206 lines
8.6 KiB
Matlab
206 lines
8.6 KiB
Matlab
function oo_ = disp_th_moments(dr, var_list, M_, options_, oo_)
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% Display theoretical moments of variables
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% Copyright (C) 2001-2018 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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nodecomposition = options_.nodecomposition;
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if options_.one_sided_hp_filter
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error(['disp_th_moments:: theoretical moments incompatible with one-sided HP filter. Use simulated moments instead'])
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end
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if isempty(var_list)
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var_list = M_.endo_names(1:M_.orig_endo_nbr);
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end
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nvar = length(var_list);
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ivar=zeros(nvar,1);
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for i=1:nvar
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i_tmp = strmatch(var_list{i}, M_.endo_names, 'exact');
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if isempty(i_tmp)
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error ('One of the variable specified does not exist');
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else
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ivar(i) = i_tmp;
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end
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end
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[oo_.gamma_y,stationary_vars] = th_autocovariances(dr, ivar, M_, options_, nodecomposition);
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m = dr.ys(ivar);
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non_stationary_vars = setdiff(1:length(ivar),stationary_vars);
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m(non_stationary_vars) = NaN;
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i1 = find(abs(diag(oo_.gamma_y{1})) > 1e-12);
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s2 = diag(oo_.gamma_y{1});
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sd = sqrt(s2);
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if options_.order == 2 && ~M_.hessian_eq_zero
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m = m+oo_.gamma_y{options_.ar+3};
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end
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z = [ m sd s2 ];
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oo_.mean = m;
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oo_.var = oo_.gamma_y{1};
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ME_present=0;
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if ~all(M_.H==0)
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[observable_pos_requested_vars,index_subset,index_observables]=intersect(ivar,options_.varobs_id,'stable');
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if ~isempty(observable_pos_requested_vars)
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ME_present=1;
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end
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end
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if size(stationary_vars, 1) > 0
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if ~nodecomposition
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oo_.variance_decomposition=100*oo_.gamma_y{options_.ar+2};
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if ME_present
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ME_Variance=diag(M_.H);
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oo_.variance_decomposition_ME=oo_.variance_decomposition(index_subset,:).*repmat(diag(oo_.var(index_subset,index_subset))./(diag(oo_.var(index_subset,index_subset))+ME_Variance(index_observables)),1,M_.exo_nbr);
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oo_.variance_decomposition_ME(:,end+1)=100-sum(oo_.variance_decomposition_ME,2);
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end
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end
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if ~options_.noprint %options_.nomoments == 0
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if options_.order == 2
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title = 'APPROXIMATED THEORETICAL MOMENTS';
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else
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title = 'THEORETICAL MOMENTS';
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end
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title = add_filter_subtitle(title, options_);
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headers = {'VARIABLE';'MEAN';'STD. DEV.';'VARIANCE'};
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labels = M_.endo_names(ivar);
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lh = cellofchararraymaxlength(labels)+2;
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dyntable(options_, title, headers, labels, z, lh, 11, 4);
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if options_.TeX
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labels = M_.endo_names_tex(ivar);
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lh = cellofchararraymaxlength(labels)+2;
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dyn_latex_table(M_, options_, title, 'th_moments', headers, labels, z, lh, 11, 4);
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end
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if M_.exo_nbr > 1 && ~nodecomposition
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skipline()
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if options_.order == 2
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title = 'APPROXIMATED VARIANCE DECOMPOSITION (in percent)';
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else
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title = 'VARIANCE DECOMPOSITION (in percent)';
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end
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title = add_filter_subtitle(title, options_);
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headers = M_.exo_names;
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headers(M_.exo_names_orig_ord) = headers;
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headers = vertcat(' ', headers);
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lh = cellofchararraymaxlength(M_.endo_names(ivar(stationary_vars)))+2;
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dyntable(options_, title, headers, M_.endo_names(ivar(stationary_vars)), 100*oo_.gamma_y{options_.ar+2}(stationary_vars,:), lh, 8, 2);
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if ME_present
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[stationary_observables, pos_index_subset] = intersect(index_subset, stationary_vars, 'stable');
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headers_ME = vertcat(headers, 'ME');
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dyntable(options_, [title,' WITH MEASUREMENT ERROR'], headers_ME, M_.endo_names(ivar(stationary_observables)), ...
