309 lines
12 KiB
Matlab
309 lines
12 KiB
Matlab
function oo_ = realtime_shock_decomposition(M_,oo_,options_,varlist,bayestopt_,estim_params_)
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% function oo_ = realtime_shock_decomposition(M_,oo_,options_,varlist,bayestopt_,estim_params_)
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% Computes shocks contribution to a simulated trajectory. The fields set are
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% oo_.realtime_shock_decomposition, oo_.conditional_shock_decomposition and oo_.realtime_forecast_shock_decomposition.
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% Subfields are arrays n_var by nshock+2 by nperiods. 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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%
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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(['realtime_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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presample = max(1,options_.presample);
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if isfield(options_.shock_decomp,'presample')
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presample = max(presample,options_.shock_decomp.presample);
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end
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% forecast_=0;
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forecast_ = options_.shock_decomp.forecast;
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forecast_params=0;
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if forecast_ && isfield(options_.shock_decomp,'forecast_params')
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forecast_params = options_.shock_decomp.forecast_params;
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end
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fast_realtime = 0;
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if isfield(options_.shock_decomp,'fast_realtime')
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fast_realtime = options_.shock_decomp.fast_realtime;
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end
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% save_realtime=0;
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save_realtime = options_.shock_decomp.save_realtime;
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% array of time points in the range options_.presample+1:options_.nobs
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zreal = zeros(endo_nbr,nshocks+2,options_.nobs+forecast_);
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zcond = zeros(endo_nbr,nshocks+2,options_.nobs);
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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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init=1;
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nobs = options_.nobs;
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if forecast_ && any(forecast_params)
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M1=M_;
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M1.params = forecast_params;
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[~,~,~,~,~,~,oo1] = dynare_resolve(M1,options_,oo_);
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end
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if fast_realtime
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skipline()
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skipline()
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running_text = 'Fast realtime shock decomposition ';
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newString=sprintf(running_text);
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fprintf(['%s'],newString);
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options_.nobs=fast_realtime;
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[oo0,M_,~,~,Smoothed_Variables_deviation_from_mean0] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
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gend0 = size(oo0.SmoothedShocks.(M_.exo_names{1}),1);
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prctdone=0.5;
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if isoctave
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printf([running_text,' %3.f%% done\r'], prctdone*100);
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else
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s0=repmat('\b',1,length(newString)+1);
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newString=sprintf([running_text,' %3.1f%% done'], prctdone*100);
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fprintf([s0,'%s'],newString);
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end
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options_.nobs=nobs;
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[oo2,M_,~,~,Smoothed_Variables_deviation_from_mean2] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
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gend2 = size(oo2.SmoothedShocks.(M_.exo_names{1}),1);
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prctdone=1;
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if isoctave
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printf([running_text,' %3.f%% done\r'], prctdone*100);
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else
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s0=repmat('\b',1,length(newString)+1);
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newString=sprintf([running_text,' %3.1f%% done'], prctdone*100);
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fprintf([s0,'%s'],newString);
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end
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end
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skipline()
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skipline()
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running_text = 'Realtime shock decomposition ';
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newString=sprintf(running_text);
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fprintf(['%s'],newString);
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for j=presample+1:nobs
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% evalin('base',['options_.nobs=' int2str(j) ';'])
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options_.nobs=j;
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if ~fast_realtime
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[oo,M_,~,~,Smoothed_Variables_deviation_from_mean] = evaluate_smoother(parameter_set,varlist,M_,oo_,options_,bayestopt_,estim_params_);
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gend = size(oo.SmoothedShocks.(M_.exo_names{1}),1);
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else
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gend = gend0+j-fast_realtime;
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if j>fast_realtime
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oo=oo2;
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Smoothed_Variables_deviation_from_mean = Smoothed_Variables_deviation_from_mean2(:,1:gend);
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else
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oo=oo0;
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Smoothed_Variables_deviation_from_mean = Smoothed_Variables_deviation_from_mean0(:,1:gend);
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end
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end
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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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if forecast_
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if any(forecast_params)
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Af = oo1.dr.ghx;
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Bf = oo1.dr.ghu;
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else
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Af = A;
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Bf = B;
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end
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end
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% initialization
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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})(1:gend);
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end
