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Commit bab401eb authored by Marco Ratto's avatar Marco Ratto
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utility to decompose initial condition effect

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function oo_ = initial_condition_decomposition(M_,oo_,options_,varlist,bayestopt_,estim_params_)
% function z = initial_condition_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
% bayestopt_: [structure] describing the priors
% estim_params_: [structure] characterizing parameters to be estimated
%
% 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 <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;
% parameter set
parameter_set = options_.parameter_set;
if isempty(parameter_set)
if isfield(oo_,'posterior_mean')
parameter_set = 'posterior_mean';
elseif isfield(oo_,'mle_mode')
parameter_set = 'mle_mode';
elseif isfield(oo_,'posterior')
parameter_set = 'posterior_mode';
else
error(['shock_decomposition: option parameter_set is not specified ' ...
'and posterior mode is not available'])
end
end
if ~isfield(oo_,'initval_decomposition')
options_.selected_variables_only = 0; %make sure all variables are stored
options_.plot_priors=0;
[oo,junk1,junk2,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
% 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); %+options_.forecast;
z = zeros(endo_nbr,endo_nbr+2,gend);
z(:,end,:) = Smoothed_Variables_deviation_from_mean;
for i=1:endo_nbr,
z(i,i,1) = Smoothed_Variables_deviation_from_mean(i,1);
end
maximum_lag = M_.maximum_lag;
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:endo_nbr,lags),[1 3 2]);
m = min(i-1,maximum_lag);
tempx = [reshape(tempx,endo_nbr*m,endo_nbr); zeros(endo_nbr*(maximum_lag-i+1),endo_nbr)];
z(:,1:endo_nbr,i) = A(inv_order_var,:)*tempx(i_state,:);
lags = lags+1;
end
z(:,endo_nbr+1,i) = z(:,endo_nbr+2,i) - sum(z(:,1:endo_nbr,i),2);
end
oo_.initval_decomposition = z;
end
% if ~options_.no_graph.shock_decomposition
oo=oo_;
oo.shock_decomposition = oo_.initval_decomposition;
M_.exo_names = M_.endo_names;
M_.exo_nbr = M_.endo_nbr;
options_.shock_decomp.screen_shocks=1;
options_.use_shock_groups = '';
options_.shock_decomp.fig_names='initval';
plot_shock_decomposition(M_,oo,options_,varlist);
% end
\ No newline at end of file
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