161 lines
5.7 KiB
Matlab
161 lines
5.7 KiB
Matlab
function disp_th_moments(dr,var_list)
|
|
% Display theoretical moments of variables
|
|
|
|
% Copyright (C) 2001-2013 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 M_ oo_ options_
|
|
|
|
nodecomposition = options_.nodecomposition;
|
|
|
|
if size(var_list,1) == 0
|
|
var_list = M_.endo_names(1:M_.orig_endo_nbr, :);
|
|
end
|
|
nvar = size(var_list,1);
|
|
ivar=zeros(nvar,1);
|
|
for i=1:nvar
|
|
i_tmp = strmatch(var_list(i,:),M_.endo_names,'exact');
|
|
if isempty(i_tmp)
|
|
error (['One of the variable specified does not exist']) ;
|
|
else
|
|
ivar(i) = i_tmp;
|
|
end
|
|
end
|
|
|
|
[oo_.gamma_y,stationary_vars] = th_autocovariances(dr,ivar,M_,options_, nodecomposition);
|
|
m = dr.ys(ivar);
|
|
non_stationary_vars = setdiff(1:length(ivar),stationary_vars);
|
|
m(non_stationary_vars) = NaN;
|
|
|
|
i1 = find(abs(diag(oo_.gamma_y{1})) > 1e-12);
|
|
s2 = diag(oo_.gamma_y{1});
|
|
sd = sqrt(s2);
|
|
if options_.order == 2
|
|
m = m+oo_.gamma_y{options_.ar+3};
|
|
end
|
|
|
|
z = [ m sd s2 ];
|
|
oo_.mean = m;
|
|
oo_.var = oo_.gamma_y{1};
|
|
|
|
if size(stationary_vars, 1) > 0
|
|
if ~nodecomposition
|
|
oo_.variance_decomposition=100*oo_.gamma_y{options_.ar+2};
|
|
end
|
|
if ~options_.noprint %options_.nomoments == 0
|
|
if options_.order == 2
|
|
title='APPROXIMATED THEORETICAL MOMENTS';
|
|
else
|
|
title='THEORETICAL MOMENTS';
|
|
end
|
|
if options_.hp_filter
|
|
title = [title ' (HP filter, lambda = ' num2str(options_.hp_filter) ')'];
|
|
end
|
|
headers=char('VARIABLE','MEAN','STD. DEV.','VARIANCE');
|
|
labels = deblank(M_.endo_names(ivar,:));
|
|
lh = size(labels,2)+2;
|
|
dyntable(title,headers,labels,z,lh,11,4);
|
|
|
|
if M_.exo_nbr > 1 && ~nodecomposition
|
|
skipline()
|
|
if options_.order == 2
|
|
title='APPROXIMATED VARIANCE DECOMPOSITION (in percent)';
|
|
else
|
|
title='VARIANCE DECOMPOSITION (in percent)';
|
|
end
|
|
if options_.hp_filter
|
|
title = [title ' (HP filter, lambda = ' ...
|
|
num2str(options_.hp_filter) ')'];
|
|
end
|
|
headers = M_.exo_names;
|
|
headers(M_.exo_names_orig_ord,:) = headers;
|
|
headers = char(' ',headers);
|
|
lh = size(deblank(M_.endo_names(ivar(stationary_vars),:)),2)+2;
|
|
dyntable(title,headers,deblank(M_.endo_names(ivar(stationary_vars), ...
|
|
:)),100* ...
|
|
oo_.gamma_y{options_.ar+2}(stationary_vars,:),lh,8,2);
|
|
end
|
|
end
|
|
|
|
conditional_variance_steps = options_.conditional_variance_decomposition;
|
|
if length(conditional_variance_steps)
|
|
StateSpaceModel.number_of_state_equations = M_.endo_nbr;
|
|
StateSpaceModel.number_of_state_innovations = M_.exo_nbr;
|
|
StateSpaceModel.sigma_e_is_diagonal = M_.sigma_e_is_diagonal;
|
|
[StateSpaceModel.transition_matrix,StateSpaceModel.impulse_matrix] = kalman_transition_matrix(dr,(1:M_.endo_nbr)',M_.nstatic+(1:M_.nspred)',M_.exo_nbr);
|
|
StateSpaceModel.state_innovations_covariance_matrix = M_.Sigma_e;
|
|
StateSpaceModel.order_var = dr.order_var;
|
|
oo_.conditional_variance_decomposition = conditional_variance_decomposition(StateSpaceModel,conditional_variance_steps,ivar);
|
|
|
|
if options_.noprint == 0
|
|
display_conditional_variance_decomposition(oo_.conditional_variance_decomposition,conditional_variance_steps,...
|
|
ivar,M_,options_);
|
|
end
|
|
end
|
|
end
|
|
|
|
if length(i1) == 0
|
|
skipline()
|
|
disp('All endogenous are constant or non stationary, not displaying correlations and auto-correlations')
|
|
skipline()
|
|
return
|
|
end
|
|
|
|
if options_.nocorr == 0 && size(stationary_vars, 1) > 0
|
|
corr = oo_.gamma_y{1}(i1,i1)./(sd(i1)*sd(i1)');
|
|
if options_.contemporaneous_correlation
|
|
oo_.contemporaneous_correlation = corr;
|
|
end
|
|
if ~options_.noprint,
|
|
skipline()
|
|
if options_.order == 2
|
|
title='APPROXIMATED MATRIX OF CORRELATIONS';
|
|
else
|
|
title='MATRIX OF CORRELATIONS';
|
|
end
|
|
if options_.hp_filter
|
|
title = [title ' (HP filter, lambda = ' num2str(options_.hp_filter) ')'];
|
|
end
|
|
labels = deblank(M_.endo_names(ivar(i1),:));
|
|
headers = char('Variables',labels);
|
|
lh = size(labels,2)+2;
|
|
dyntable(title,headers,labels,corr,lh,8,4);
|
|
end
|
|
end
|
|
if options_.ar > 0 && size(stationary_vars, 1) > 0
|
|
z=[];
|
|
for i=1:options_.ar
|
|
oo_.autocorr{i} = oo_.gamma_y{i+1};
|
|
z(:,i) = diag(oo_.gamma_y{i+1}(i1,i1));
|
|
end
|
|
if ~options_.noprint,
|
|
skipline()
|
|
if options_.order == 2
|
|
title='APPROXIMATED COEFFICIENTS OF AUTOCORRELATION';
|
|
else
|
|
title='COEFFICIENTS OF AUTOCORRELATION';
|
|
end
|
|
if options_.hp_filter
|
|
title = [title ' (HP filter, lambda = ' num2str(options_.hp_filter) ')'];
|
|
end
|
|
labels = deblank(M_.endo_names(ivar(i1),:));
|
|
headers = char('Order ',int2str([1:options_.ar]'));
|
|
lh = size(labels,2)+2;
|
|
dyntable(title,headers,labels,z,lh,8,4);
|
|
end
|
|
end
|