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function oo_ = shock_decomposition(M_,oo_,options_,varlist)
% function z = shock_decomposition(M_,oo_,options_,varlist)
% Computes shocks contribution to a simulated trajectory
%
% INPUTS
% M_: [structure] Definition of the model
% oo_: [structure] Storage of results
% options_: [structure] Options
% varlist: [char] List of variables
%
% OUTPUTS
% oo_: [structure] Storage of results
%
% SPECIAL REQUIREMENTS
% none
% Copyright (C) 2009-2011 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/>.
% indices of endogenous variables
if size(varlist,1) == 0
varlist = M_.endo_names(1:M_.orig_endo_nbr,:);
end
[i_var,nvar] = varlist_indices(varlist,M_.endo_names);
% number of variables
endo_nbr = M_.endo_nbr;
% number of shocks
nshocks = M_.exo_nbr;
% parameter set
parameter_set = options_.parameter_set;
if isempty(parameter_set)
if isfield(oo_,'posterior_mean')
parameter_set = 'posterior_mean';
elseif isfield(oo_,'posterior_mode')
parameter_set = 'posterior_mode';
else
error(['shock_decomposition: option parameter_set is not specified ' ...
'and posterior mode is not available'])
end
end
oo = evaluate_smoother(parameter_set);
% reduced form
dr = oo.dr;
% data reordering
order_var = dr.order_var;
inv_order_var = dr.inv_order_var;
% coefficients
A = dr.ghx;
B = dr.ghu;
% initialization
for i=1:nshocks
epsilon(i,:) = eval(['oo.SmoothedShocks.' M_.exo_names(i,:)]);
end
gend = size(epsilon,2);
z = zeros(endo_nbr,nshocks+2,gend);
for i=1:endo_nbr
z(i,end,:) = eval(['oo.SmoothedVariables.' M_.endo_names(i,:)]);
end
maximum_lag = M_.maximum_lag;
lead_lag_incidence = M_.lead_lag_incidence;
k2 = dr.kstate(find(dr.kstate(:,2) <= maximum_lag+1),[1 2]);
i_state = order_var(k2(:,1))+(min(i,maximum_lag)+1-k2(:,2))*M_.endo_nbr;
for i=1:gend
if i > 1 && i <= maximum_lag+1
lags = min(i-1,maximum_lag):-1:1;
end
if i > 1
tempx = permute(z(:,1:nshocks,lags),[1 3 2]);
m = min(i-1,maximum_lag);
tempx = [reshape(tempx,endo_nbr*m,nshocks); zeros(endo_nbr*(maximum_lag-i+1),nshocks)];
z(:,1:nshocks,i) = A(inv_order_var,:)*tempx(i_state,:);
lags = lags+1;
end
z(:,1:nshocks,i) = z(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i)',endo_nbr,1);
z(:,nshocks+1,i) = z(:,nshocks+2,i) - sum(z(:,1:nshocks,i),2);
end
oo_.shock_decomposition = z;
graph_decomp(z,M_.exo_names,M_.endo_names,i_var,options_.initial_date)
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