255 lines
7.5 KiB
Matlab
255 lines
7.5 KiB
Matlab
function [data,rawdata]=dynare_estimation_init(var_list_, igsa)
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% function dynare_estimation_init(var_list_)
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% preforms initialization tasks before estimation or
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% global sensitivity analysis
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%
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% INPUTS
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% var_list_: selected endogenous variables vector
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%
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% OUTPUTS
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% data: data after required transformation
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% rawdat: data as in the data file
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%
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% SPECIAL REQUIREMENTS
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% none
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% Copyright (C) 2003-2007 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 M_ options_ oo_ estim_params_
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global bayestopt_
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if nargin<2 | isempty(igsa),
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igsa=0;
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end
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options_.varlist = var_list_;
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options_.lgyidx2varobs = zeros(size(M_.endo_names,1),1);
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for i = 1:size(M_.endo_names,1)
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tmp = strmatch(deblank(M_.endo_names(i,:)),options_.varobs,'exact');
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if ~isempty(tmp)
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options_.lgyidx2varobs(i,1) = tmp;
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end
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end
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if ~isempty(strmatch('dsge_prior_weight',M_.param_names))
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options_.bvar_dsge = 1;
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end
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if options_.order > 1
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options_.order = 1;
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end
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if options_.prefilter == 1
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options_.noconstant = 1;
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end
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if options_.filtered_vars ~= 0 & options_.filter_step_ahead == 0
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options_.filter_step_ahead = 1;
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end
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if options_.filter_step_ahead ~= 0
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options_.nk = max(options_.filter_step_ahead);
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else
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options_.nk = 0;
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end
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%% Add something to the parser ++>
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% The user should be able to choose another name
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% for the directory...
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M_.dname = M_.fname;
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pnames = [' ';'beta ';'gamm ';'norm ';'invg ';'unif ';'invg2'];
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n_varobs = size(options_.varobs,1);
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if ~isempty(estim_params_)
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[xparam1,estim_params_,bayestopt_,lb,ub, M_] = set_prior(estim_params_, M_, options_);
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if any(bayestopt_.pshape > 0)
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if options_.mode_compute
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plot_priors
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end
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else
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options_.mh_replic = 0;
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end
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% set prior bounds and check initial value of the parameters
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bounds = prior_bounds(bayestopt_);
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bounds(:,1)=max(bounds(:,1),lb);
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bounds(:,2)=min(bounds(:,2),ub);
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if any(xparam1 < bounds(:,1)) | any(xparam1 > bounds(:,2))
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find(xparam1 < bounds(:,1))
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find(xparam1 > bounds(:,2))
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error('Initial parameter values are outside parameter bounds')
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end
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lb = bounds(:,1);
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ub = bounds(:,2);
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bayestopt_.lb = lb;
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bayestopt_.ub = ub;
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else
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xparam1 = [];
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bayestopt_.lb = [];
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bayestopt_.ub = [];
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bayestopt_.jscale = [];
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bayestopt_.pshape = [];
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bayestopt_.p1 = [];
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bayestopt_.p2 = [];
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bayestopt_.p3 = [];
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bayestopt_.p4 = [];
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estim_params_.nvx = 0;
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estim_params_.nvn = 0;
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estim_params_.ncx = 0;
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estim_params_.ncn = 0;
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estim_params_.np = 0;
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end
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nvx = estim_params_.nvx;
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nvn = estim_params_.nvn;
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ncx = estim_params_.ncx;
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ncn = estim_params_.ncn;
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np = estim_params_.np ;
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nx = nvx+nvn+ncx+ncn+np;
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if ~isfield(options_,'trend_coeffs')
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bayestopt_.with_trend = 0;
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else
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bayestopt_.with_trend = 1;
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bayestopt_.trend_coeff = {};
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trend_coeffs = options_.trend_coeffs;
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nt = length(trend_coeffs);
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for i=1:n_varobs
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if i > length(trend_coeffs)
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bayestopt_.trend_coeff{i} = '0';
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else
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bayestopt_.trend_coeff{i} = trend_coeffs{i};
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end
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end
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end
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bayestopt_.penalty = 1e8; % penalty
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dr = set_state_space([],M_);
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nstatic = dr.nstatic;
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npred = dr.npred;
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nspred = dr.nspred;
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if isempty(options_.varobs)
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error('ESTIMATION: VAROBS is missing')
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end
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%% Setting resticted state space (observed + predetermined variables)
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k = [];
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k1 = [];
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for i=1:n_varobs
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k = [k strmatch(deblank(options_.varobs(i,:)),M_.endo_names(dr.order_var,:),'exact')];
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k1 = [k1 strmatch(deblank(options_.varobs(i,:)),M_.endo_names, 'exact')];
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end
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% union of observed and state variables
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k2 = union(k',[dr.nstatic+1:dr.nstatic+dr.npred]');
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% set restrict_state to postion of observed + state variables
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% in expanded state vector
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bayestopt_.restrict_var_list = k2;
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% set mf0 to positions of state variables in restricted state vector
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% for likelihood computation.
