);
      agg_tau_unique_draws(t,:) = cw(t,:)*tmp;
      agg_tau_unique_xfe_draws(t,:) = cw_xfe(t,:)*tmp;
      agg_tau_unique_xe_draws(t,:) = cw_xe(t,:)*tmp;
      
      tmp = y_f_tau_common_draws(t,:,:);
      tmp = reshape(tmp,n_y,n_draws_save);
      agg_f_tau_common_draws(t,:) = cw(t,:)*tmp;
      
      tmp = y_f_tau_unique_draws(t,:,:);
      tmp = reshape(tmp,n_y,n_draws_save);
      agg_f_tau_unique_draws(t,:) = cw(t,:)*tmp;
      
      tmp = var_y_eps_unique_total_draws(t,:,:);
      tmp = reshape(tmp,n_y,n_draws_save);
      var_agg_eps_unique_total_draws(t,:) = cw_sq(t,:)*tmp;
      
      tmp = var_y_dtau_unique_draws(t,:,:);
      tmp = reshape(tmp,n_y,n_draws_save);
      var_agg_dtau_unique_draws(t,:) = cw_sq(t,:)*tmp;
      
      tmp = alpha_eps_draws(t,:,:);
      tmp = reshape(tmp,n_y,n_draws_save);
      alpha_agg = cw(t,:)*tmp;
      var_agg_eps_common_total_draws(t,:) = (alpha_agg.*sigma_eps_common_total_draws(t,:)).^2;
      
      tmp = alpha_tau_draws(t,:,:);
      tmp = reshape(tmp,n_y,n_draws_save);
      alpha_agg = cw(t,:)*tmp;
      var_agg_dtau_common_draws(t,:) = (alpha_agg.*sigma_dtau_common_draws(t,:)).^2;
  end;
  
  % Free up memory
  var_y_eps_unique_draws = 1;
  var_y_eps_unique_total_draws = 1;
  var_y_eps_common_draws = 1;
  var_y_eps_common_total_draws = 1;
  var_y_eps_unique_draws = 1;
  var_y_dtau_common_draws = 1;
  var_y_dtau_unique_draws = 1;
  
  agg_tau_draws = agg_tau_common_draws + agg_tau_unique_draws;
  agg_tau_xfe_draws = agg_tau_common_xfe_draws + agg_tau_unique_xfe_draws;
  agg_tau_xe_draws = agg_tau_common_xe_draws + agg_tau_unique_xe_draws;
  agg_f_tau_draws = agg_f_tau_common_draws + agg_f_tau_unique_draws;
  var_agg_eps_total_draws = var_agg_eps_common_total_draws + var_agg_eps_unique_total_draws;
  var_agg_dtau_draws = var_agg_dtau_common_draws + var_agg_dtau_unique_draws;
  
  var_agg_eps_common_total_mean_pct = post_mean_pct(var_agg_eps_common_total_draws',pctvec);
  var_agg_dtau_common_mean_pct = post_mean_pct(var_agg_dtau_common_draws',pctvec);
  var_agg_eps_unique_total_mean_pct = post_mean_pct(var_agg_eps_unique_total_draws',pctvec);
  var_agg_dtau_unique_mean_pct = post_mean_pct(var_agg_dtau_unique_draws',pctvec);
  var_agg_eps_total_mean_pct = post_mean_pct(var_agg_eps_total_draws',pctvec);
  var_agg_dtau_mean_pct = post_mean_pct(var_agg_dtau_draws',pctvec);
 
  agg_tau_common_mean_pct = post_mean_pct(agg_tau_common_draws',pctvec);
  agg_tau_unique_mean_pct = post_mean_pct(agg_tau_unique_draws',pctvec);
  agg_tau_mean_pct = post_mean_pct(agg_tau_draws',pctvec);
  agg_tau_xfe_mean_pct = post_mean_pct(agg_tau_xfe_draws',pctvec);
  agg_tau_xe_mean_pct = post_mean_pct(agg_tau_xe_draws',pctvec);
  agg_f_tau_common_mean_pct = post_mean_pct(agg_f_tau_common_draws',pctvec);
  agg_f_tau_unique_mean_pct = post_mean_pct(agg_f_tau_unique_draws',pctvec);
  agg_f_tau_mean_pct = post_mean_pct(agg_f_tau_draws',pctvec);
  
