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