105 lines
3.2 KiB
Matlab
105 lines
3.2 KiB
Matlab
function [yf,int_width,int_width_ME]=forcst(dr,y0,horizon,var_list,M_,oo_,options_)
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% function [yf,int_width,int_width_ME]=forecst(dr,y0,horizon,var_list,M_,oo_,options_)
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% computes mean forecast for a given value of the parameters
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% computes also confidence band for the forecast
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%
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% INPUTS:
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% dr: structure containing decision rules
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% y0: initial values
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% horizon: nbr of periods to forecast
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% var_list: list of variables (character matrix)
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% M_: Dynare model structure
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% options_: Dynare options structure
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% oo_: Dynare results structure
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% OUTPUTS:
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% yf: mean forecast
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% int_width: distance between upper bound and
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% mean forecast
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% int_width_ME:distance between upper bound and
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% mean forecast when considering measurement error
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright © 2003-2019 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 <https://www.gnu.org/licenses/>.
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yf = simult_(M_,options_,y0,dr,zeros(horizon,M_.exo_nbr),1);
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nstatic = M_.nstatic;
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nspred = M_.nspred;
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nc = size(dr.ghx,2);
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inv_order_var = dr.inv_order_var;
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[A,B] = kalman_transition_matrix(dr,nstatic+(1:nspred),1:nc);
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if isempty(var_list)
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var_list = M_.endo_names(1:M_.orig_endo_nbr);
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end
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nvar = length(var_list);
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ivar = zeros(nvar, 1);
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for i=1:nvar
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i_tmp = strmatch(var_list{i}, M_.endo_names, 'exact');
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if isempty(i_tmp)
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disp(var_list{i});
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error ('One of the variable specified does not exist') ;
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else
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ivar(i) = i_tmp;
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end
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end
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ghx1 = dr.ghx(inv_order_var(ivar),:);
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ghu1 = dr.ghu(inv_order_var(ivar),:);
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%initialize recursion
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sigma_u = B*M_.Sigma_e*B';
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sigma_u1 = ghu1*M_.Sigma_e*ghu1';
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sigma_y = 0; %no uncertainty about the states
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var_yf = NaN(horizon,nvar); %initialize
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for i = 1:horizon
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%map uncertainty about states into uncertainty about observables
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sigma_y1 = ghx1*sigma_y*ghx1'+sigma_u1;
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var_yf(i,:) = diag(sigma_y1)';
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if i == horizon
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break
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end
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%update uncertainty about states
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sigma_u = A*sigma_u*A';
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sigma_y = sigma_y+sigma_u;
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end
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if nargout==3
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var_yf_ME=var_yf;
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[loc_H, loc_varlist] = ismember(options_.varobs, var_list);
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loc_varlist(loc_varlist==0) = [];
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if ~isempty(loc_varlist)
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var_yf_ME(:,loc_varlist) = var_yf(:,loc_varlist)+repmat(diag(M_.H(loc_H,loc_H))', horizon, 1);
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end
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int_width_ME = zeros(horizon, nvar);
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end
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fact = norminv((1-options_.forecasts.conf_sig)/2, 0, 1);
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int_width = zeros(horizon, nvar);
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for i = 1:nvar
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int_width(:,i) = -fact*sqrt(var_yf(:,i));
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if nargout==3
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int_width_ME(:,i) = -fact*sqrt(var_yf_ME(:,i));
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end
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end
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yf = yf(ivar,:);
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