237 lines
10 KiB
Matlab
237 lines
10 KiB
Matlab
function imcforecast(constrained_paths, constrained_vars, options_cond_fcst)
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% Computes conditional forecasts.
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%
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% INPUTS
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% o constrained_paths [double] m*p array, where m is the number of constrained endogenous variables and p is the number of constrained periods.
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% o constrained_vars [char] m*x array holding the names of the controlled endogenous variables.
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% o options_cond_fcst [structure] containing the options. The fields are:
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% + replic [integer] scalar, number of monte carlo simulations.
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% + parameter_set [char] values of the estimated parameters:
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% "posterior_mode",
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% "posterior_mean",
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% "posterior_median",
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% "prior_mode" or
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% "prior mean".
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% [double] np*1 array, values of the estimated parameters.
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% + controlled_varexo [char] m*x array, list of controlled exogenous variables.
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% + conf_sig [double] scalar in [0,1], probability mass covered by the confidence bands.
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%
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% OUTPUTS
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% None.
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%
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% SPECIAL REQUIREMENTS
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% This routine has to be called after an estimation statement or an estimated_params block.
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%
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% REMARKS
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% [1] Results are stored in a structure which is saved in a mat file called conditional_forecasts.mat.
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% [2] Use the function plot_icforecast to plot the results.
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% Copyright (C) 2006-2014 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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global options_ oo_ M_ bayestopt_
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if ~isfield(options_cond_fcst,'parameter_set') || isempty(options_cond_fcst.parameter_set)
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options_cond_fcst.parameter_set = 'posterior_mode';
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end
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if ~isfield(options_cond_fcst,'replic') || isempty(options_cond_fcst.replic)
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options_cond_fcst.replic = 5000;
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end
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if ~isfield(options_cond_fcst,'periods') || isempty(options_cond_fcst.periods)
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options_cond_fcst.periods = 40;
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end
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if ~isfield(options_cond_fcst,'conf_sig') || isempty(options_cond_fcst.conf_sig)
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options_cond_fcst.conf_sig = .8;
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end
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if isequal(options_cond_fcst.parameter_set,'calibration')
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estimated_model = 0;
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else
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estimated_model = 1;
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end
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if estimated_model
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if ischar(options_cond_fcst.parameter_set)
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switch options_cond_fcst.parameter_set
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case 'posterior_mode'
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xparam = get_posterior_parameters('mode');
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graph_title='Posterior Mode';
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case 'posterior_mean'
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xparam = get_posterior_parameters('mean');
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graph_title='Posterior Mean';
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case 'posterior_median'
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xparam = get_posterior_parameters('median');
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graph_title='Posterior Median';
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case 'prior_mode'
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xparam = bayestopt_.p5(:);
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graph_title='Prior Mode';
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case 'prior_mean'
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xparam = bayestopt_.p1;
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graph_title='Prior Mean';
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otherwise
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disp('imcforecast:: If the input argument is a string, then it has to be equal to:')
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disp(' ''calibration'', ')
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disp(' ''posterior_mode'', ')
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disp(' ''posterior_mean'', ')
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disp(' ''posterior_median'', ')
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disp(' ''prior_mode'' or')
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disp(' ''prior_mean''.')
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error('imcforecast:: Wrong argument type!')
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end
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else
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xparam = options_cond_fcst.parameter_set;
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if length(xparam)~=length(M_.params)
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error('imcforecast:: The dimension of the vector of parameters doesn''t match the number of estimated parameters!')
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end
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end
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set_parameters(xparam);
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[dataset_,dataset_info] = makedataset(options_);
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data = transpose(dataset_.data);
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data_index = dataset_info.missing.aindex;
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gend = dataset_.nobs;
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missing_value = dataset_info.missing.state;
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[atT,innov,measurement_error,filtered_state_vector,ys,trend_coeff] = DsgeSmoother(xparam,gend,data,data_index,missing_value);
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trend = repmat(ys,1,options_cond_fcst.periods+1);
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for i=1:M_.endo_nbr
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j = strmatch(deblank(M_.endo_names(i,:)),options_.varobs,'exact');
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if ~isempty(j)
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trend(i,:) = trend(i,:)+trend_coeff(j)*(gend+(0:options_cond_fcst.periods));
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end
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end
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trend = trend(oo_.dr.order_var,:);
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InitState(:,1) = atT(:,end);
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else
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graph_title='Calibration';
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end
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if isempty(options_.qz_criterium)
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options_.qz_criterium = 1+1e-6;
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end
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[T,R,ys,info,M_,options_,oo_] = dynare_resolve(M_,options_,oo_);
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if ~isdiagonal(M_.Sigma_e)
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warning(sprintf('The innovations are correlated (the covariance matrix has non zero off diagonal elements), the results of the conditional forecasts will\ndepend on the ordering of the innovations (as declared after varexo) because a Cholesky decomposition is used to factorize the covariance matrix.\n\n=> It is preferable to declare the correlations in the model block (explicitly imposing the identification restrictions), unless you are satisfied\nwith the implicit identification restrictions implied by the Cholesky decomposition.'))
