function oo_ = shock_decomposition(M_,oo_,options_,varlist) % function z = shock_decomposition(M_,oo_,options_,varlist) % Computes shocks contribution to a simulated trajectory. The field set is % oo_.shock_decomposition. It is a n_var by nshock+2 by nperiods array. The % first nshock columns store the respective shock contributions, column n+1 % stores the role of the initial conditions, while column n+2 stores the % value of the smoothed variables. Both the variables and shocks are stored % in the order of declaration, i.e. M_.endo_names and M_.exo_names, respectively. % % 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) 2009-2016 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 . % 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,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist); % 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 gend = size(oo.SmoothedShocks.(deblank(M_.exo_names(1,:))),1); epsilon=NaN(nshocks,gend); for i=1:nshocks epsilon(i,:) = oo.SmoothedShocks.(deblank(M_.exo_names(i,:))); end z = zeros(endo_nbr,nshocks+2,gend); z(:,end,:) = Smoothed_Variables_deviation_from_mean; 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; if options_.use_shock_groups shock_groups = M_.shock_groups.(options_.use_shock_groups); shock_names = fieldnames(shock_groups); ngroups = length(shock_names); zz = zeros(endo_nbr,ngroups+2,gend); for i=1:length(shock_names) for j = shock_groups.(shock_names{i}) k = find(strcmp(j,cellstr(M_.exo_names))); zz(:,i,:) = zz(:,i,:) + z(:,k,:); end end zz(:,ngroups+(1:2),:) = z(:,nshocks+(1:2),:); z = zz; else shock_names = M_.exo_names; end graph_decomp(z,shock_names,M_.endo_names,i_var,options_.initial_date,M_,options_)