127 lines
4.1 KiB
Matlab
127 lines
4.1 KiB
Matlab
function [oo_,M_] = shock_decomposition(M_,oo_,options_,varlist,bayestopt_,estim_params_)
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% function z = shock_decomposition(M_,oo_,options_,varlist)
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% Computes shocks contribution to a simulated trajectory. The field set is
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% oo_.shock_decomposition. It is a n_var by nshock+2 by nperiods array. The
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% first nshock columns store the respective shock contributions, column n+1
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% stores the role of the initial conditions, while column n+2 stores the
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% value of the smoothed variables. Both the variables and shocks are stored
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% in the order of declaration, i.e. M_.endo_names and M_.exo_names, respectively.
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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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% bayestopt_: [structure] describing the priors
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% estim_params_: [structure] characterizing parameters to be estimated
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%
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% OUTPUTS
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% oo_: [structure] Storage of results
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% M_: [structure] Definition of the model; makes sure that
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% M_.params is correctly updated
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2009-2018 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 isempty(varlist)
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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,index_uniques] = varlist_indices(varlist, M_.endo_names);
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varlist = varlist(index_uniques);
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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_,'mle_mode')
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parameter_set = 'mle_mode';
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elseif isfield(oo_,'posterior')
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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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options_.selected_variables_only = 0; %make sure all variables are stored
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options_.plot_priors=0;
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[oo_, M_, junk1, junk2, Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set, varlist, M_, oo_, options_, bayestopt_, estim_params_);
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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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gend = size(oo_.SmoothedShocks.(M_.exo_names{1}),1);
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epsilon=NaN(nshocks,gend);
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for i=1:nshocks
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epsilon(i,:) = oo_.SmoothedShocks.(M_.exo_names{i});
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end
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z = zeros(endo_nbr,nshocks+2,gend);
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z(:,end,:) = Smoothed_Variables_deviation_from_mean;
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maximum_lag = M_.maximum_lag;
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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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if i > options_.shock_decomp.init_state
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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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end
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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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if ~options_.no_graph.shock_decomposition
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plot_shock_decomposition(M_,oo_,options_,varlist);
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end |