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oo_.variance_decomposition_ME(pos_index_subset,:), lh, 8, 2);
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end
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if options_.TeX
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headers = M_.exo_names_tex;
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headers = vertcat(' ', headers);
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labels = M_.endo_names_tex(ivar(stationary_vars));
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lh = cellofchararraymaxlength(labels)+2;
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dyn_latex_table(M_, options_, title, 'th_var_decomp_uncond', headers, labels, 100*oo_.gamma_y{options_.ar+2}(stationary_vars,:), lh, 8, 2);
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if ME_present
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headers_ME = vertcat(headers, 'ME');
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dyn_latex_table(M_, options_, [title,' WITH MEASUREMENT ERROR'], ...
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'th_var_decomp_uncond_ME', headers_ME, labels, oo_.variance_decomposition_ME(pos_index_subset,:), lh, 8, 2);
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end
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end
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end
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end
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conditional_variance_steps = options_.conditional_variance_decomposition;
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if length(conditional_variance_steps)
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StateSpaceModel.number_of_state_equations = M_.endo_nbr;
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StateSpaceModel.number_of_state_innovations = M_.exo_nbr;
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StateSpaceModel.sigma_e_is_diagonal = M_.sigma_e_is_diagonal;
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[StateSpaceModel.transition_matrix, StateSpaceModel.impulse_matrix] = ...
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kalman_transition_matrix(dr,(1:M_.endo_nbr)',M_.nstatic+(1:M_.nspred)',M_.exo_nbr);
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StateSpaceModel.state_innovations_covariance_matrix = M_.Sigma_e;
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StateSpaceModel.order_var = dr.order_var;
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StateSpaceModel.measurement_error = M_.H;
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StateSpaceModel.observable_pos = options_.varobs_id;
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[oo_.conditional_variance_decomposition, oo_.conditional_variance_decomposition_ME] = ...
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conditional_variance_decomposition(StateSpaceModel, conditional_variance_steps, ivar);
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if options_.noprint == 0
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display_conditional_variance_decomposition(oo_.conditional_variance_decomposition, conditional_variance_steps, ivar, M_, options_);
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if ME_present
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display_conditional_variance_decomposition(oo_.conditional_variance_decomposition_ME, conditional_variance_steps, ...
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observable_pos_requested_vars, M_, options_);
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end
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end
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end
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end
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if length(i1) == 0
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skipline()
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disp('All endogenous are constant or non stationary, not displaying correlations and auto-correlations')
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skipline()
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return
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end
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if options_.nocorr == 0 && size(stationary_vars, 1)>0
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corr = NaN(size(oo_.gamma_y{1}));
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corr(i1,i1) = oo_.gamma_y{1}(i1,i1)./(sd(i1)*sd(i1)');
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if options_.contemporaneous_correlation
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oo_.contemporaneous_correlation = corr;
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end
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if ~options_.noprint
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skipline()
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if options_.order==2
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title = 'APPROXIMATED MATRIX OF CORRELATIONS';
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else
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title = 'MATRIX OF CORRELATIONS';
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end
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title = add_filter_subtitle(title, options_);
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labels = M_.endo_names(ivar(i1));
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headers = vertcat('Variables', labels);
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lh = cellofchararraymaxlength(labels)+2;
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dyntable(options_, title, headers, labels, corr(i1,i1), lh, 8, 4);
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if options_.TeX
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labels = M_.endo_names_tex(ivar(i1));
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headers = vertcat('Variables', labels);
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lh = cellofchararraymaxlength(labels)+2;
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dyn_latex_table(M_, options_, title, 'th_corr_matrix', headers, labels, corr(i1,i1), lh, 8, 4);
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end
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end
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end
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if options_.ar > 0 && size(stationary_vars, 1) > 0
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z=[];
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for i=1:options_.ar
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oo_.autocorr{i} = oo_.gamma_y{i+1};
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z(:,i) = diag(oo_.gamma_y{i+1}(i1,i1));
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end
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if ~options_.noprint
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skipline()
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if options_.order == 2
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title = 'APPROXIMATED COEFFICIENTS OF AUTOCORRELATION';
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else
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title = 'COEFFICIENTS OF AUTOCORRELATION';
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end
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title = add_filter_subtitle(title, options_);
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labels = M_.endo_names(ivar(i1));
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headers = vertcat('Order ', cellstr(int2str([1:options_.ar]')));
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lh = cellofchararraymaxlength(labels)+2;
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dyntable(options_, title, headers, labels, z, lh, 8, 4);
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if options_.TeX
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labels = M_.endo_names_tex(ivar(i1));
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headers = vertcat('Order ', cellstr(int2str([1:options_.ar]')));
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lh = cellofchararraymaxlength(labels)+2;
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dyn_latex_table(M_, options_, title, 'th_autocorr_matrix', headers, labels, z, lh, 8, 4);
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end
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end
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end
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