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mom.get_data_moments.m: do not pass full oo_ structure

kalman-mex
Johannes Pfeifer 2023-09-25 22:31:45 +02:00
parent acb9518dec
commit fa29689b13
3 changed files with 8 additions and 7 deletions
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11
matlab/+mom/get_data_moments.m
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@ -1,10 +1,11 @@
function [dataMoments, m_data] = get_data_moments(data, oo_, matched_moments_, options_mom_)
% [dataMoments, m_data] = get_data_moments(data, oo_, matched_moments_, options_mom_)
function [dataMoments, m_data] = get_data_moments(data, obs_var, inv_order_var, matched_moments_, options_mom_)
% [dataMoments, m_data] = get_data_moments(data, obs_var, inv_order_var, matched_moments_, options_mom_)
% This function computes the user-selected empirical moments from data
% =========================================================================
% INPUTS
% o data [T x varobs_nbr] data set
% o oo_: [structure] storage for results
% o obs_var: [integer] index of observables
% o inv_order_var: [integer] inverse decision rule order
% o matched_moments_: [structure] information about selected moments to match in estimation
% o options_mom_: [structure] information about all settings (specified by the user, preprocessor, and taken from global options_)
% -------------------------------------------------------------------------
@ -45,11 +46,11 @@ m_data = NaN(T,options_mom_.mom.mom_nbr);
% Product moment for each time period, i.e. each row t contains y_t1(l1)^p1*y_t2(l2)^p2*...
% note that here we already are able to treat leads and lags and any power product moments
for jm = 1:options_mom_.mom.mom_nbr
vars = oo_.dr.inv_order_var(matched_moments_{jm,1})';
vars = inv_order_var(matched_moments_{jm,1})';
leadlags = matched_moments_{jm,2}; % lags are negative numbers and leads are positive numbers
powers = matched_moments_{jm,3};
for jv = 1:length(vars)
jvar = (oo_.mom.obs_var == vars(jv));
jvar = (obs_var == vars(jv));
y = NaN(T,1); %Take care of T_eff instead of T for lags and NaN via mean with 'omitnan' option below
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);
if jv==1

2
matlab/+mom/objective_function.m
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@ -247,7 +247,7 @@ if strcmp(options_mom_.mom.mom_method,'SMM')
if options_mom_.prefilter
y_sim = bsxfun(@minus, y_sim, mean(y_sim,1));
end
oo_.mom.model_moments = mom.get_data_moments(y_sim, oo_, M_.matched_moments, options_mom_);
oo_.mom.model_moments = mom.get_data_moments(y_sim, oo_.mom.obs_var, oo_.dr.inv_order_var, M_.matched_moments, options_mom_);
end

2
matlab/+mom/run.m
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@ -435,7 +435,7 @@ if strcmp(options_mom_.mom.mom_method,'GMM') || strcmp(options_mom_.mom.mom_meth
% Provide info on data moments handling
fprintf('Computing data moments. Note that NaN values in the moments (due to leads and lags or missing data) are replaced by the mean of the corresponding moment.\n');
% Get data moments for the method of moments
[oo_.mom.data_moments, oo_.mom.m_data] = mom.get_data_moments(dataset_.data, oo_, M_.matched_moments, options_mom_);
[oo_.mom.data_moments, oo_.mom.m_data] = mom.get_data_moments(dataset_.data, oo_.mom.obs_var, oo_.dr.inv_order_var, M_.matched_moments, options_mom_);
if ~isreal(dataset_.data)
error('method_of_moments: The data moments contain complex values!')
end