  str_tmp = [matdir 'var_agg_eps_common_total_mean_pct' mlabel]; save(str_tmp,'var_agg_eps_common_total_mean_pct');
  str_tmp = [matdir 'var_agg_dtau_common_mean_pct' mlabel]; save(str_tmp,'var_agg_dtau_common_mean_pct');
  str_tmp = [matdir 'var_agg_eps_unique_total_mean_pct' mlabel]; save(str_tmp,'var_agg_eps_unique_total_mean_pct');
  str_tmp = [matdir 'var_agg_dtau_unique_mean_pct' mlabel]; save(str_tmp,'var_agg_dtau_unique_mean_pct');
  str_tmp = [matdir 'var_agg_eps_total_mean_pct' mlabel]; save(str_tmp,'var_agg_eps_total_mean_pct');
  str_tmp = [matdir 'var_agg_dtau_mean_pct' mlabel]; save(str_tmp,'var_agg_dtau_mean_pct');
  
  str_tmp = [matdir 'agg_tau_common_mean_pct' mlabel]; save(str_tmp,'agg_tau_common_mean_pct');
  str_tmp = [matdir 'agg_tau_unique_mean_pct' mlabel]; save(str_tmp,'agg_tau_unique_mean_pct');
  str_tmp = [matdir 'agg_f_tau_common_mean_pct' mlabel]; save(str_tmp,'agg_f_tau_common_mean_pct');          
  str_tmp = [matdir 'agg_f_tau_unique_mean_pct' mlabel]; save(str_tmp,'agg_f_tau_unique_mean_pct');
  str_tmp = [matdir 'agg_tau_mean_pct' mlabel]; save(str_tmp,'agg_tau_mean_pct');
  str_tmp = [matdir 'agg_tau_xfe_mean_pct' mlabel]; save(str_tmp,'agg_tau_xfe_mean_pct');
  str_tmp = [matdir 'agg_tau_xe_mean_pct' mlabel]; save(str_tmp,'agg_tau_xe_mean_pct');
  str_tmp = [matdir 'agg_f_tau_mean_pct' mlabel]; save(str_tmp,'agg_f_tau_mean_pct'); 
  
  agg_tau_mean = mean(agg_tau_draws,2);
  tmp = mean(agg_tau_draws.^2,2);
  agg_tau_var = tmp-agg_tau_mean.^2;
  agg_tau_sd = sqrt(agg_tau_var);
  agg_tau_mean_sd = [agg_tau_mean agg_tau_sd];
  
  agg_f_tau_mean = mean(agg_f_tau_draws,2);
  tmp = mean(agg_f_tau_draws.^2,2);
  agg_f_tau_var = tmp-agg_f_tau_mean.^2;
  agg_f_tau_sd = sqrt(agg_f_tau_var);
  agg_f_tau_mean_sd = [agg_f_tau_mean agg_f_tau_sd];
  
  disagg_f_tau_mean = NaN*zeros(notim,n_y);
  disagg_tau_mean = NaN*zeros(notim,n_y);
  for i = 1:n_y;
      tmp1 = y_tau_common_draws(:,i,:);
      tmp1 = reshape(tmp1,notim,n_draws_save);
      tmp2 = y_tau_unique_draws(:,i,:);
      tmp2 = reshape(tmp2,notim,n_draws_save);
      tmp = tmp1+tmp2;
      disagg_tau_mean(:,i) = mean(tmp,2);
      tmp1 = y_f_tau_common_draws(:,i,:);
      tmp1 = reshape(tmp1,notim,n_draws_save);
      tmp2 = y_f_tau_unique_draws(:,i,:);
      tmp2 = reshape(tmp2,notim,n_draws_save);
      tmp = tmp1+tmp2;
      disagg_f_tau_mean(:,i) = mean(tmp,2);
  end;
  str_tmp = [matdir 'disagg_f_tau_mean' mlabel]; save(str_tmp,'disagg_f_tau_mean');
  str_tmp = [matdir 'disagg_tau_mean' mlabel]; save(str_tmp,'disagg_tau_mean');  
  str_tmp = [matdir 'agg_f_tau_mean_sd' mlabel]; save(str_tmp,'agg_f_tau_mean_sd'); 
  str_tmp = [matdir 'agg_tau_mean_sd' mlabel]; save(str_tmp,'agg_tau_mean_sd');
  
