72 lines
3.4 KiB
Matlab
72 lines
3.4 KiB
Matlab
function [dataMoments, m_data] = data_moments(data, oo_, matched_moments_, options_mom_)
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% [dataMoments, m_data] = data_moments(data, oo_, matched_moments_, options_mom_)
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% This function computes the user-selected empirical moments from data
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% =========================================================================
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% INPUTS
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% o data [T x varobs_nbr] data set
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% o oo_: [structure] storage for results
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% o matched_moments_: [structure] information about selected moments to match in estimation
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% o options_mom_: [structure] information about all settings (specified by the user, preprocessor, and taken from global options_)
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% -------------------------------------------------------------------------
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% OUTPUTS
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% o dataMoments [numMom x 1] mean of selected empirical moments
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% o m_data [T x numMom] selected empirical moments at each point in time
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% -------------------------------------------------------------------------
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% This function is called by
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% o mom.run.m
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% o mom.objective_function.m
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% =========================================================================
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% Copyright © 2020-2021 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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% -------------------------------------------------------------------------
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% Author(s):
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% o Willi Mutschler (willi@mutschler.eu)
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% o Johannes Pfeifer (jpfeifer@uni-koeln.de)
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% =========================================================================
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% Initialization
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T = size(data,1); % Number of observations (T)
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dataMoments = NaN(options_mom_.mom.mom_nbr,1);
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m_data = NaN(T,options_mom_.mom.mom_nbr);
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% Product moment for each time period, i.e. each row t contains y_t1(l1)^p1*y_t2(l2)^p2*...
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% note that here we already are able to treat leads and lags and any power product moments
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for jm = 1:options_mom_.mom.mom_nbr
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vars = oo_.dr.inv_order_var(matched_moments_{jm,1})';
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leadlags = matched_moments_{jm,2}; % lags are negative numbers and leads are positive numbers
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powers = matched_moments_{jm,3};
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for jv = 1:length(vars)
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jvar = (oo_.dr.obs_var == vars(jv));
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y = NaN(T,1); %Take care of T_eff instead of T for lags and NaN via mean with 'omitnan' option below
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y( (1-min(leadlags(jv),0)) : (T-max(leadlags(jv),0)), 1) = data( (1+max(leadlags(jv),0)) : (T+min(leadlags(jv),0)), jvar).^powers(jv);
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if jv==1
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m_data_tmp = y;
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else
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m_data_tmp = m_data_tmp.*y;
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end
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end
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% We replace NaN (due to leads and lags and missing values) with the corresponding mean
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if isoctave || matlab_ver_less_than('8.5')
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dataMoments(jm,1) = nanmean(m_data_tmp);
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else
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dataMoments(jm,1) = mean(m_data_tmp,'omitnan');
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end
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m_data_tmp(isnan(m_data_tmp)) = dataMoments(jm,1);
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m_data(:,jm) = m_data_tmp;
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end
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end %function end |