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[junk,bayestopt_.mf0] = ismember([dr.nstatic+1:dr.nstatic+dr.npred]',k2);
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% set mf1 to positions of observed variables in restricted state vector
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% for likelihood computation.
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[junk,bayestopt_.mf1] = ismember(k,k2);
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% set mf2 to positions of observed variables in expanded state vector
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% for filtering and smoothing
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bayestopt_.mf2 = k;
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bayestopt_.mfys = k1;
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[junk,ic] = intersect(k2,nstatic+(1:npred)');
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bayestopt_.restrict_columns = [ic; length(k2)+(1:nspred-npred)'];
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aux = dr.transition_auxiliary_variables;
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aux(:,2) = aux(:,2) + sum(k2 <= nstatic);
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k = find(aux(:,2) > npred);
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aux(k,2) = aux(k,2) + sum(k2 > nstatic+npred);
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bayestopt_.restrict_aux = aux;
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%% Initialization with unit-root variables
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if ~isempty(options_.unit_root_vars)
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n_ur = size(options_.unit_root_vars,1);
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i_ur = zeros(n_ur,1);
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for i=1:n_ur
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i1 = strmatch(deblank(options_.unit_root_vars(i,:)),M_.endo_names(dr.order_var,:),'exact');
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if isempty(i1)
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error('Undeclared variable in unit_root_vars statement')
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end
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i_ur(i) = i1;
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end
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bayestopt_.var_list_stationary = setdiff((1:M_.endo_nbr)',i_ur);
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[junk,bayestopt_.restrict_var_list_nonstationary] = ...
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intersect(bayestopt_.restrict_var_list,i_ur);
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bayestopt_.restrict_var_list_stationary = ...
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setdiff((1:length(bayestopt_.restrict_var_list))', ...
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bayestopt_.restrict_var_list_nonstationary);
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if M_.maximum_lag > 1
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l1 = flipud([cumsum(M_.lead_lag_incidence(1:M_.maximum_lag-1,dr.order_var),1);ones(1,M_.endo_nbr)]);
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l2 = l1(:,bayestopt_.restrict_var_list);
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il2 = find(l2' > 0);
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l2(il2) = (1:length(il2))';
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bayestopt_.restrict_var_list_stationary = ...
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nonzeros(l2(:,bayestopt_.restrict_var_list_stationary));
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bayestopt_.restrict_var_list_nonstationary = ...
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nonzeros(l2(:,bayestopt_.restrict_var_list_nonstationary));
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end
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options_.lik_init = 3;
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end % if ~isempty(options_.unit_root_vars)
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if isempty(options_.datafile),
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if igsa,
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data=[];
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rawdata=[];
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return,
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else
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error('ESTIMATION: datafile option is missing'),
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end
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end
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%% If jscale isn't specified for an estimated parameter, use
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%% global option options_.jscale, set to 0.2, by default
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k = find(isnan(bayestopt_.jscale));
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bayestopt_.jscale(k) = options_.mh_jscale;
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%% Read and demean data
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rawdata = read_variables(options_.datafile,options_.varobs,[],options_.xls_sheet,options_.xls_range);
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options_ = set_default_option(options_,'nobs',size(rawdata,1)-options_.first_obs+1);
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gend = options_.nobs;
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rawdata = rawdata(options_.first_obs:options_.first_obs+gend-1,:);
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if options_.loglinear == 1 & ~options_.logdata
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rawdata = log(rawdata);
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end
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if options_.prefilter == 1
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bayestopt_.mean_varobs = mean(rawdata,1)';
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data = transpose(rawdata-repmat(bayestopt_.mean_varobs',gend,1));
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else
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data = transpose(rawdata);
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end
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if ~isreal(rawdata)
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error(['There are complex values in the data. Probably a wrong' ...
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' transformation'])
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end
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