  
  % ----- Compute and Save average values of factor loadings and standard deviations of shocks
  alpha_eps_mean = NaN*zeros(notim,n_y);
  alpha_tau_mean = NaN*zeros(notim,n_y);
  sigma_eps_common_mean = NaN*zeros(notim,1);
  sigma_eps_common_total_mean = NaN*zeros(notim,1);
  sigma_dtau_common_mean = NaN*zeros(notim,1);
  sigma_eps_unique_mean = NaN*zeros(notim,n_y);
  sigma_eps_unique_total_mean = NaN*zeros(notim,n_y);
  sigma_dtau_unique_mean = NaN*zeros(notim,n_y);
  var_eps_common_mean = NaN*zeros(notim,1);
  var_eps_common_total_mean = NaN*zeros(notim,1);
  var_dtau_common_mean = NaN*zeros(notim,1);
  var_eps_unique_mean = NaN*zeros(notim,n_y);
  var_eps_unique_total_mean = NaN*zeros(notim,n_y);
  var_dtau_unique_mean = NaN*zeros(notim,n_y);
  
  for i = 1:n_y
     tmp = alpha_eps_draws(:,i,:);
     tmp = reshape(tmp,notim,n_draws_save);
     alpha_eps_mean(:,i) = mean(tmp,2);
     
     tmp = alpha_tau_draws(:,i,:);
     tmp = reshape(tmp,notim,n_draws_save);
     alpha_tau_mean(:,i) = mean(tmp,2);
     
     tmp = sigma_eps_unique_draws(:,i,:);
     tmp = reshape(tmp,notim,n_draws_save);
     sigma_eps_unique_mean(:,i) = mean(tmp,2);
     var_eps_unique_mean(:,i) = mean(tmp.^2,2);
     
     tmp = sigma_eps_unique_total_draws(:,i,:);
     tmp = reshape(tmp,notim,n_draws_save);
     sigma_eps_unique_total_mean(:,i) = mean(tmp,2);
     var_eps_unique_total_mean(:,i) = mean(tmp.^2,2);
     
     tmp = sigma_dtau_unique_draws(:,i,:);
     tmp = reshape(tmp,notim,n_draws_save);
     sigma_dtau_unique_mean(:,i) = mean(tmp,2);
     var_dtau_unique_mean(:,i) = mean(tmp.^2,2);
  end;
  sigma_eps_common_mean = mean(sigma_eps_common_draws,2);
  sigma_eps_common_total_mean = mean(sigma_eps_common_total_draws,2);
  sigma_dtau_common_mean = mean(sigma_dtau_common_draws,2);
  var_eps_common_mean = mean(sigma_eps_common_draws.^2,2);
  var_eps_common_total_mean = mean(sigma_eps_common_total_draws.^2,2);
  var_dtau_common_mean = mean(sigma_dtau_common_draws.^2,2);
  
  str_tmp = [matdir 'alpha_eps_mean' mlabel]; save(str_tmp,'alpha_eps_mean');
  str_tmp = [matdir 'alpha_tau_mean' mlabel]; save(str_tmp,'alpha_tau_mean');
  str_tmp = [matdir 'sigma_eps_common_mean' mlabel]; save(str_tmp,'sigma_eps_common_mean');
  str_tmp = [matdir 'sigma_eps_common_total_mean' mlabel]; save(str_tmp,'sigma_eps_common_total_mean');
  str_tmp = [matdir 'sigma_eps_unique_mean' mlabel]; save(str_tmp,'sigma_eps_unique_mean');
  str_tmp = [matdir 'sigma_eps_unique_total_mean' mlabel]; save(str_tmp,'sigma_eps_unique_total_mean'); 
  str_tmp = [matdir 'sigma_dtau_common_mean' mlabel]; save(str_tmp,'sigma_dtau_common_mean'); 
  str_tmp = [matdir 'sigma_dtau_unique_mean' mlabel]; save(str_tmp,'sigma_dtau_unique_mean'); 
 
  str_tmp = [matdir 'var_eps_common_mean' mlabel]; save(str_tmp,'var_eps_common_mean');
  str_tmp = [matdir 'var_eps_common_total_mean' mlabel]; save(str_tmp,'var_eps_common_total_mean');
  str_tmp = [matdir 'var_eps_unique_mean' mlabel]; save(str_tmp,'var_eps_unique_mean');
  str_tmp = [matdir 'var_eps_unique_total_mean' mlabel]; save(str_tmp,'var_eps_unique_total_mean'); 
  str_tmp = [matdir 'var_dtau_common_mean' mlabel]; save(str_tmp,'var_dtau_common_mean'); 
  str_tmp = [matdir 'var_dtau_unique_mean' mlabel]; save(str_tmp,'var_dtau_unique_mean'); 

  