188 lines
6.8 KiB
Matlab
188 lines
6.8 KiB
Matlab
function [forecast,info] = dyn_forecast(var_list,M,options,oo,task,dataset_info)
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% function dyn_forecast(var_list,M,options,oo,task,dataset_info)
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% computes mean forecast for a given value of the parameters
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% compues also confidence band for the forecast
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%
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% INPUTS
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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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% task: indicates how to initialize the forecast
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% either 'simul' or 'smoother'
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% dataset_info: Various informations about the dataset (descriptive statistics and missing observations).
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% OUTPUTS
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% nothing is returned but the procedure saves output
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% in oo_.forecast.Mean
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% oo_.forecast.HPDinf
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% oo_.forecast.HPDsup
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2003-2016 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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if nargin<6 && options.prefilter
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error('The prefiltering option is not allowed without providing a dataset')
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elseif nargin==6
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mean_varobs=dataset_info.descriptive.mean';
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end
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info = 0;
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oo=make_ex_(M,options,oo);
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maximum_lag = M.maximum_lag;
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endo_names = M.endo_names;
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if isempty(var_list)
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var_list = endo_names(1:M.orig_endo_nbr, :);
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end
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i_var = [];
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for i = 1:size(var_list)
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tmp = strmatch(var_list(i,:),endo_names,'exact');
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if isempty(tmp)
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error([var_list(i,:) ' isn''t and endogenous variable'])
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end
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i_var = [i_var; tmp];
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end
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n_var = length(i_var);
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trend = 0;
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switch task
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case 'simul'
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horizon = options.periods;
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if horizon == 0
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horizon = 5;
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end
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if isempty(M.endo_histval)
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y0 = repmat(oo.dr.ys,1,maximum_lag);
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else
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y0 = M.endo_histval;
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end
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case 'smoother'
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horizon = options.forecast;
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y_smoothed = oo.SmoothedVariables;
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y0 = zeros(M.endo_nbr,maximum_lag);
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for i = 1:M.endo_nbr
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v_name = deblank(M.endo_names(i,:));
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y0(i,:) = y_smoothed.(v_name)(end-maximum_lag+1:end); %includes steady state or mean, but simult_ will subtract only steady state
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% 2. Subtract mean/steady state and add steady state; takes care of prefiltering
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if isfield(oo.Smoother,'Constant') && isfield(oo.Smoother.Constant,v_name)
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y0(i,:)=y0(i,:)-oo.Smoother.Constant.(v_name)(end-maximum_lag+1:end); %subtract mean or steady state
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if options.loglinear
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y0(i,:)=y0(i,:)+log_variable(i,oo.dr.ys,M);
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else
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y0(i,:)=y0(i,:)+oo.dr.ys(strmatch(v_name,deblank(M.endo_names),'exact'));
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end
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end
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% 2. Subtract trend
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if isfield(oo.Smoother,'Trend') && isfield(oo.Smoother.Trend,v_name)
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y0(i,:)=y0(i,:)-oo.Smoother.Trend.(v_name)(end-maximum_lag+1:end); %subtract trend, which is not subtracted by simult_
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end
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end
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gend = options.nobs;
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if isfield(oo.Smoother,'TrendCoeffs')
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var_obs = options.varobs;
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endo_names = M.endo_names;
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order_var = oo.dr.order_var;
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i_var_obs = [];
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trend_coeffs = [];
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for i=1:length(var_obs)
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tmp = strmatch(var_obs{i},endo_names(i_var,:),'exact');
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trend_var_index=strmatch(var_obs{i},M.endo_names,'exact');
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if ~isempty(tmp)
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i_var_obs = [ i_var_obs; tmp];
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trend_coeffs = [trend_coeffs; oo.Smoother.TrendCoeffs(trend_var_index)];
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end
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end
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if ~isempty(trend_coeffs)
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trend = trend_coeffs*(options.first_obs+gend-1+(1-M.maximum_lag:horizon));
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if options.prefilter
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trend = trend - repmat(mean(trend_coeffs*[options.first_obs:options.first_obs+gend-1],2),1,horizon+1); %subtract mean trend
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end
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end
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else
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trend_coeffs=zeros(length(options.varobs),1);
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end
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otherwise
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error('Wrong flag value')
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end
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if M.exo_det_nbr == 0
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if isequal(M.H,0)
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[yf,int_width] = forcst(oo.dr,y0,horizon,var_list,M,oo,options);
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else
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[yf,int_width,int_width_ME] = forcst(oo.dr,y0,horizon,var_list,M,oo,options);
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end
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else
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exo_det_length = size(oo.exo_det_simul,1)-M.maximum_lag;
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if horizon > exo_det_length
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ex = zeros(horizon,M.exo_nbr);
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oo.exo_det_simul = [ oo.exo_det_simul;...
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repmat(oo.exo_det_steady_state',...
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horizon- ...
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exo_det_length,1)];
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elseif horizon < exo_det_length
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ex = zeros(exo_det_length,M.exo_nbr);
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end
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if isequal(M.H,0)
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[yf,int_width] = simultxdet(y0,ex,oo.exo_det_simul,...
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options.order,var_list,M,oo,options);
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else
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[yf,int_width,int_width_ME] = simultxdet(y0,ex,oo.exo_det_simul,...
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options.order,var_list,M,oo,options);
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end
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end
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if ~isscalar(trend) %add trend back to forecast
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yf(i_var_obs,:) = yf(i_var_obs,:) + trend;
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end
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if options.loglinear == 1
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if options.prefilter == 1 %subtract steady state and add mean for observables
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yf(i_var_obs,:)=yf(i_var_obs,:)-repmat(log(oo.dr.ys(i_var_obs)),1,horizon+M.maximum_lag)+ repmat(mean_varobs,1,horizon+M.maximum_lag);
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end
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else
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if options.prefilter == 1 %subtract steady state and add mean for observables
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yf(i_var_obs,:)=yf(i_var_obs,:)-repmat(oo.dr.ys(i_var_obs),1,horizon+M.maximum_lag)+ repmat(mean_varobs,1,horizon+M.maximum_lag);
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end
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end
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for i=1:n_var
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vname = deblank(var_list(i,:));
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forecast.Mean.(vname) = yf(i,maximum_lag+(1:horizon))';
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forecast.HPDinf.(vname)= yf(i,maximum_lag+(1:horizon))' - int_width(1:horizon,i);
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forecast.HPDsup.(vname) = yf(i,maximum_lag+(1:horizon))' + int_width(1:horizon,i);
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if ~isequal(M.H,0) && ismember(var_list(i,:),options.varobs)
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forecast.HPDinf_ME.(vname)= yf(i,maximum_lag+(1:horizon))' - int_width_ME(1:horizon,i);
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forecast.HPDsup_ME.(vname) = yf(i,maximum_lag+(1:horizon))' + int_width_ME(1:horizon,i);
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end
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end
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for i=1:M.exo_det_nbr
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forecast.Exogenous.(deblank(M.exo_det_names(i,:))) = oo.exo_det_simul(maximum_lag+(1:horizon),i);
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end
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if options.nograph == 0
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oo.forecast = forecast;
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forecast_graphs(var_list,M, oo,options)
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end
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