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epsilon=[epsilon zeros(nshocks,forecast_)];
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z = zeros(endo_nbr,nshocks+2,gend+forecast_);
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z(:,end,1:gend) = 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+forecast_
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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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if i > gend
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z(:,nshocks+2,i) = Af(inv_order_var,:)*z(i_state,nshocks+2,lags);
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% z(:,nshocks+2,i) = A(inv_order_var,:)*permute(z(i_state,nshocks+2,lags),[1 3 2]);
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z(:,1:nshocks,i) = Af(inv_order_var,:)*tempx(i_state,:);
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else
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z(:,1:nshocks,i) = A(inv_order_var,:)*tempx(i_state,:);
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end
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lags = lags+1;
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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(:,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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%% conditional shock decomp 1 step ahead
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z1 = zeros(endo_nbr,nshocks+2);
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z1(:,end) = Smoothed_Variables_deviation_from_mean(:,gend);
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for i=gend
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z1(:,1:nshocks) = z1(:,1:nshocks) + B(inv_order_var,:).*repmat(epsilon(:,i)',endo_nbr,1);
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z1(:,nshocks+1) = z1(:,nshocks+2) - sum(z1(:,1:nshocks),2);
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end
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%%
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%% conditional shock decomp k step ahead
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if forecast_ && forecast_<j
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zn = zeros(endo_nbr,nshocks+2,forecast_+1);
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zn(:,end,1:forecast_+1) = Smoothed_Variables_deviation_from_mean(:,gend-forecast_:gend);
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for i=1:forecast_+1
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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(zn(:,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+1-1),nshocks)];
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zn(:,1:nshocks,i) = A(inv_order_var,:)*tempx(i_state,:);
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lags = lags+1;
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zn(:,1:nshocks,i) = zn(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i+gend-forecast_-1)',endo_nbr,1);
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end
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% zn(:,1:nshocks,i) = zn(:,1:nshocks,i) + B(inv_order_var,:).*repmat(epsilon(:,i+gend-forecast_-1)',endo_nbr,1);
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zn(:,nshocks+1,i) = zn(:,nshocks+2,i) - sum(zn(:,1:nshocks,i),2);
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end
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oo_.conditional_shock_decomposition.(['time_' int2str(j-forecast_)])=zn;
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end
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%%
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if init
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zreal(:,:,1:j) = z(:,:,1:j);
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else
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zreal(:,:,j) = z(:,:,gend);
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end
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zcond(:,:,j) = z1;
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if ismember(j,save_realtime)
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oo_.realtime_shock_decomposition.(['time_' int2str(j)])=z;
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end
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if forecast_
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zfrcst(:,:,j+1) = z(:,:,gend+1);
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oo_.realtime_forecast_shock_decomposition.(['time_' int2str(j)])=z(:,:,gend:end);
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if j>forecast_+presample
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%% realtime conditional shock decomp k step ahead
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oo_.realtime_conditional_shock_decomposition.(['time_' int2str(j-forecast_)]) = ...
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zreal(:,:,j-forecast_:j) - ...
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oo_.realtime_forecast_shock_decomposition.(['time_' int2str(j-forecast_)]);
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oo_.realtime_conditional_shock_decomposition.(['time_' int2str(j-forecast_)])(:,end-1,:) = ...
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oo_.realtime_conditional_shock_decomposition.(['time_' int2str(j-forecast_)])(:,end-1,:) + ...
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oo_.realtime_forecast_shock_decomposition.(['time_' int2str(j-forecast_)])(:,end,:);
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oo_.realtime_conditional_shock_decomposition.(['time_' int2str(j-forecast_)])(:,end,:) = ...
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zreal(:,end,j-forecast_:j);
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if j==nobs
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for my_forecast_=(forecast_-1):-1:1
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oo_.realtime_conditional_shock_decomposition.(['time_' int2str(j-my_forecast_)]) = ...
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zreal(:,:,j-my_forecast_:j) - ...
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oo_.realtime_forecast_shock_decomposition.(['time_' int2str(j-my_forecast_)])(:,:,1:my_forecast_+1);
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oo_.realtime_conditional_shock_decomposition.(['time_' int2str(j-my_forecast_)])(:,end-1,:) = ...
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oo_.realtime_conditional_shock_decomposition.(['time_' int2str(j-my_forecast_)])(:,end-1,:) + ...
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oo_.realtime_forecast_shock_decomposition.(['time_' int2str(j-my_forecast_)])(:,end,1:my_forecast_+1);
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oo_.realtime_conditional_shock_decomposition.(['time_' int2str(j-my_forecast_)])(:,end,:) = ...
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zreal(:,end,j-my_forecast_:j);
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end
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end
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end
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end
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prctdone=(j-presample)/(nobs-presample);
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if isoctave
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printf([running_text,' %3.f%% done\r'], prctdone*100);
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else
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s0=repmat('\b',1,length(newString));
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newString=sprintf([running_text,' %3.1f%% done'], prctdone*100);
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fprintf([s0,'%s'],newString);
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end
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init=0;
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end
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oo_.realtime_shock_decomposition.pool = zreal;
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oo_.conditional_shock_decomposition.pool = zcond;
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if forecast_
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oo_.realtime_forecast_shock_decomposition.pool = zfrcst;
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end
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skipline()
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