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end
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sQ = chol(M_.Sigma_e,'lower');
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if ~estimated_model
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InitState(:,1) = oo_.steady_state(oo_.dr.order_var)-ys(oo_.dr.order_var,:); %initial state in deviations from steady state
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trend = repmat(ys(oo_.dr.order_var,:),1,options_cond_fcst.periods+1); %trend needs to contain correct steady state
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end
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sQ = sqrt(M_.Sigma_e);
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NumberOfStates = length(InitState);
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FORCS1 = zeros(NumberOfStates,options_cond_fcst.periods+1,options_cond_fcst.replic);
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FORCS1(:,1,:) = repmat(InitState,1,options_cond_fcst.replic); %set initial steady state to deviations from steady state in first period
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EndoSize = M_.endo_nbr;
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ExoSize = M_.exo_nbr;
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n1 = size(constrained_vars,1);
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n2 = size(options_cond_fcst.controlled_varexo,1);
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if n1 ~= n2
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error(['imcforecast:: The number of constrained variables doesn''t match the number of controlled shocks'])
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end
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idx = [];
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jdx = [];
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for i = 1:n1
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idx = [idx ; oo_.dr.inv_order_var(constrained_vars(i,:))];
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jdx = [jdx ; strmatch(deblank(options_cond_fcst.controlled_varexo(i,:)),M_.exo_names,'exact')];
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end
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mv = zeros(n1,NumberOfStates);
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mu = zeros(ExoSize,n2);
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for i=1:n1
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mv(i,idx(i)) = 1;
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mu(jdx(i),i) = 1;
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end
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% number of periods with constrained values
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cL = size(constrained_paths,2);
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constrained_paths = bsxfun(@minus,constrained_paths,trend(idx,1:cL));
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FORCS1_shocks = zeros(n1,cL,options_cond_fcst.replic);
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%randn('state',0);
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for b=1:options_cond_fcst.replic %conditional forecast using cL set to constrained values
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shocks = sQ*randn(ExoSize,options_cond_fcst.periods);
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shocks(jdx,:) = zeros(length(jdx),options_cond_fcst.periods);
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[FORCS1(:,:,b), FORCS1_shocks(:,:,b)] = mcforecast3(cL,options_cond_fcst.periods,constrained_paths,shocks,FORCS1(:,:,b),T,R,mv, mu);
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FORCS1(:,:,b)=FORCS1(:,:,b)+trend; %add trend
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end
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mFORCS1 = mean(FORCS1,3);
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mFORCS1_shocks = mean(FORCS1_shocks,3);
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tt = (1-options_cond_fcst.conf_sig)/2;
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t1 = round(options_cond_fcst.replic*tt);
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t2 = round(options_cond_fcst.replic*(1-tt));
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forecasts.controlled_variables = constrained_vars;
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forecasts.instruments = options_cond_fcst.controlled_varexo;
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for i = 1:EndoSize
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eval(['forecasts.cond.Mean.' deblank(M_.endo_names(oo_.dr.order_var(i),:)) ' = mFORCS1(i,:)'';']);
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tmp = sort(squeeze(FORCS1(i,:,:))');
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eval(['forecasts.cond.ci.' deblank(M_.endo_names(oo_.dr.order_var(i),:)) ...
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' = [tmp(t1,:)'' ,tmp(t2,:)'' ]'';']);
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end
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for i = 1:n1
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eval(['forecasts.controlled_exo_variables.Mean.' deblank(options_cond_fcst.controlled_varexo(i,:)) ' = mFORCS1_shocks(i,:)'';']);
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tmp = sort(squeeze(FORCS1_shocks(i,:,:))');
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eval(['forecasts.controlled_exo_variables.ci.' deblank(options_cond_fcst.controlled_varexo(i,:)) ...
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' = [tmp(t1,:)'' ,tmp(t2,:)'' ]'';']);
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end
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clear FORCS1 mFORCS1_shocks;
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FORCS2 = zeros(NumberOfStates,options_cond_fcst.periods+1,options_cond_fcst.replic);
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FORCS2(:,1,:) = repmat(InitState,1,options_cond_fcst.replic); %set initial steady state to deviations from steady state in first period
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%randn('state',0);
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for b=1:options_cond_fcst.replic %conditional forecast using cL set to 0
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shocks = sQ*randn(ExoSize,options_cond_fcst.periods);
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shocks(jdx,:) = zeros(length(jdx),options_cond_fcst.periods);
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FORCS2(:,:,b) = mcforecast3(0,options_cond_fcst.periods,constrained_paths,shocks,FORCS2(:,:,b),T,R,mv, mu)+trend;
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end
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mFORCS2 = mean(FORCS2,3);
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for i = 1:EndoSize
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eval(['forecasts.uncond.Mean.' deblank(M_.endo_names(oo_.dr.order_var(i),:)) ' = mFORCS2(i,:)'';']);
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tmp = sort(squeeze(FORCS2(i,:,:))');
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eval(['forecasts.uncond.ci.' deblank(M_.endo_names(oo_.dr.order_var(i),:)) ...
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' = [tmp(t1,:)'' ,tmp(t2,:)'' ]'';']);
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end
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forecasts.graph.title=graph_title;
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forecasts.graph.fname=M_.fname;
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save('conditional_forecasts.mat','forecasts'); |